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Rohit Sharma

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M.Tech., IIT Delhi

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Rohit Sharma is the Program Director for the UpGrad-IIIT Bangalore, PG Diploma Data Analytics Program.

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Binary Tree in Data Structure: Properties, Types, Representation & Benefits
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Binary Tree in Data Structure: Properties, Types, Representation & Benefits

Data structures serve as the backbone of efficient data organization and management within computer systems. They play a pivotal role in computer algorithms and software development, contributing significantly to designing streamlined and effective programs. Data structures are indispensable across various domains in computer science, ranging from Artificial Intelligence to Operating Systems.  One of the most widely used non-linear data structures is trees. Trees portray a hierarchical arrangement, rendering ordering information irrelevant, unlike their linear counterparts, such as arrays, stacks, queues, and linked lists.  Comprising nodes and pointers, a tree’s structure involves parent nodes with left and right children, presenting a versatile and fundamental concept in data organization. This brings us to the topic we will discuss extensively in this blog – binary tree representation in data structure. Amongst different types of data structures are binary trees that come with more uses than most of the other types. Their most notable applications include peer-to-peer programming, search, cryptography, network routers with higher bandwidth than others, and 3D video games. We will now discuss in detail what binary trees in data science are, what are their types, and how are they represented.  What are binary trees? If you have worked on normal trees before or even know about their basics, you would know that there are no restrictions when it comes to the number of children that different nodes are allowed to have in these trees. Binary trees are a little different in this sense. Every parent or node in binary trees can have a maximum of only two children.  In binary tree representation, this limitation, however, simplifies the structure, making binary trees highly efficient in scenarios where quick data retrieval is essential. The binary nature allows for straightforward decision-making processes commonly used in searching algorithms. Despite the restriction on the number of children, the versatility and speed of binary trees make them crucial elements in various computer science applications. All nodes in a binary tree have three primary components –  a data element  This component has the actual information or value held by the node. It could represent various types of data, such as numbers, strings, or other relevant content, depending on the context of the tree. a right reference  The right reference is a pointer or reference that directs to the node’s right child. The right child is a node positioned to the right of the current node within the binary tree hierarchy. This reference helps traverse and explore the tree’s right branch. a left reference  Similar to the right reference, the left reference is a pointer or reference that points to the node’s left child. The left child is positioned to the left of the current node, forming the left branch of the binary tree. This reference is important for navigating and analyzing the tree’s left subtree. In combination, these three components create a cohesive structure, allowing for efficient organization, storage, and data retrieval within the binary tree representation. The data element holds the pertinent information, while the right and left references guide the relationships between nodes, forming the foundation for the tree’s hierarchical arrangement. The node that lies at the top of the tree is referred to as the root node. Parent nodes are those that have children. Children nodes and parent nodes are connected to each other through references. Nodes that don’t have any children are referred to as leaf nodes. It is clearly evident that nodes in binary trees can have one child, two children, or no children at all. Binary trees aren’t linear data structures like queues, arrays, stacks, and linked lists. They are hierarchical data structures instead.  Check out: Data Science Project Ideas for Beginners Important properties of nodes in binary trees A better understanding of these properties will help you in making the most of this discussion on binary trees. The depth of different nodes is defined as the number of nodes that exist on the way that connects the root to a particular node. That is why the depth of the root node is 0. On the other hand, the height of different nodes in a binary tree is the number of nodes that lie in the path that connects a particular node with the root node. That is why the height of leaf nodes is 0.  As you can clearly see, the depth of a node is measured by starting from the root node and then going down to reach that node. On the other hand, when it comes to calculating the height, we start at the node in question and then journey towards the root node. Both the times, we start at 0. There are people who also measure height and depth from1 and not from 0, which isn’t wrong and is just what different people prefer.  Now the maximum depth of a node is defined as the depth of a binary tree. Similarly, the maximum height of a node is defined as the height of a binary tree. So the height and depth of a binary tree are always the same.  Learn more: Data Structures & Algorithm in Python What is a binary search tree?  A binary search tree is the most common of all the other types of binary trees. It is a specialized binary tree that comes with properties that are different and more useful than any other form of a binary tree. What exactly is a binary search tree or BST? Just as its name suggests, a binary search tree is used to search data in the tree. A BST comes with properties that allow it to facilitate efficient searches. A BST is a binary tree that has the key of the node that is smaller and greater than nodes in the right sub-tree and nodes in the left sub-tree respectively.  Our learners also read: Free excel courses! upGrad’s Exclusive Data Science Webinar for you – Transformation & Opportunities in Analytics & Insights document.createElement('video'); https://cdn.upgrad.com/blog/jai-kapoor.mp4 Advantages of binary search tree There are several advantages of binary search tree that contribute to their widespread use in computer science and algorithm design: Efficient search operations: The binary search tree structure enables quick and efficient search operations. The binary nature of the tree ensures that at each node, the left subtree contains values smaller than the node, and the right subtree contains values greater. This property allows for a binary search algorithm, reducing the search space at each step. Simple insertion and deletion: Inserting and deleting elements in a binary search tree is quite straightforward. The binary structure allows for easy maintenance of the order, and the tree can be rebalanced if needed to maintain optimal search performance. Ordered data retrieval: In-order traversal of a binary search tree results in sorted data retrieval. This property is valuable in scenarios where data needs to be accessed or processed in sorted order without additional sorting operations. Space efficiency: Compared to other data structures like arrays, binary search trees are more space-efficient, especially when dealing with dynamic datasets. Nodes are allocated as needed, and memory is not pre-allocated, making it flexible for varying data sizes. Versatility in applications: Binary search trees find applications in various domains, including databases, symbol tables, and compilers. Their efficient search and ordered retrieval properties make them versatile where these operations are critical. Balanced binary search trees: When balanced, binary search trees ensure optimal performance in terms of search, insertion, and deletion operations. Balanced trees, such as AVL trees or Red-Black trees, maintain a balanced structure, preventing the tree from degenerating into a linked list and ensuring logarithmic time complexity for operations. Representation of binary trees Binary tree representation in data structure can be carried out in different ways, each with distinct pros and cons. The prominent methods are binary tree representation using linked list (Linked representation), storage representation of binary tree (Sequential representation), and linear representation. 1. Linked representation Binary trees in linked representation are stored in the memory as linked lists. These lists have nodes that aren’t stored at adjacent or neighboring memory locations and are linked to each other through the parent-child relationship associated with trees.  In this representation, each node has three different parts –  Pointer that points towards the right node This is the more common representation. All binary trees consist of a root pointer that points in the direction of the root node. When you see a root node pointing towards null or 0, you should know that you are dealing with an empty binary tree. The right and left pointers store the address of the right and left children of the tree.  Pointer that points towards the left node  The second part is another pointer pointing towards the left child node. Similar to the right pointer, this element establishes the connection between the current node and its left child, contributing to the hierarchical structure of the binary tree. Data element The third part is the actual data element or value associated with the node. Depending on the context of the binary tree’s application, this could be any type of data, such as a number, a string, or another relevant piece of information. This is the more common representation. All binary trees consist of a root pointer that points in the direction of the root node. When you see a root node pointing towards null or 0, you should know that you are dealing with an empty binary tree. The right and left pointers store the address of the right and left children of the tree.  The linked representation of binary tree in data structure offers dynamic memory allocation, making it adaptable to varying tree sizes. The linked representation of binary tree in memory offers flexibility and ease of manipulation, making it a popular choice in applications requiring dynamic data structures. Our learners also read: Free Python Course with Certification 2. Sequential representation Although it is simpler than linked representation, its inefficiency makes it a less preferred binary tree representation of the two. The inefficiency lies in the amount of space it requires for the storage of different tree elements. The sequential representation uses an array for the storage of tree elements. The number of nodes a binary tree has defines the size of the array being used. The root node of the binary tree lies at the array’s first index. The index at which a particular node is stored will define the indices at which the right and left children of the node will be stored. An empty tree has null or 0 as its first index.  Also read: Free data structures and algorithm course! Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses Despite its inefficiency, the sequential representation of binary tree in data structure is beneficial where memory allocation is a concern, as it uses a contiguous memory block. However, this memory representation of binary tree may become impractical for large or dynamic datasets due to its fixed array size. 3. Linear Representation of Binary Tree Amongst the various representations of binary trees the linear representation focuses on organizing its elements linearly or sequentially, often for ease of traversal or processing. Two common linear ways of representing binary tree in memory are array-based linearization and in-order linearization. Array-Based Linearization: In the linear representation of binary tree, the tree is linearized into an array by traversing it in a specific order, such as level order or inorder. The elements are placed in the array based on their order in the traversal, allowing for a linear structure that simplifies access and manipulation. This method is particularly useful when a sequential representation is required for efficient storage and retrieval. In-order Linearization: In in-order linearization, the binary tree is traversed in an in-order fashion, meaning left subtree, root, and right subtree. This linearization results in a sequence of elements that follows the sorted order of the binary search tree. In-order linearization is often employed when the goal is to process or display elements in a sorted manner. Types of binary trees Full binary trees: Full binary trees are those binary trees whose nodes either have two children or none. In other words, a binary tree becomes a full binary tree when apart from leaves, all its other nodes have two children.Understanding the nature of full binary trees is fundamental, as their balanced structure enhances the efficiency of various tree operations. Complete binary trees: Complete binary trees are those that have all their different levels completely filled. The only exception to this could be their last level, whose keys are predominantly on the left. A binary heap is often taken as an example of a complete binary tree.Complete binary trees, exemplified by binary heaps, showcase specific flexibility in their structure, optimizing memory use while facilitating efficient data retrieval. Perfect binary trees: Perfect binary trees are binary trees whose leaves are present at the same level and whose internal nodes carry two children. A common example of a perfect binary tree is an ancestral family tree.With their balanced and symmetrical layout, perfect binary trees provide an idealized model for understanding the structural efficiency of certain tree-based data structures. Pathological degenerate binary trees: Degenerate trees are those binary trees whose internal nodes have one child. Their performance levels are similar to linked lists. Learn more about the types of binary tree. Pathological degenerate binary trees serve as a reminder that not all tree structures are inherently advantageous, emphasizing the importance of selecting the appropriate type based on specific use cases. Read: The Six Most Commonly Used Data Structures in R Benefits of binary trees There are a myriad of advantages of binary tree that make them a valuable choice for storing and organizing data hierarchically.  Some of the major advantages of binary tree include: An ideal way to go with the hierarchical way of storing data.  The parent-child relationships among nodes create a natural hierarchy, facilitating efficient organization and retrieval of information. Reflect structural relationships that exist in the given data set.  The arrangement of nodes, with each node having at most two children, captures relationships and dependencies within the data, enhancing the representation’s meaningfulness. Make insertion and deletion faster than linked lists and arrays. The hierarchical structure allows for streamlined updates, making it easy to add or remove elements while maintaining the overall organization of the tree. A flexible way of holding and moving data. The dynamic nature of the structure, with nodes being added or removed as needed, enables adaptability to changing data requirements. This flexibility is particularly advantageous in dynamic or evolving datasets. Are used to store as many nodes as possible. The hierarchical organization ensures that the tree can accommodate a substantial volume of data while maintaining a balanced structure. Are faster than linked lists and slower than arrays when comes to accessing elements. In terms of access speed, binary trees strike a balance between linked lists and arrays. While they may not match the swift access times of arrays, they outperform linked lists in speed. This balanced performance makes binary trees the preferred choice where a compromise between insertion/deletion efficiency and access speed is crucial. Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? Conclusion In this blog, we have discussed what binary trees in data structures are as well as talked about their types, their representations, and their benefits. The two major uses of the trees are for searching and storing data, and hence they are integral to the study of Data Science and its related fields.   Understanding the different types of binary trees helps us handle various data organization challenges, while the ways we represent them offer insights into storing and retrieving information dynamically. Recognizing the inherent benefits of binary trees, we see them not just as computer science structures but as crucial elements supporting the foundations of Data Science. Top Data Science Skills to Learn Top Data Science Skills to Learn 1 Data Analysis Course Inferential Statistics Courses 2 Hypothesis Testing Programs Logistic Regression Courses 3 Linear Regression Courses Linear Algebra for Analysis If you are curious to learn about binary trees in data structures, data science, check out IIIT-B & upGrad’s Executive PG Programme in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.

by Rohit Sharma

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21 May 2024

Python Free Online Course with Certification [2024]
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Python Free Online Course with Certification [2024]

Summary: In this Article, you will learn about python free online course with certification. Programming with Python: Introduction for Beginners Learn Basic Python Programming Python Libraries Read more to know each in detail. Want to become a data scientist but don’t know Python? Don’t worry; we’ve got your back. With our free online Python course for beginners, you can learn Python online free and kickstart your data science journey. You don’t have to spend a dime to enroll in this program. The only investment you’d have to make is 30 minutes a day for a few weeks, and by the end, you’d know how to use Python for data science.  To enroll in our Python course free, head to our upGrad free course page, select the “Python course, and register. This article will discuss the basics of python and its industrial application, our course contents, and what its advantages are. Let’s get started.  Why Learn Python? Python is among the most popular programming languages on the planet. According to a survey from RedMonk, a prominent analyst firm, Python ranked 2nd in their ranking of programming languages by popularity. Python became the first language other than Java or and JavaScript to enter the top two spots. You can see how relevant Python is in the current market. It’s a general-purpose programming language, which means you can use it for many tasks. Apart from data science, Python has applications in web development, machine learning, etc.  Python is one of the most popular programming languages. Python is used for web development, game development, language development, etc. It helps in conducting complex statistical complications and performing data visualisation. It is compatible with various platforms and has an extensive library. Top Python libraries are Numpy, Pandas, Scipy, Keras, Tensorflow, SciKit learn, Matplotlib, Plotly, Seaborn, Scrapy, and Selenium. These libraries serve different purposes such as some of them are for data processing, data modelling, data visualisation, and data mining. You can also consider doing our Python Bootcamp course from upGrad to upskill your career. In data science, Python has many applications. It has multiple libraries that simplify various data operations. For example, Pandas is a Python library for data analysis and manipulation. It offers numerous functions to manipulate vast quantities of structured data. This way, it makes data analysis much more straightforward. Another primary Python library in data science is matplotlib, which helps you with data visualization. Python is one of the core skills of data science professionals. Learning it will undoubtedly help you in entering this field.  Read: Python Applications in Real World Python Installation and Setup Python installation is a simple procedure. Visit the Python website to get hold of the most recent version. Take care to add python to your system’s PATH during installation. You can look for a free python course with certificate online to gain practical experience. Many platforms provide thorough training to assist you in understanding the essentials. After installing python, create and run your code using an integrated development environment (IDE).  Don’t forget to look at python’s numerous libraries and frameworks, which can make development much simpler. As you advance through your python free course with certificate or python certification free put your newfound knowledge into practice by working on projects and practicing consistently. With perseverance, you’ll soon become an expert Python programmer, prepared to take on a variety of programming tasks. Check out all trending Python tutorial concepts in 2024 Basic Python Syntax and Data Types Any programming enthusiast must be familiar with the fundamental Python syntax and data structures. 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The python free online course with certificate will place a strong emphasis on applying these ideas through exercises and projects as you progress through your learning process. By the end of the course, you’ll have a firm understanding of python’s syntax and data types and be prepared to go on to more advanced programming approaches. Control Flow and Loops In order to succeed as a programmer, you must master python’s control flow and loops. A thorough python certification course free will go through these topics in great detail. Your program can make decisions depending on conditions with the help of control flow structures like if, else, and elif. Another important idea is the use of loops, which let your code carry out repeated actions. The python full course free with certificate will guide you through the two main forms of loops: for and while. You can iterate over sequences like lists or strings with the “for” loop. At the same time, a condition is true; a ‘while’ loop, on the other hand, keeps repeating. By completing real-world examples and exercises in your chosen python free certification course, you’ll earn practical experience. Your comprehension of control flow and loops will become more robust as a result. By the end of the course, you’ll be able to design complex programs that efficiently make use of these structures. A solid understanding of control flow and loops is crucial when automating processes or creating intricate algorithms, and the correct course will provide you with these important skills. Why Choose Python free course from upGrad? There are many advantages to joining our Python free courses. Here are some of them: Expert Instructors At upGrad, our Python free course with certificate is given by a team of seasoned instructors, ensuring the best online learning experience for participants. 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The curriculum is continuously updated to reflect the latest industry trends, making it the best free online Python course with certificate for staying current in the dynamic field of technology.  Our instructors excel not only in their subject matter expertise but also in their ability to convey complex concepts effectively. They leverage interactive teaching methods, making the learning process engaging and accessible. This approach caters to participants of all levels, from beginners to seasoned professionals looking to enhance their skills. Participants of the upGrad free Python course with certificate can rest assured that they are learning from the best in the industry. The comprehensive nature of the course, coupled with the expertise of our instructors, makes it the go-to choice for those seeking a python free certification course that provides both quality education and a valuable certificate. Hands-On Projects In our Python free certification course at upGrad, we recognize the pivotal role hands-on experience plays in mastering Python programming. That’s why our course goes beyond theoretical instruction, offering a diverse range of practical, real-world projects that empower learners to apply their knowledge Participants engage in online Python courses with certificates that feature hands-on projects designed to simulate industry challenges. These projects are carefully curated to align with the course curriculum, providing learners with a seamless transition from theory to practice. By actively working on these assignments, participants not only reinforce their understanding of Python concepts but also gain invaluable hands-on experience in solving authentic problems. The emphasis on hands-on projects is crucial to our approach to an online Python course free with certificate. These projects serve as a bridge between theoretical learning and practical application, ensuring that learners are well-prepared for real-world scenarios. The skills honed through these projects contribute significantly to building a strong portfolio, showcasing the practical expertise gained during the course. By completing these projects, participants not only earn their Python certification free but also graduate with a portfolio that reflects their ability to tackle complex challenges. This portfolio becomes a valuable asset for job seekers, demonstrating to potential employers their proficiency in Python programming through project-based accomplishments. Interactive Learning Platform  At upGrad, our Python programming online free course with certificate is not just about content delivery. It’s about creating an interactive learning environment through our user-friendly online platform. Designed to cater to learners of all levels, our platform promotes engagement and collaboration throughout the entire Python course.  Our platform features discussion forums where participants can engage in meaningful conversations, share insights, and seek assistance from instructors and peers. This collaborative space enhances the sense of community, creating a supportive network for learners beginning their Python journey. Quizzes and interactive assignments are seamlessly integrated into the platform, allowing participants to assess their understanding in real-time.  These assessments not only reinforce theoretical concepts learned during the Python free online course with certificate but also provide immediate feedback, helping in continuous improvement. The user-friendly interface ensures easy navigation, making the learning experience accessible to all. Learners can progress through the Python course online free with certificate at their own pace, accessing materials and resources effortlessly. Cutting Edge Content upGrad’s professionally created content ensures that you get the best online learning experience. The curriculum of the course is industry relevant and focuses on practical concepts. To be able to learn the concepts a curriculum which is strong is recommended. This is what upGrad recommends. And after finishing a course, there are practice questions that one can solve in order to gauge retention. This free online python course for beginners is focused on the basics of python programming, It is a good opportunity for someone who is new to the field as it would take the learners on the journey step by step. It is also ideal for those learners who have been in the field for a long, so those candidates can brush up on their skills and revisit the concepts. Community and Networking Joining the upGrad Python community is not just about gaining a free Python certificate or completing a full course. It’s about becoming part of a strong network of learners, industry professionals, and alumni. Our platform thrives on developing connections that extend beyond the confines of the course, providing a comprehensive learning experience. The strong community of learners offers a supportive environment where participants can engage in discussions, share insights, and seek advice. This collaborative atmosphere enhances the overall learning journey, making the Python course free with certificate an interactive and enriching experience. Our platform facilitates interaction with industry professionals and alumni, providing unique insights into real-world applications and potential career paths. These connections go beyond the duration of the online Python course, serving as a valuable resource for ongoing learning and collaboration. Alumni networks often prove instrumental in opening doors to job opportunities, mentorship, and industry insights, making the upGrad Python full course free with certificate not just a learning platform but a gateway to a thriving professional community. Skill Assessment and Feedback In the upGrad Python free course with certificate, continuous skill assessment and personalized feedback mechanisms are integral components, setting it apart as the best free online python course with certificate. Throughout the program, participants engage in assessments strategically placed to evaluate their understanding of Python programming concepts. These assessments, tailored to align with the course content, serve as checkpoints to measure individual progress. Regular quizzes and assignments in the online Python course free with certificate not only reinforce theoretical knowledge but also provide learners with immediate feedback. This constructive feedback is crucial in helping participants identify strengths and areas for improvement, contributing to a more targeted and personalized learning experience. The importance of ongoing skill assessment cannot be overstated. It allows learners to track their progress systematically, ensuring that they understand each concept before moving forward. The feedback loop provided by these assessments becomes a valuable tool for self-reflection and improvement, enhancing the overall effectiveness of the Python free certification course. By integrating skill assessments and feedback into the Python full course free with certificate, upGrad ensures that learners receive a comprehensive educational experience. Continuous Updates upGrad’s commitment to offering the best free Python course with certificate is evident through our dedication to staying current with industry trends. Recognizing the dynamic nature of technology, we ensure that our Python programming online free course with certificate is continuously updated to reflect the latest advancements in the field. The online free Python course content undergoes regular reviews and enhancements, aligning with the evolving landscape of Python programming. Our emphasis on providing a Python free online course with certificate that adapts to industry changes is crucial in preparing learners for the demands of real-world applications. Learners can trust that the upGrad Python course is not static but dynamic, mirroring the rapid developments in Python programming. By staying ahead of the curve, our program equips participants with the most relevant skills and knowledge, ensuring they graduate well-prepared for the challenges of the industry. Choosing a Python free course with certificate that prioritizes continuous updates is crucial for individuals seeking a program that evolves with the industry. This commitment reflects our dedication to offering a learning experience that remains at the forefront of technological advancements, making upGrad the ideal choice for those pursuing online Python courses with certificates that truly keep pace with the dynamic field of Python programming. Free Certificate After you complete our Python online course free, you’ll receive a certificate for completion. The certificate would enhance your CV substantially.  Apart from these benefits, the biggest one is that you can join the course for free. It doesn’t require any monetary investment. The free certificate is the validation of your knowledge. You could add the skill of knowing python to your CV and present the certificate in order to show authenticity. Also, the free certificate is shareable on LinkedIn. You could show your skill to potential recruiters. When you are appearing for any interview, or are looking to get promoted at your job these little things come to help where one can confidently show the document for the skillset that they have mentioned in the CV. It sets one apart from the rest of the candidates.  Access to Additional Resources In addition to our comprehensive Python programming online free course with certificate, upGrad provides participants with access to a wealth of supplementary resources, enriching the overall learning experience. Learners can benefit from webinars conducted by industry experts, offering insights into real-world applications and emerging trends in Python programming. Our dedication to providing a comprehensive educational experience is demonstrated by our workshops, which offer practical knowledge beyond the main curriculum. These courses provide participants with invaluable opportunities to improve their problem-solving abilities and gain a deeper understanding of particular Python ideas. Moreover, participants in the Python course online free with certificate gain access to additional reading materials curated to broaden their perspectives. By providing these supplementary resources, upGrad ensures that participants not only earn their free Python certificate but also gain a comprehensive understanding of the subject matter. Industry Recognition Earning a Python certification free from upGrad signifies industry recognition, validating your proficiency in Python programming. This Python free certification holds substantial weight in the job market, demonstrating your commitment to continuous learning and skill development. upGrad’s Python course free with certificate is meticulously designed to align with industry standards, ensuring that participants acquire practical, in-demand skills. As you complete online Python courses with certificates, you not only enhance your knowledge but also showcase your dedication to staying competitive in the ever-evolving tech landscape. This industry-recognized certification serves as a testament to your expertise, making you a sought-after candidate for Python-related roles and solidifying your position as a competent professional in the field. Additionally, the free Python course with certificate from upGrad adds significant value to your resume, opening doors to diverse opportunities. Let’s now discuss what the course is about and what it will teach you: Must read: Data structures and algorithms free course! Watch our Webinar on How to Build Digital & Data Mindset? Top Data Science Skills to Learn Top Data Science Skills to Learn 1 Data Analysis Course Inferential Statistics Courses 2 Hypothesis Testing Programs Logistic Regression Courses 3 Linear Regression Courses Linear Algebra for Analysis What Will You Learn? Learning Python is crucial for becoming a data scientist. It has many applications in this field, and without it, you can’t perform many vital operations related to data science. Because Python is a programming language, many students and professionals hesitate to study it. They read about Python’s various applications in data science, artificial intelligence, and machine learning and think it’s a highly complicated subject. However, Python is an elementary programming language that you can learn quickly.  Our free Python online course for beginners covers this prominent programming language’s basics and helps you understand its fundamental uses in data science. Below are the list of courses available in Python: Programming with Python: Introduction for Beginners Learn Basic Python Programming Python Libraries These sections allow you to learn Python in a stepwise manner. Let’s discuss each one of these sections in detail: Programming with Python: Python Free Online Course for Beginners In this course, you’ll get a stepwise python tutorial. It will familiarize you with Python’s fundamentals, what it is, and how you can learn this programming language. Apart from the basics, this section will explain the various jargons present in data science to you. You’ll get to know the meaning behind many technical terms data scientists usually use, including EDA, NLP, Deep Learning, Predictive Analytics, etc. Understanding what Python is will give you the foundation you need to study its more advanced concepts later on.  When you’d know the meaning behind data science jargon, you would understand how straightforward this subject is. It’s an excellent method to get rid of your hesitation in learning data science. By the end of this course, you would be able to use data science jargon casually like another data professional.  In the introduction, you will get to learn about the primary consoles, what are primary actions, what are statuses, and what important pointers. These topics will be covered in the introduction. The primary console is nothing but a media that takes the input front the user and then interprets it. In this opportunity to learn python online for free, you get to understand python programming from the basics. There is no compromise on imparting education. Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses Learn Basic Python Programming This section of our course will teach you Python’s basics from a coding perspective, including strings, lists, and data structures. Data structures are one of the essential concepts you can study in data science. The second topic would be concentrating on the basics of python that will be covering the introduction, history of python, how to do installation documentation, and what are arithmetic operations, and string operations. After the module would be over there would also be a focus on practice questions. These practice questions can be solved to understand how much understanding the learner has gotten. The learners upon answering will get the response to the questions on a real-time basis. Python online course free gives an opportunity to gain the skill of knowing python. They help in organizing data so you can access it and perform operations on it quickly. Understanding data structures is vital to becoming a proficient data scientist. Many recruiters ask the candidates about data structures and their applications in technical interviews. This module focuses on programming with Python in data science. So, it covers the basic concepts of many data structures, such as Tuples, sets, dictionaries etc.  The curriculum would also be focusing on dictionaries, and how to map, filter, and reduce functions. It also will focus on the OOPs, class and objects, methods, inheritance, and overriding. They are very important topics, for example, the OOPs is a computer programming model. It includes methods, classes, objects, etc. OOPs is useful for creating and developing real-life applications. Also visit upGrad’s Degree Counselling page for all undergraduate and postgraduate programs. When you’re familiar with the basics, you can easily use them later in more advanced applications. For example, lists are among the most versatile data structures. They allow the storage of heterogeneous items (items of different data types) such as strings, integers, and even other lists. Another prominent property that makes lists a preferred choice is they are mutable. This allows you to change their elements even after you create the list. This course will cover many other topics similar like this. Our learners also read: Excel online course free! Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? Learn Python Libraries: NumPy, Matplotlib and Pandas Python is popular among data scientists for many reasons. One of those reasons is its large number of libraries. There are more than 1,37,000 Python libraries. This number should give you an idea of how valuable these libraries are. These libraries simplify specific processes and make it easier for developers to perform related functions. In this course for beginners, you’ll learn about multiple Python libraries data scientists use, such as NumPy, matplotlib, and Pandas.  A Python library contains reusable code that helps you perform specific tasks with less effort. Unlike C or C++, its libraries don’t focus on a context. They are collections of modules. You can import a module from another program to use its functionality. Every Python library simplifies certain functions. For example, with NumPy, you can perform mathematical operations in Python smoothly. It has many high-level mathematical functions and support for multi-dimensional matrices and arrays. Understanding these libraries will help you in performing operations on data.   Pandas are used for better representation of the data, more work can be done with less coding in Pandas. It is a library of python for data analysis purposes. Pandas can be used for neuroscience, analytics, statistics, data science, advertising, etc.   Matplotlib is a library for Python. It is used for data visualisation and graphical plotting. The APIs (Application Programming Interfaces) of the matplotlib can also be used to plot in GUI applications.  Must Read: Python Project Ideas & Topics for Beginners How to Start To join our free online courses on python, follow the below mentioned steps: Head to our upGrad Free Courses Page Select the Python course Click on Register Complete the registration process That’s it. You can learn python for free with upGrad’s Free Courses and get started with your data science journey. You’d only have to invest 30 minutes a day for a few weeks. This program requires no monetary investment.  Sign up today and get started.  If you have any questions or suggestions regarding this topic, please let us know in the comments below. We’d love to hear from you.  If you are curious to learn about Python, data science, check out IIIT-B & upGrad’s Executive PG Programme in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.

by Rohit Sharma

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20 May 2024

13 Interesting Data Structure Projects Ideas and Topics For Beginners [2023]
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248379

13 Interesting Data Structure Projects Ideas and Topics For Beginners [2023]

 In the world of computer science, understanding data structures is essential, especially for beginners. These structures serve as the foundation for organizing and manipulating data effectively. To assist newcomers in grasping these concepts, I’ll provide you with data structure projects ideas for beginners. These projects are tailored to offer hands-on learning experiences, allowing beginners to explore various data structures while honing their programming skills. By working on these projects, beginners can gain practical insights into data organization and algorithmic thinking, laying a solid foundation for their journey into computer science. Let’s delve into some exciting data structure projects ideas designed specifically for beginners.. These projects are tailored to offer hands-on learning experiences, allowing beginners to explore various data structures while honing their programming skills. By working on these projects, beginners can gain practical insights into data organization and algorithmic thinking, laying a solid foundation for their journey into computer science. Let’s delve into some exciting data structure project ideas designed specifically for beginners.  You can also check out our free courses offered by upGrad under machine learning and IT technology. Data Structure Basics Data structures can be classified into the following basic types: Arrays Linked Lists Stacks Queues Trees Hash tables Graphs What is Data Structure? The topic of data structure revolves around the organization, management, and storage of data in a way that enables efficient access and modification. It includes various ways of structuring data such as arrays, linked lists, trees, graphs, stacks, queues, and hash tables, each with unique properties and specific use cases. Understanding data structures is crucial for developing efficient algorithms that can manage large volumes of data, perform complex data analysis, and optimize software applications for speed and performance. This foundational concept is essential in computer science, helping to solve problems related to data management and algorithm design effectively. Selecting the appropriate setting for your data is an integral part of the programming and problem-solving process. And you can observe that data structures organize abstract data types in concrete implementations. To attain that result, they make use of various algorithms, such as sorting, searching, etc. Learning data structures is one of the important parts in data science courses. With the rise of big data and analytics, learning about these fundamentals has become almost essential for data scientists. The training typically incorporates various topics in data structure to enable the synthesis of knowledge from real-life experiences. Here is a list of dsa topics to get you started! Check out our Python Bootcamp created for working professionals. Benefits of Data structures: Data structures are fundamental building blocks in computer science and programming. They are important tools that helps inorganizing, storing, and manipulating data efficiently. On top of that it provide a way to represent and manage information in a structured manner, which is essential for designing efficient algorithms and solving complex problems. So, let’s explore the numerous benefits of Data Structures and dsa topics list in the below post: – 1. Efficient Data Access Data structures enable efficient access to data elements. Arrays, for example, provide constant-time access to elements using an index. Linked lists allow for efficient traversal and modification of data elements. Efficient data access is crucial for improving the overall performance of algorithms and applications. 2. Memory Management Data structures help manage memory efficiently. They helps in allocating and deallocating memory resources as per requirement, reducing memory wastage and fragmentation. Remember, proper memory management is important for preventing memory leaks and optimizing resource utilization. 3. Organization of Data Data structures offers a structured way to organize and store data. For example, a stack organizes data in a last-in, first-out (LIFO) fashion, while a queue uses a first-in, first-out (FIFO) approach. These organizations make it easier to model and solve specific problems efficiently. 4. Search and Retrieval Efficient data search and retrieval are an important aspect in varied applications, like, databases and information retrieval systems. Data structures like binary search trees and hash tables enable fast lookup and retrieval of data, reducing the time complexity of search operations. 5. Sorting Sorting is a fundamental operation in computer science. Data structures like arrays and trees can implement various sorting algorithms. Efficient sorting is crucial for maintaining ordered data lists and searching for specific elements. 6. Dynamic Memory Allocation Many programming languages and applications require dynamic memory allocation. Data structures like dynamic arrays and linked lists can grow or shrink dynamically, allowing for efficient memory management in response to changing data requirements. 7. Data Aggregation Data structures can aggregate data elements into larger, more complex structures. For example, arrays and lists can create matrices and graphs, enabling the representation and manipulation of intricate data relationships. 8. Modularity and Reusability Data structures promote modularity and reusability in software development. Well-designed data structures can be used as building blocks for various applications, reducing code duplication and improving maintainability. 9. Complex Problem Solving Data structures play a crucial role in solving complex computational problems. Algorithms often rely on specific data structures tailored to the problem’s requirements. For instance, graph algorithms use data structures like adjacency matrices or linked lists to represent and traverse graphs efficiently. 10. Resource Efficiency Selecting the right data structure for a particular task can impact the efficiency of an application. Regards to this, Data structures helps in minimizing resource usage, such as time and memory, leading to faster and more responsive software. 11. Scalability Scalability is a critical consideration in modern software development. Data structures that efficiently handle large datasets and adapt to changing workloads are essential for building scalable applications and systems. 12. Algorithm Optimization Algorithms that use appropriate data structures can be optimized for speed and efficiency. For example, by choosing a hash table data structure, you can achieve constant-time average-case lookup operations, improving the performance of algorithms relying on data retrieval. 13. Code Readability and Maintainability Well-defined data structures contribute to code readability and maintainability. They provide clear abstractions for data manipulation, making it easier for developers to understand, maintain, and extend code over time. 14. Cross-Disciplinary Applications Data structures are not limited to computer science; they find applications in various fields, such as biology, engineering, and finance. Efficient data organization and manipulation are essential in scientific research and data analysis. Other benefits: It can store variables of various data types. It allows the creation of objects that feature various types of attributes. It allows reusing the data layout across programs. It can implement other data structures like stacks, linked lists, trees, graphs, queues, etc. Why study data structures & algorithms? They help to solve complex real-time problems. They improve analytical and problem-solving skills. They help you to crack technical interviews. Topics in data structure can efficiently manipulate the data. Studying relevant DSA topics increases job opportunities and earning potential. Therefore, they guarantee career advancement. What are DSA Projects? DSA projects, or Data Structures and Algorithms projects, involve creating software applications that emphasize the use and implementation of various data structures and algorithms to solve complex problems efficiently. An example could be developing a search engine using trie data structures for fast text retrieval or crafting a route optimization application using graph algorithms like Dijkstra’s or A*. These projects help students and professionals demonstrate their proficiency in coding, optimizing data handling, and solving algorithmic challenges, which are crucial skills in software development and computer science. Data Structures Projects Ideas 1. Obscure binary search trees Items, such as names, numbers, etc. can be stored in memory in a sorted order called binary search trees or BSTs. And some of these data structures can automatically balance their height when arbitrary items are inserted or deleted. Therefore, they are known as self-balancing BSTs. Further, there can be different implementations of this type, like the BTrees, AVL trees, and red-black trees. But there are many other lesser-known executions that you can learn about. Some examples include AA trees, 2-3 trees, splay trees, scapegoat trees, and treaps.  You can base your project on these alternatives and explore how they can outperform other widely-used BSTs in different scenarios. For instance, splay trees can prove faster than red-black trees under the conditions of serious temporal locality.  Also, check out our business analytics course to widen your horizon. 2. BSTs following the memoization algorithm Memoization related to dynamic programming. In reduction-memoizing BSTs, each node can memoize a function of its subtrees. Consider the example of a BST of persons ordered by their ages. Now, let the child nodes store the maximum income of each individual. With this structure, you can answer queries like, “What is the maximum income of people aged between 18.3 and 25.3?” It can also handle updates in logarithmic time.  Moreover, such data structures are easy to accomplish in C language. You can also attempt to bind it with Ruby and a convenient API. Go for an interface that allows you to specify ‘lambda’ as your ordering function and your subtree memoizing function. All in all, you can expect reduction-memoizing BSTs to be self-balancing BSTs with a dash of additional book-keeping.  Dynamic coding will need cognitive memorisation for its implementation. Each vertex in a reducing BST can memorise its sub–trees’ functionality. For example, a BST of persons is categorised by their age. This DSA topics based project idea allows the kid node to store every individual’s maximum salary. This framework can be used to answer the questions like “what’s the income limit of persons aged 25 to 30?” Checkout: Types of Binary Tree Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses 3. Heap insertion time When looking for data structure projects, you want to encounter distinct problems being solved with creative approaches. One such unique research question concerns the average case insertion time for binary heap data structures. According to some online sources, it is constant time, while others imply that it is log(n) time. It is one of great examples of data science project.  But Bollobas and Simon give a numerically-backed answer in their paper entitled, “Repeated random insertion into a priority queue.” First, they assume a scenario where you want to insert n elements into an empty heap. There can be ‘n!’ possible orders for the same. Then, they adopt the average cost approach to prove that the insertion time is bound by a constant of 1.7645. When looking for Data Structures tasks in this project idea, you will face challenges that are addressed using novel methods. One of the interesting research subjects is the mean response insertion time for the sequential heap DS. Inserting ‘n’ components into an empty heap will yield ‘n!’ arrangements which you can use in suitable DSA projects in C++. Subsequently, you can implement the estimated cost approach to specify that the inserting period is limited by a fixed constant. Our learners also read: Excel online course free! 4. Optimal treaps with priority-changing parameters Treaps are a combination of BSTs and heaps. These randomized data structures involve assigning specific priorities to the nodes. You can go for a project that optimizes a set of parameters under different settings. For instance, you can set higher preferences for nodes that are accessed more frequently than others. Here, each access will set off a two-fold process: Choosing a random number Replacing the node’s priority with that number if it is found to be higher than the previous priority As a result of this modification, the tree will lose its random shape. It is likely that the frequently-accessed nodes would now be near the tree’s root, hence delivering faster searches. So, experiment with this data structure and try to base your argument on evidence.  Also read: Python online course free! At the end of the project, you can either make an original discovery or even conclude that changing the priority of the node does not deliver much speed. It will be a relevant and useful exercise, nevertheless. Constructing a heap involves building an ordered binary tree and letting it fulfill the “heap” property. But if it is done using a single element, it would appear like a line. This is because in the BST, the right child should be greater or equal to its parent, and the left child should be less than its parent. However, for a heap, every parent must either be all larger or all smaller than its children. The numbers show the data structure’s heap arrangement (organized in max-heap order). The alphabets show the tree portion. Now comes the time to use the unique property of treap data structure in DSA projects in C++. This treap has only one arrangement irrespective of the order by which the elements were chosen to build the tree. You can use a random heap weight to make the second key more useful. Hence, now the tree’s structure will completely depend on the randomized weight offered to the heap values. In the file structure mini project topics, we obtain randomized heap priorities by ascertaining that you assign these randomly. Top Data Science Skills to Learn Top Data Science Skills to Learn 1 Data Analysis Course Inferential Statistics Courses 2 Hypothesis Testing Programs Logistic Regression Courses 3 Linear Regression Courses Linear Algebra for Analysis upGrad’s Exclusive Data Science Webinar for you – Transformation & Opportunities in Analytics & Insights document.createElement('video'); https://cdn.upgrad.com/blog/jai-kapoor.mp4 5. Research project on k-d trees K-dimensional trees or k-d trees organize and represent spatial data. These data structures have several applications, particularly in multi-dimensional key searches like nearest neighbor and range searches. It is example of one of the advanced data science projects. Here is how k-d trees operate: Every leaf node of the binary tree is a k-dimensional point Every non-leaf node splits the hyperplane (which is perpendicular to that dimension) into two half-spaces The left subtree of a particular node represents the points to the left of the hyperplane. Similarly, the right subtree of that node denotes the points in the right half. You can probe one step further and construct a self-balanced k-d tree where each leaf node would have the same distance from the root. Also, you can test it to find whether such balanced trees would prove optimal for a particular kind of application.  Also, visit upGrad’s Degree Counselling page for all undergraduate and postgraduate programs. Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? With this, we have covered five interesting ideas that you can study, investigate, and try out. Now, let us look at some more projects on data structures and algorithms.  Read : Data Scientist Salary in India 6. Knight’s travails In this project, we will understand two algorithms in action – BFS and DFS. BFS stands for Breadth-First Search and utilizes the Queue data structure to find the shortest path. Whereas, DFS refers to Depth-First Search and traverses Stack data structures.  For starters, you will need a data structure similar to binary trees. Now, suppose that you have a standard 8 X 8 chessboard, and you want to show the knight’s movements in a game. As you may know, a knight’s basic move in chess is two forward steps and one sidestep. Facing in any direction and given enough turns, it can move from any square on the board to any other square.  If you want to know the simplest way your knight can move from one square (or node) to another in a two-dimensional setup, you will first have to build a function like the one below. knight_plays([0,0], [1,2]) == [[0,0], [1,2]] knight_plays([0,0], [3,3]) == [[0,0], [1,2], [3,3]] knight_plays([3,3], [0,0]) == [[3,3], [1,2], [0,0]]  Furthermore, this project would require the following tasks:  Creating a script for a board game and a night Treating all possible moves of the knight as children in the tree structure Ensuring that any move does not go off the board Choosing a search algorithm for finding the shortest path in this case Applying the appropriate search algorithm to find the best possible move from the starting square to the ending square. 7. Fast data structures in non-C systems languages Programmers usually build programs quickly using high-level languages like Ruby or Python but implement data structures in C/C++. And they create a binding code to connect the elements. However, the C language is believed to be error-prone, which can also cause security issues. Herein lies an exciting project idea.  You can implement a data structure in a modern low-level language such as Rust or Go, and then bind your code to the high-level language. With this project, you can try something new and also figure out how bindings work. If your effort is successful, you can even inspire others to do a similar exercise in the future and drive better performance-orientation of data structures.   Also read: Data Science Project Ideas for Beginners 8. Search engine for data structures The software aims to automate and speed up the choice of data structures for a given API. This project not only demonstrates novel ways of representing different data structures but also optimizes a set of functions to equip inference on them. We have compiled its summary below. The data structure search engine project requires knowledge about data structures and the relationships between different methods. It computes the time taken by each possible composite data structure for all the methods. Finally, it selects the best data structures for a particular case.  Read: Data Mining Project Ideas 9. Phone directory application using doubly-linked lists This project can demonstrate the working of contact book applications and also teach you about data structures like arrays, linked lists, stacks, and queues. Typically, phone book management encompasses searching, sorting, and deleting operations. A distinctive feature of the search queries here is that the user sees suggestions from the contact list after entering each character. You can read the source-code of freely available projects and replicate the same to develop your skills.  This project demonstrates how to address the book programs’ function. It also teaches you about queuing, stacking, linking lists, and arrays. Usually, this project’s directory includes certain actions like categorising, scanning, and removing. Subsequently, the client shows recommendations from the address book after typing each character. This is the web searches’ unique facet. You can inspect the code of extensively used DSA projects in C++ and applications and ultimately duplicate them. This helps you to advance your data science career. 10. Spatial indexing with quadtrees The quadtree data structure is a special type of tree structure, which can recursively divide a flat 2-D space into four quadrants. Each hierarchical node in this tree structure has either zero or four children. It can be used for various purposes like sparse data storage, image processing, and spatial indexing.  Spatial indexing is all about the efficient execution of select geometric queries, forming an essential part of geo-spatial application design. For example, ride-sharing applications like Ola and Uber process geo-queries to track the location of cabs and provide updates to users. Facebook’s Nearby Friends feature also has similar functionality. Here, the associated meta-data is stored in the form of tables, and a spatial index is created separately with the object coordinates. The problem objective is to find the nearest point to a given one.  You can pursue quadtree data structure projects in a wide range of fields, from mapping, urban planning, and transportation planning to disaster management and mitigation. We have provided a brief outline to fuel your problem-solving and analytical skills.  QuadTrees are techniques for indexing spatial data. The root node signifies the whole area and every internal node signifies an area called a quadrant which is obtained by dividing the area enclosed into half across both axes. These basics are important to understand QuadTrees-related data structures topics. Objective: Creating a data structure that enables the following operations Insert a location or geometric space Search for the coordinates of a specific location Count the number of locations in the data structure in a particular contiguous area One of the leading applications of QuadTrees in the data structure is finding the nearest neighbor. For example, you are dealing with several points in a space in one of the data structures topics. Suppose somebody asks you what’s the nearest point to an arbitrary point. You can search in a quadtree to answer this question. If there is no nearest neighbor, you can specify that there is no point in this quadrant to be the nearest neighbor to an arbitrary point. Consequently, you can save time otherwise spent on comparisons. Spatial indexing with Quadtrees is also used in image compression wherein every node holds the average color of each child. You get a more detailed image if you dive deeper into the tree. This project idea is also used in searching for the nods in a 2D area. For example, you can use quadtrees to find the nearest point to the given coordinates. Follow these steps to build a quadtree from a two-dimensional area: Divide the existing two-dimensional space into four boxes. Create a child object if a box holds one or more points within.  This object stores the box’s 2D space. Don’t create a child for a box that doesn’t include any points. Repeat these steps for each of the children. You can follow these steps while working on one of the file structure mini project topics. 11. Graph-based projects on data structures You can take up a project on topological sorting of a graph. For this, you will need prior knowledge of the DFS algorithm. Here is the primary difference between the two approaches: We print a vertex & then recursively call the algorithm for adjacent vertices in DFS. In topological sorting, we recursively first call the algorithm for adjacent vertices. And then, we push the content into a stack for printing.  Therefore, the topological sort algorithm takes a directed acyclic graph or DAG to return an array of nodes.  Let us consider the simple example of ordering a pancake recipe. To make pancakes, you need a specific set of ingredients, such as eggs, milk, flour or pancake mix, oil, syrup, etc. This information, along with the quantity and portions, can be easily represented in a graph. But it is equally important to know the precise order of using these ingredients. This is where you can implement topological ordering. Other examples include making precedence charts for optimizing database queries and schedules for software projects. Here is an overview of the process for your reference: Call the DFS algorithm for the graph data structure to compute the finish times for the vertices Store the vertices in a list with a descending finish time order  Execute the topological sort to return the ordered list  12. Numerical representations with random access lists In the representations we have seen in the past, numerical elements are generally held in Binomial Heaps. But these patterns can also be implemented in other data structures. Okasaki has come up with a numerical representation technique using binary random access lists. These lists have many advantages: They enable insertion at and removal from the beginning They allow access and update at a particular index Know more: The Six Most Commonly Used Data Structures in R 13. Stack-based text editor Your regular text editor has the functionality of editing and storing text while it is being written or edited. So, there are multiple changes in the cursor position. To achieve high efficiency, we require a fast data structure for insertion and modification. And the ordinary character arrays take time for storing strings.  You can experiment with other data structures like gap buffers and ropes to solve these issues. Your end objective will be to attain faster concatenation than the usual strings by occupying smaller contiguous memory space.  This project idea handles text manipulation and offers suitable features to improve the experience. The key functionalities of text editors include deleting, inserting, and viewing text. Other features needed to compare with other text editors are copy/cut and paste, find and replace, sentence highlighting, text formatting, etc. This project idea’s functioning depends on the data structures you determined to use for your operations. You will face tradeoffs when choosing among the data structures. This is because you must consider the implementation difficulty for the memory and performance tradeoffs. You can use this project idea in different file structure mini project topics to accelerate the text’s insertion and modification. Conclusion Data structure skills are foundational in software development, especially for managing vast data sets in today’s digital landscape. Top companies like Adobe, Amazon, and Google seek professionals proficient in data structures and algorithms for lucrative positions. During interviews, recruiters evaluate not only theoretical knowledge but also practical skills. Therefore, practicing data structure project ideas for beginners is essential to kickstart your career.  If you’re interested in delving into data science, I strongly recommend exploring IIIT-B & upGrad’s Executive PG Programme in Data Science. Tailored for working professionals, this program offers 10+ case studies & projects, practical workshops, mentorship with industry experts, 1-on-1 sessions with mentors, 400+ hours of learning, and job assistance with leading firms. It’s a comprehensive opportunity to advance your skills and excel in the field. 

by Rohit Sharma

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20 May 2024

Top 30 Python Pattern Programs You Must Know About
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41023

Top 30 Python Pattern Programs You Must Know About

Summary Pattern in Python or “Python patterns” is an essential part of Python programming, especially when you are just starting out with using algorithms to print various types of output in Python. Number pattern programs in Python are extremely popular when it comes to examinations and assessments. For example, pattern questions in Python such as the number pyramid pattern in Python are recurring questions given in various job interviews. In this article, you will learn the top 18 python pattern programs you must know about. Take a glimpse below. Pattern #1: Simple Number Triangle Pattern  Pattern #2: Inverted Pyramid of Numbers Pattern #3: Half Pyramid Pattern of Numbers  Pattern #4: Inverted Pyramid of Descending Numbers  Pattern #5: Inverted Pyramid of the Same Digit   Pattern #6: Reverse Pyramid of Numbers  Pattern #7: Inverted Half Pyramid Number Pattern  Pattern #8: Pyramid of Natural Numbers Less Than 10  Pattern #9: Reverse Pattern of Digits from 10  Pattern #10: Unique Pyramid Pattern of Digits  Pattern #11: Connected Inverted Pyramid Pattern of Numbers  Pattern #12: Even Number Pyramid Pattern …so on… Read the full article to know more about all 18 Python Pattern Programs in detail. Python is a user-friendly language, allowing diverse helpful features to simplify the coding process and enable users to develop exceptional programming prowess. Users are free to access diverse libraries containing modules with program codes for structuring any framework, and it is one of the greatest reasons why pattern question in python fame among programmers is unlikely to die down. When put to use, these programming skills can reap exceptional results though making it through the interview round with challenging questionnaires can be a difficult task requiring more logical programming solutions.  Preparing for technical interviews takes a lot of preparation, and it’s highly probable that you might have to create Python pattern programs there. That’s why we’ve sorted a list of multiple ideas for pattern printing in Python to start your preparations.  Python is a user-friendly language, allowing diverse helpful features to simplify the coding process and enable users to develop exceptional programming prowess. Users are free to access diverse libraries containing modules with program codes for structuring any framework, and it is one of the greatest reasons why Python’s fame among programmers is unlikely to die down. Python has its applications in many areas such as web development, game development, software development, network programming, and database access. Python pattern programs are also useful because it creates data visualisation, solves complex calculations, and helps in the analysation of the data. It has many other advantages also added to it, for example, its syntax is similar to the English language, so it is easier to code and also easily portable, and has massive libraries to use. There are some of the factors that contribute to python’s popularity. When put to use, these programming skills can reap exceptional results though making it through the interview round with challenging questionnaires can be a difficult task requiring more logical programming solutions.  upGrad’s Exclusive Data Science Webinar for you – Transformation & Opportunities in Analytics & Insights document.createElement('video'); https://cdn.upgrad.com/blog/jai-kapoor.mp4   What are Python patterns?  Python patterns encode programs in different shapes and formats to create recognized patterns. These patterns are built using different combinations of codes to allow programmers logical practice to implement the same strategy in real-life courses and improve programming skills. Some of the most famous Python programs called the number triangle, pyramid pattern in Python, reverse, mirrored, equilateral, and star pattern in Python equip programmers to accomplish complex programming issues. Therefore, preparing them to take on kind of coding patterns for precision and explaining how to print pattern in Python. We have multiple kinds of Python pattern programs in this list, so choose your expertise and experience. Make sure that you understand what part of code does what before you move onto the next pattern. Without a proper understanding of how the system works, you would face a lot of difficulty in explaining its working. Also, Check out our python free courses Many times, the interviewer asks for an explanation of how you performed pattern printing in Python. Knowing how everything works will help you in answering those questions effectively. Data science certification in your resume improves your chance of getting hired.  While how to print pattern in Python is a typical question for many people, these Python patterns can easily be printed using multiple combinations of multiple for, while, or for while loops. The working of pyramid and star pattern in pattern question in python depends on a few of the major points that include- the outer loop outputs the number of rows in a Python syntax, while to output the number of columns, an inner loop is used.  Check out all trending Python tutorial concepts in 2024 You’ll find the Python code along with every pattern below:  Key Features of Python Having gained an understanding of what Python pattern programs entail, it’s time to delve into several fundamental characteristics that define Python:  Easy to Learn and Readable Language       Python boasts exceptional ease of learning, whose syntax is remarkably straightforward, and its learning curve associated with Python pattern programs is notably gentle. Coding in Python is highly accessible, and using indentation instead of traditional curly braces contributes to the readability of Python code. This quality has led to many educational institutions, ranging from schools to colleges and universities, adopting Python as the initial coding language for their students embarking on their coding journeys. Interpreted Language  Python pattern programs function as the interpreted language, a programming paradigm where programs are typically interpreted rather than compiled into machine-level instructions. In this approach, the instructions aren’t executed directly by the target machine; instead, they are read and run by a separate program called an interpreter. Python includes an Interactive Development Environment (IDLE) as part of its package. This IDLE functions as an interpreter and follows the structure of REPL (Read Evaluate Print Loop), akin to the operation of Node.js. When using IDLE, Python code is executed, and its output is displayed one line at a time. Consequently, when running a line of Python code, any errors are promptly exhibited, including a comprehensive stack trace detailing the error. Dynamically Typed Language Python pattern programs operate as a dynamically typed language, implying that there’s no requirement to declare the data types of defined variables explicitly. Instead, the Python interpreter is responsible for ascertaining variable data types during runtime, guided by the kinds of components within an expression. While this characteristic enhances coding convenience for programmers, it also introduces the potential for runtime errors. To elaborate, Python adheres to the concept of duck typing. Open Source and Free Python is an open-source programming language, accessible for free download from its official website. The community of Python pattern programs enthusiasts consistently collaborates to enhance the Python codebase, striving for continual improvement. High-Level Language A high-level language (HLL) is a programming language that empowers programmers to create programs mainly agnostic to the specifics of a particular computer architecture. These languages are considered “high-level” due to their close resemblance to human languages and their significant abstraction from machine-level languages. Unlike C, Python belongs to the category of high-level languages. Python’s comprehensibility is notably high, and its proximity to the user surpasses that of middle-level languages like C. With Python, there’s no need to retain intricate system architecture details or handle memory management intricacies. Portable Python pattern programs possess portability, signifying that identical code may be employed across diverse machines. For instance, if you create a Python script on your Mac, it can be executed on Linux or Windows without necessitating any modifications. This eliminates the requirement to adapt the code for various platforms, eliminating the need to develop separate programs for multiple operating systems. Object-Oriented and Procedure-Oriented A programming language adopts an object-oriented approach when its design centers on data and objects rather than functions and logic. Conversely, a programming language is considered procedure-oriented when its emphasis lies more on parts that can be reused. A pivotal feature of Python is its capability to accommodate object-oriented and procedure-oriented programming paradigms. Support for GUI One of the critical aspects of any programming language is support for GUI or Graphical User Interface. A user may interact with software easily using the GUI. Moreover, Python pattern programs also offer several toolkits, like wxPython, Tkinter, and JPython, enabling the GUI’s fast and easy development. You can also consider doing our Python Bootcamp course from upGrad to upskill your career. Eager to put your Python skills to the test or build something amazing? Dive into our collection of Python project ideas to inspire your next coding adventure. Python Pattern Program List for Beginners With Examples Pattern problems in python encode programs in different shapes and formats to create recognized patterns. These patterns are built using different combinations of codes to allow programmers logical practice to implement the same strategy in real-life courses and improve programming skills. Python design patterns are important because they help to generate repeatable solutions to those problems which are occurring recurrently. So this way continuous coding to solve the same problem is not required. The design patterns help in creating a well-structured software using lesser time. These patterns also helps in creation of objects without specifying their type. Some of the most famous Python pattern programs called the number triangle, pyramid pattern in Python, reverse, mirrored, equilateral, and star pattern in Python equip programmers to accomplish complex programming issues. Therefore, preparing them to take on kind of coding patterns for precision and explaining how to print pattern in Python. While how to print pattern in Python is a typical question for many people, these Python patterns can easily be printed using multiple combinations of multiple for, while, or for while loops. The working of pyramid and star pattern in Python depends on a few of the major points that include- the outer loop outputs the number of rows in a Python syntax, while to output the number of columns, an inner loop is used.  The Python pattern programs can be printed with the help of loops. The outer loop handles the number of rows, whereas the inner loop handles the number of columns. Once the pattern style has been worked with, the different patterns can be printed such as numbers, alphabet, stars, etc. When working with Python pattern programs for practice, you can use an online emulator such as GDB Online Debugger. When it comes Python all pattern programs can be used with emulators without needing to install Python on your system. However, if you want to know how to print pattern in Python offline, we have got your back. You can simply go to Python’s official website and download the right Python version for your OS. Once you finish setting up Python in your system, you can make a .py file and start using the codes below to run programs such as the number pyramid pattern in Python or Python pattern programs using for loop. Top Data Science Skills to Learn Top Data Science Skills to Learn 1 Data Analysis Course Inferential Statistics Courses 2 Hypothesis Testing Programs Logistic Regression Courses 3 Linear Regression Courses Linear Algebra for Analysis Pattern #1: Simple Number Triangle Pattern Pattern: 1   2 2   3 3 3   4 4 4 4   5 5 5 5 5 Code: rows = 6 for num in range(rows):   for i in range(num):       print(num, end=" ") # print number   # line after each row to display pattern correctly     print(" ") Our learners also read: Excel online course free! Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses Pattern #2: Inverted Pyramid of Numbers Pattern: 1 1 1 1 1  2 2 2 2  3 3 3  4 4  5 Code: rows = 5 b = 0 for i in range(rows, 0, -1):   b += 1   for j in range(1, i + 1):       print(b, end=' ')     print('\r') Read: Career Opportunities in Python: Everything You Need To Know Pattern #3: Half Pyramid Pattern of Numbers Pattern: 1  1 2  1 2 3  1 2 3 4  1 2 3 4 5 Code: rows = 5 for row in range(1, rows+1):   for column in range(1, row + 1):       print(column, end=' ')     print("") Our learners also read – learn python online free! Pattern #4: Inverted Pyramid of Descending Numbers Pattern: 5 5 5 5 5  4 4 4 4  3 3 3  2 2  1 Code: rows = 5 for i in range(rows, 0, -1):   num = i   for j in range(0, i):       print(num, end=' ')     print("\r") Must read: Data structures and algorithms free course! Pattern #5: Inverted Pyramid of the Same Digit Pattern: 5 5 5 5 5  5 5 5 5  5 5 5  5 5  5 Code: rows = 5 num = rows for i in range(rows, 0, -1):   for j in range(0, i):       print(num, end=' ')     print("\r") Also, check Full Stack Development Bootcamp Job Guaranteed from upGrad Pattern #6: Reverse Pyramid of Numbers Pattern: 1  2 1  3 2 1  4 3 2 1  5 4 3 2 1 Code: rows = 6 for row in range(1, rows):   for column in range(row, 0, -1):       print(column, end=' ')     print("") Also visit upGrad’s Degree Counselling page for all undergraduate and postgraduate programs. Pattern #7: Inverted Half Pyramid Number Pattern Pattern: 0 1 2 3 4 5  0 1 2 3 4  0 1 2 3  0 1 2  0 1 Code: rows = 5 for i in range(rows, 0, -1):   for j in range(0, i + 1):       print(j, end=' ')     print("\r") Pattern #8: Pyramid of Natural Numbers Less Than 10 Pattern: 1  2 3 4  5 6 7 8 9 Code: currentNumber = 1 stop = 2 rows = 3 # Rows you want in your pattern for i in range(rows):   for column in range(1, stop):       print(currentNumber, end=' ')       currentNumber += 1   print("")     stop += 2 Pattern #9: Reverse Pattern of Digits from 10  Pattern: 1 3 2 6 5 4 10 9 8 7 Code: start = 1 stop = 2 currentNumber = stop for row in range(2, 6):   for col in range(start, stop):       currentNumber -= 1       print(currentNumber, end=' ')   print("")   start = stop   stop += row     currentNumber = stop Pattern #10: Unique Pyramid Pattern of Digits Pattern: 1  1 2 1  1 2 3 2 1  1 2 3 4 3 2 1  1 2 3 4 5 4 3 2 1 Code: rows = 6 for i in range(1, rows + 1):   for j in range(1, i - 1):       print(j, end=" ")   for j in range(i - 1, 0, -1):       print(j, end=" ")     print() Check out: Top 36 Python Interview Questions & Answers: Ultimate Guide Pattern #11: Connected Inverted Pyramid Pattern of Numbers Pattern: 5 4 3 2 1 1 2 3 4 5  5 4 3 2 2 3 4 5  5 4 3 3 4 5  5 4 4 5  5 5 Code: rows = 6 for i in range(0, rows):   for j in range(rows - 1, i, -1):       print(j, '', end='')   for l in range(i):       print(' ', end='')   for k in range(i + 1, rows):       print(k, '', end='')     print('\n') Pattern #12: Even Number Pyramid Pattern Pattern: 10  10 8  10 8 6  10 8 6 4  10 8 6 4 2 Code: rows = 5 LastEvenNumber = 2 * rows evenNumber = LastEvenNumber for i in range(1, rows+1):   evenNumber = LastEvenNumber   for j in range(i):       print(evenNumber, end=' ')       evenNumber -= 2     print("\r") Pattern #13: Pyramid of Horizontal Tables Pattern: 0   0 1   0 2 4   0 3 6 9   0 4 8 12 16   0 5 10 15 20 25   0 6 12 18 24 30 36 Code: rows = 7 for i in range(0, rows):   for j in range(0, i + 1):       print(i * j, end=' ')     print() Pattern #14: Pyramid Pattern of Alternate Numbers Pattern: 1  3 3  5 5 5  7 7 7 7  9 9 9 9 9 Code: rows = 5 i = 1 while i <= rows:   j = 1   while j <= i:       print((i * 2 - 1), end=" ")       j = j + 1   i = i + 1     print() Pattern #15: Mirrored Pyramid (Right-angled Triangle) Pattern of Numbers Pattern:            1           1 2        1 2 3     1 2 3 4   1 2 3 4 5 Code: rows = 6 for row in range(1, rows):   num = 1   for j in range(rows, 0, -1):       if j > row:           print(" ", end=' ')       else:           print(num, end=' ')           num += 1     print("") Pattern #16: Equilateral Triangle with Stars (Asterisk Symbol) Pattern:             *               * *              * * *             * * * *            * * * * *           * * * * * *          * * * * * * * Code: print("Print equilateral triangle Pyramid using stars ") size = 7 m = (2 * size) - 2 for i in range(0, size):   for j in range(0, m):       print(end=" ")   m = m - 1 # decrementing m after each loop   for j in range(0, i + 1):       # printing full Triangle pyramid using stars       print("* ", end=' ')     print(" ") Pattern #17: Downward Triangle Pattern of Stars Pattern:         * * * * * *           * * * * *            * * * *             * * *              * *               *  Code: rows = 5 k = 2 * rows - 2 for i in range(rows, -1, -1):   for j in range(k, 0, -1):       print(end=" ")   k = k + 1   for j in range(0, i + 1):       print("*", end=" ")     print("") Pattern #18: Pyramid Pattern of Stars Pattern: *  * *  * * *  * * * *  * * * * * Code: rows = 5 for i in range(0, rows):   for j in range(0, i + 1):       print("*", end=' ')     print("\r") These are some of the most widely used Python patterns fueling expertise for core and advanced Python programming skills. The concept for the pattern for loop is popularly featured in interview questions to check your programming and logic skills. With the nature and syntax of any programming language limiting its uses, Python is comparatively programmer-friendly, with detailed modules to implement relevant coding. These pyramid and triangle pattern in Python can easily be printed using a sequence of multiple loops.   The given patterns above such as star and pyramid pattern in Python, offer an in-depth understanding of patterns in Python and their logical implementation. Besides being used in interviews to check expertise level, patterns like triangle pattern in Python can be further used to learn areas like data science.  Also Read: 42 Exciting Python Project Ideas & Topics for Beginners Pattern #19: Spiral Star Pattern Pattern: def spiral_star_pattern(rows):     matrix = [[‘ ‘] * rows for _ in range(rows)]     directions = [(0, 1), (1, 0), (0, -1), (-1, 0)]     direction = 0     row, col = 0, 0     for i in range(1, rows * rows + 1):         matrix[row][col] = ‘*’         new_row, new_col = row + directions[direction][0], col + directions[direction][1]         if 0 <= new_row < rows and 0 <= new_col < rows and matrix[new_row][new_col] == ‘ ‘:             row, col = new_row, new_col         else:             direction = (direction + 1) % 4             row, col = row + directions[direction][0], col + directions[direction][1]     for i in range(rows):         for j in range(rows):             print(matrix[i][j], end=” “)         print() spiral_star_pattern(4) Pattern #20: Prime Number Spiral Pattern Pattern: Code: def is_prime(num):     if num < 2:         return False     for i in range(2, int(num**0.5) + 1):         if num % i == 0:             return False     return True   def prime_spiral_pattern(rows):     matrix = [[‘ ‘] * rows for _ in range(rows)]     directions = [(0, 1), (1, 0), (0, -1), (-1, 0)]     direction = 0     row, col = 0, 0     num = 1     for i in range(1, rows * rows + 1):         if is_prime(num):             matrix[row][col] = num         num += 1         new_row, new_col = row + directions[direction][0], col + directions[direction][1]         if 0 <= new_row < rows and 0 <= new_col < rows and matrix[new_row][new_col] == ‘ ‘:             row, col = new_row, new_col         else:             direction = (direction + 1) % 4             row, col = row + directions[direction][0], col + directions[direction][1]     for i in range(rows):         for j in range(rows):             print(str(matrix[i][j]).rjust(3) if matrix[i][j] != ‘ ‘ else ‘   ‘, end=” “)         print() prime_spiral_pattern(5) Pattern #21: Sierpinski Triangle Pattern: Code: def sierpinski_triangle(n):     def draw_triangle(height):         triangle = []         for i in range(height):             spaces = ‘ ‘ * (height – i – 1)             stars = ‘*’ * (2 * i + 1)             triangle.append(spaces + stars + spaces)         return triangle     def merge_triangles(top, bottom):         return [t1 + ‘ ‘ + t2 for t1, t2 in zip(top, bottom)]     def generate_sierpinski(level):         if level == 0:             return [‘*’]         else:             lower = generate_sierpinski(level – 1)             upper = draw_triangle(2 ** (level – 1))             return merge_triangles(upper, lower)     triangle = generate_sierpinski(n)     for line in triangle:         print(line) sierpinski_triangle(4) Pattern #22: Fractal Tree Pattern: Code: def fractal_tree(height):     def draw_branch(length):         if length <= 0:             return [”]         else:             trunk = ‘|’ * length             branches = [‘/’.rjust(length – i, ‘ ‘) + ‘\\’ + ‘\n’ for i in range(length)]             return branches + [trunk]     def merge_branches(top, bottom):         return [t1 + t2 for t1, t2 in zip(top, bottom)]     def generate_tree(level):         if level == 0:             return [”]         else:             lower = generate_tree(level – 1)             upper = draw_branch(2 ** (level – 1))             return merge_branches(upper, lower)     tree = generate_tree(height)     for line in tree:         print(line) fractal_tree(4) Pattern #23: Peano Curve Pattern: Code: def peano_curve(order, size):     def draw_peano(order, size, direction):         if order == 0:             return []         else:             sub_curve = draw_peano(order – 1, size, direction)             sub_curve.append(direction)             sub_curve += draw_peano(order – 1, size, 0)             sub_curve += draw_peano(order – 1, size, direction)             sub_curve.append(0)             sub_curve += draw_peano(order – 1, size, -direction)             sub_curve += draw_peano(order – 1, size, 0)             sub_curve += draw_peano(order – 1, size, -direction)             sub_curve += draw_peano(order – 1, size, 0)             sub_curve += draw_peano(order – 1, size, direction)             sub_curve.append(0)             sub_curve += draw_peano(order – 1, size, direction)             sub_curve += draw_peano(order – 1, size, 0)             sub_curve += draw_peano(order – 1, size, -direction)             sub_curve += draw_peano(order – 1, size, direction)             return sub_curve     def scale_curve(curve, size):         scaled_curve = []         for step in curve:             scaled_curve.extend([step] * size)         return scaled_curve     curve = draw_peano(order, size, 1)     scaled_curve = scale_curve(curve, size)     # Displaying the output     for step in scaled_curve:         if step == 1:             print(‘|’, end=”)         elif step == -1:             print(‘-‘, end=”)         else:             print(‘ ‘, end=”)     print() # Example with order=2 and size=3 peano_curve(2, 3) Pattern #24: Hilbert Curve Pattern: Code: def hilbert_curve(order, size):     def draw_hilbert(order, size, direction):         if order == 0:             return []         else:             sub_curve = draw_hilbert(order – 1, size, -direction)             sub_curve.append(direction)             sub_curve += draw_hilbert(order – 1, size, direction)             sub_curve.append(0)             sub_curve += draw_hilbert(order – 1, size, direction)             sub_curve.append(-direction)             sub_curve += draw_hilbert(order – 1, size, -direction)             return sub_curve     def scale_curve(curve, size):         scaled_curve = []         for step in curve:             scaled_curve.extend([step] * size)         return scaled_curve     hilbert = draw_hilbert(order, size, 1)     scaled_hilbert = scale_curve(hilbert, size)     for step in scaled_hilbert:         if step == 1:             print(‘|’, end=”)         elif step == -1:             print(‘-‘, end=”)         else:             print(‘ ‘, end=”)     print() hilbert_curve(3, 3) Pattern #25: Dragon Curve Pattern: Code: def dragon_curve(order):     def generate_curve(order):         if order == 0:             return [1]         else:             prev_curve = generate_curve(order – 1)             return prev_curve + [1] + [1 if x == 0 else 0 for x in reversed(prev_curve)]     curve = generate_curve(order)     for step in curve:         if step == 1:             print(‘+’, end=”)         else:             print(‘-‘, end=”)     print() dragon_curve(5) Pattern #26: Alternating Pyramid Pattern The alternating pyramid pattern is a very popular pattern program in Python. Pattern: Code: def alternating_pyramid_pattern(rows):     def is_even(num):         return num % 2 == 0     def get_element(row, col):         if is_even(row + col):             return str(row)         else:             return ‘*’     for i in range(1, rows + 1):         for j in range(1, 2 * rows):             if j <= rows – i or j >= rows + i:                 print(‘ ‘, end=”)             else:                 print(get_element(i, j – rows), end=”)         print() alternating_pyramid_pattern(5) Pattern #27: Butterfly Pattern With Numbers Let us create a program that prints a butterfly pattern where each half of the butterfly consists of numbers in increasing order and the middle column is left empty. The numbers represent the column-wise sequence. The butterfly wings are symmetrical, and the numbers are printed in a zigzag fashion. Pattern: Code: def butterfly_pattern(rows):     for i in range(1, rows + 1):         for j in range(1, 2 * rows + 1):             if j <= i or j > 2 * rows – i:                 print(j, end=’ ‘)             else:                 print(‘ ‘, end=’ ‘)         print()     for i in range(rows, 0, -1):         for j in range(1, 2 * rows + 1):             if j <= i or j > 2 * rows – i:                 print(j, end=’ ‘)             else:                 print(‘ ‘, end=’ ‘)         print() butterfly_pattern(4) Pattern #28: Diamond Pattern With Alphabets Let us make a program that will print a diamond pattern where each level of the diamond consists of characters from ‘A‘ to the current row’s character. Pattern: Code: def diamond_alphabet_pattern(rows):     start_char = ord(‘A’)     for i in range(1, rows + 1):         for j in range(1, rows – i + 1):             print(” “, end=” “)         for j in range(1, 2 * i):             print(chr(start_char + i – 1), end=” “)         print()     for i in range(rows – 1, 0, -1):         for j in range(1, rows – i + 1):             print(” “, end=” “)         for j in range(1, 2 * i):             print(chr(start_char + i – 1), end=” “)         print() diamond_alphabet_pattern(4) Pattern #29: Pyramid of Squares Let us build a program that prints a pyramid of squares where each level of the pyramid consists of numbers in an increasing and then decreasing order. The numbers represent the row-wise sequence. Pattern: Code: def pyramid_of_squares(rows):     for i in range(1, rows + 1):         for j in range(1, rows – i + 1):             print(” “, end=” “)         for j in range(1, i + 1):             print(j, end=” “)         for j in range(i – 1, 0, -1):             print(j, end=” “)         print() pyramid_of_squares(5) Pattern #30: Flipped Mountain Pattern Let us look at an example of the flipped mountain pattern program in Python. This is another example of creating Python pattern programs using for loop. Pattern: Code: def flipped_mountain_pattern(height):     for i in range(height, 0, -1):         spaces = ” ” * (height – i)         mountains = “*” * (2 * i – 1)         print(spaces + mountains) flipped_mountain_pattern(5) Now that we have covered these 30 pattern questions in Python, you should have a strong understanding of patterns in Python. Learn More About Python Programs list These are some of the most widely used Python pattern programs fueling expertise for core and advanced Python programming skills. The concept for the pattern for loop is popularly featured in interview questions to check your programming and logic skills. With the nature and syntax of any programming language limiting its uses, Python is comparatively programmer-friendly, with detailed modules to implement relevant coding. These pyramid and triangle pattern in Python can easily be printed using a sequence of multiple loops.   Python pattern programs is used to develop multiple applications which are compatible with various platforms. It helps in handling data analysis, data visualisation, text processing, etc. The number of patterns depends upon the number of loops. The two loops have their own significance such as the first loop is used for the row. And the second loop works for the column. Pattern matching python is also one of the features which facilitates providing a pattern and the action associated with it. The action can be taken forward if the data fits. The given pattern program in python above such as star and pyramid pattern in Python, offer an in-depth understanding of patterns in Python and their logical implementation. Besides being used in interviews to check expertise level, patterns like triangle pattern in Python can be further used to learn areas like data science.  The reason why Python is useful is that its syntax is similar to the English language. This syntax helps the programmers in developing a code that can be coded using fewer lines. It follows the interpreter system where the code can be executed immediately as it is written. If you’re interested in learning more about Python, go to our blog and find multiple detailed articles on this topic.   If you have any questions regarding the Python pattern programs we’ve shared here, please let us know through the comments below. We’d love to hear from you.  There are plenty of Python pattern programs out there and the possibilities are endless when it comes to programs with symbol patterns in Python or number pattern programs in Python. Pattern problems in Python or patterns in Python are an essential part of Python programming and Data Science in general. If you are curious to learn about data science, check out IIIT-B & upGrad’s Executive PG Program in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.

by Rohit Sharma

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19 May 2024

15 Exciting Data Science Project Ideas &#038;  Topics for Beginners [2024]
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15 Exciting Data Science Project Ideas & Topics for Beginners [2024]

Summary: In this Article, you will learn about 15 exciting data science project ideas & topics for beginners. 1. Beginner Level | Data Science Project Ideas Fake News Detection Human Action Recognition Forest Fire Prediction Road Lane Line Detection 2. Data Science Projects Ideas |Intermediate Level Recognition of Speech Emotion Gender and Age Detection with Data Science Driver Drowsiness Detection in Python Handwritten Digit & Character Recognition Project 3. Advance Level Data Science Projects Ideas Credit Card Fraud Detection Project Customer Segmentations Traffic Signs Recognition 4. Top Data Analytics Projects Web Scraping Data Cleaning Exploratory Data Analysis Sentiment Analysis Read more to know each in detail. An Expression on Data Science Project Ideas Data Science is continuously thriving as a great career option for this generation. It is among the most promising & happening choices altogether. The market is boosting up with more demands for Data Scientists. It has been reported recently that the demand will increase further to many folds in the coming years. So, if you are a data science beginner, the best thing you can do is work on some real-time data science project ideas. You can also check out our free courses offered by upGrad under Data Science. So, if you are an aspiring Data Scientist, it is highly recommended to practice skills to become an efficient professional for this field. After grabbing some very good theoretical knowledge on Data Science, if you are really looking ahead to explore what it seems like to be a professional, then now is the time to do some practical projects. You must do some of the technical & real-time Data Science projects so that it helps you boost your career growth. The more you practice with Data Science projects, we assure you that you can keep up the pace towards becoming a sound Data Scientist professional. Check out our Python Bootcamp created for working professionals. Therefore, if you do some live Data Science Projects, it will enhance your knowledge, technical skills, and overall confidence. But most importantly, if you showcase even a few Data Science projects in your resume, then getting a good job is much easier for you. Why so? Because then the interviewer will know that you are really serious about a Data Science career. Your real-time experience on Live Data Science Projectswill let you hold a strong grip on Data Science trends & technologies. So, layout your hands on real-time Data Science projects & you will know how beneficial it will be for your speedy career growth. After all these discussions, we know that finding that perfect Data Science Project ideafor your Data Science project concerns you even more than its actual implementation. Our learners also read: Python online course free! In this Data Science blog, we have listed out the names of a few Data Science Project ideas. And to answer your question – ‘What kind of Data Science project is good to start with?’, we have compiled a few good Data Science Project ideas for you to choose from. The article also includes some of the best data science projects for beginners, that you can check out.  No Coding Experience Required. 360° Career support. PG Diploma in Machine Learning & AI from IIIT-B and upGrad. What is Data Science? Data Science is a versatile discipline with a mix of various data science research topics and projects on data science like, statistics, mathematics, computer science, etc, to unearth meaningful insights from data. However, the process involves gathering, refining, scrutinizing, and interpreting extensive datasets to unveil patterns, trends, and correlations, facilitating informed data-driven decisions.  Data Scientists employ an array of tools and techniques, including machine learning, data visualization, and predictive modeling, to extract valuable insights that propel business growth. By amalgamating diverse skills, data science projects or data science topics for project forms an important bridge between raw data and actionable insights, fostering a deeper understanding of complex datasets and empowering organizations to make strategic decisions based on empirical evidence. What is the Future Demand for Data Science? Sustained Growth in Industries The future demand for data science projects or data science project ideas remains robust as industries across the spectrum increasingly rely on data-driven decision-making. From healthcare and finance to technology, the pervasive influence of data science project ideas 2023 is expected to grow, creating a sustained demand for skilled professionals. Evolving Technological Landscape As technology continues to advance, so does the demand for data science expertise or data science project ideas 2023. The artificial intelligence, machine learning, and big data technologies present new opportunities for data scientists while doing data science projects for final year to harness and interpret wide amounts of data, providing valuable insights that drive innovation and competitiveness. Integration with AI and Automation The integration of Data Science along with artificial intelligence (AI) and automation further fuels demand. Organizations are seeking data scientists after doing data science projects for final year to develop algorithms, machine learning models, and automation solutions that optimize processes, enhance efficiency, and contribute to strategic decision-making. Emerging Fields and Specializations Data science projects or data science research topics is branching into specialized fields such as data engineering, natural language processing, and computer vision. As these domains gain prominence, the demand for topics for data analysis project or data science projects for beginners and professionals with niche expertise is expected to rise. The diversification of roles within the broader field of data science projects or data science project ideas contributes to a nuanced demand landscape. Enhanced Business Intelligence In an era where data is often hailed as the new currency, businesses are increasingly recognizing the pivotal role of Data Science in gaining a competitive edge. The ability to transform raw data into actionable insights enhances business intelligence, enabling companies to make informed decisions, understand customer behavior, and adapt to market trends swiftly. Global Adoption and Data Privacy As data science projects or data science topics for project becomes a global phenomenon, challenges related to data privacy and security emerge. The demand for professionals and topics for data analysis project well-versed in ethical data practices and regulatory compliance is on the rise. Data scientists who can navigate these challenges in the form of data engineer projects while extracting valuable insights will be in high demand, ensuring the responsible and effective use of data. Why is Data Science a Very Attractive Career Opportunity? Embarking on a career as a data scientist and looking for project ideas for data analytics or data engineer projects isn’t just visually appealing from the outside; it also offers an engaging and rewarding journey within. Let’s delve into the various perks that make this career path stand out while getting into project ideas for data analytics. Freedom One of the foremost perks that data scientists revel in is the freedom to choose their data science projects for beginners and technologies. Unlike being confined to a specific industry, data scientists can navigate diverse realms, especially those brimming with enormous potential. This liberty fosters a dynamic work environment, keeping the profession consistently invigorating. Working with Reputed Organizations The marriage of data science projects with artificial intelligence and machine learning opens doors to collaborations with industry behemoths such as Uber, Apple, and Amazon. The sheer volume of data, or “big data,” stored by these global corporations ensures an enriching experience for data scientists, contributing to the enhancement of user interactions and overall business strategies. Rewarding Salary The financial allure of a data science projects or python data science projects is undeniable. With a median salary exceeding $120,000, data scientists are handsomely rewarded for the value they bring to organizations. This substantial remuneration cements data science as one of the most attractive and best career options. In-Demand Skills In a tech-driven era, the demand for data scientists is soaring, with a growth rate surpassing 100% annually. As predicted by IBM in 2018, this trend continues unabated. The skill set of data scientists remains in high demand, aligning with the ever-evolving technological landscape. Stable Career Option Unlike transient sectors in the corporate landscape, data science stands out as a stable career option. While industries may rise and fall, the relevance and growth trajectory of data science remains steadfast. With the integration of artificial intelligence as a driving force, big data, and consequently, data science, are poised for sustained significance. Entrepreneurial Opportunities A unique advantage for seasoned data scientists lies in the potential to venture into entrepreneurship. This means candidates full of comprehensive industry knowledge data scientists can seamlessly transition into business ownership. This entrepreneurial journey could manifest in ventures within the data science and big data domain or even branch into specific industries they’ve previously engaged with, such as e-commerce or video streaming platforms. Skills Needed to Become a Data Science Professional Technical Proficiency At the core of a data science professional’s skill set lies technical proficiency. This includes mastery of programming languages like Python or R, as well as a strong command of statistical analysis and data manipulation. A solid foundation in these technical aspects empowers professionals to effectively navigate and manipulate datasets. Data Visualization The ability to translate complex data into visually understandable insights is a crucial skill. Data visualization tools like Tableau or Matplotlib help professionals while doing data analysis project ideas for students create compelling visuals that convey patterns and trends. This skill not only aids in the interpretation of data but also enhances communication with stakeholders by presenting findings in a clear and impactful manner. Machine Learning Expertise Proficiency in ML algorithms and techniques is imperative for a data science professional. Understanding supervised and unsupervised learning, regression, and classification algorithms equips individuals to apply predictive analytics, extract meaningful patterns, and make informed decisions based on data-driven models. Domain Knowledge Beyond technical understanding, a data scientist benefits greatly from domain knowledge. Understanding the industry or field in which they operate allows professionals to contextualize data findings. This bridge between data and industry insights enhances the relevance and impact of their analyses, facilitating more informed decision-making. Problem-Solving Skills Data science is inherently about solving problems, and strong problem-solving skills are a cornerstone of success. Professionals need to approach data challenges with a logical mindset, breaking down complex issues into manageable components. This skill ensures effective troubleshooting and the development of innovative solutions. Continuous Learning In the ever-evolving landscape of data science, a commitment to continuous learning is essential. Professionals should stay abreast of emerging technologies, tools, and methodologies. This proactive approach not only keeps their skills relevant but also positions them to leverage the latest advancements, contributing to their effectiveness as data science practitioners. To conclude, becoming a proficient data science professional requires a combination of technical mastery, data visualization skills, machine learning expertise, industry-specific knowledge, problem-solving capabilities, and a commitment to continuous learning. This comprehensive skill set equips professionals to navigate the complexities of data analysis and contribute meaningfully to decision-making processes in a rapidly advancing field. Why Should You Learn Data Science? Before going further into the different data science project ideas that are available, let’s take a look at some of the reasons why data science projects are considered to be so important in today’s world.  1. Data is the new driving force behind industries Needless to say, in today’s technology-driven world, large enterprises across different industries rely heavily on data for everything, starting with their business growth to expansion. Thus, it wouldn’t be too wrong to say that data is the electricity that powers all the industries of today. Industries make use of data to improve their performance, generate revenue, and provide better customer service. Infact, the automobile industry, too, is harnessing the power of data to improve the safety of their vehicles. Their goal is to create powerful machines that think in the form of data.  2. Demand And Supply Although there is a huge abundance of data, there are not enough resources available that can convert this data into powerful products. This basically means that there is still a huge dent in the data scientists, because of a lack of data literacy in the market.  3. High Paying Job Opportunities Currently, data science is considered a highly lucrative career. Infact, according to some researchers, a data scientist makes 63% more than the national average salary. Apart from this, data scientists also get to enjoy a position of prestige in the company. This is because companies rely heavily on data scientists to make data-driven decisions and guide the organization in the right direction.  4. Data Science is the next big thing As more and more industries are becoming data-driven, there is a constant need for data scientists. The field of technology is becoming more dynamic and new innovations are being made every day. Thus, data science is the career of the future.  Here are 50 Data Science Project ideas for you, and in the blog ahead, we are discussing a few of these projects in detail. So let’s begin! Chatbot Analyzing the impact of climate change on global food supply Weather Prediction Keyword generation for google ads Traffic Signs Recognition Wine Quality Analysis Stock Market Prediction Fake News Detection Video Classification Human Action Recognition Medical Report Generation using CT Scans Email Classification Uber Data Analysis Sound Classification Credit Card Fraud Detection Sign Language Recognition Class of Flower Prediction Colour Detection Loan Prediction Road Traffic Prediction Income Classification Speech Emotion Recognition Celebrity Voice Prediction Store Sales Prediction Detecting Parkinson’s Disease Air Pollution Prediction Age and Gender Detection Optimizing Product Price  IMDB Predictions Handwritten Digit Recognition Quora Insincere Questions Classification Driver Drowsiness Detection  Web Traffic Time Series Forecasting Survival Prediction on the Titanic Time Series Modelling Image Caption Generator Insurance Purchase Prediction Crime Analysis Customer Segmentation Taxi Trip Time Prediction Job Recommendation System Boston Housing Predictions Sentiment Analysis Interest Level in Rental Properties Keyword generation for Google Ads Breast Cancer Classification Employee Computer Access Needs Tweets Classification Movie Recommendation System Product Price Suggestions Also, check out our business analytics course to widen your horizon. Best Data Science Project Ideas We have segmented all the Data Science Project Ideas with source code as per the learner’s level. Therefore, you will get a list of a few amazing project briefs for beginner, intermediate & advanced Data Science project ideas. Our learners also read: Free excel courses! 1. Beginner Level | Data Science Project Ideas This list of data science project ideas for college students is suited for beginners, and those just starting out with Python or Data Science in general. These data science project ideas will get you going with all the practicalities you need to succeed in your career as a data science developer. Must read: Data structures and algorithms free course! Further, if you’re looking for data science project ideas for final year, this list should get you going. So, without further ado, let’s jump straight into some data science project ideas that will strengthen your base and allow you to climb up the ladder. 1.1 Climate Change Impacts on the Global Food Supply The first one to make it to the list of data science projects for beginners is climate change impacts on the global food supply. Frequent Climate change and irregularities are big challenging environmental issues. These irregularities in climate divisions are drastically affecting the human lives residing on the Earth. This Data Science Project concentrates on how the climate impact will highly affect global food production worldwide and how much quantification will impact climate change.   The main aim of development for this project is to calculate the potentialities on the staple crop productions due to climate change. Through this project, all the implications related to temperatures & precipitation change. It will then be taken into account how much carbon dioxide affects the growth of plants and the uncertainties happening in the climatic conditioning. Hence, this project will largely deal with Data Visualisations. It will also compare the production in various regions at different time zones.  Also, visit upGrad’s Degree Counselling page for all undergraduate and postgraduate programs. upGrad’s Exclusive Data Science Webinar for you – How to Build Digital & Data Mindset document.createElement('video'); https://cdn.upgrad.com/blog/webinar-on-building-digital-and-data-mindset.mp4 1.2 Fake News Detection Source You can drive your Data Science career with this amazing Data Science Project idea for beginners – Detection of Fake News using Python language. The act of wrong or misleading journalism on a digital platform or fake news can be detected by this project. Falsifications are spreading out via social media platforms and online channels & digital media to attain any political agenda.  With this data science project idea, you can use Python language to develop a specific model that can precisely detect whether the news is real journalism or false information.. For this, you need to build a ‘TfidfVectorizer’ classifier and then use a ‘PassiveAggressiveClassifier’ to classify the news into either a “Real” and “Fake” segmentations. There will be a dataset of the shape of 7796×4 dimensions and execute all these in the ‘JupyterLab’. The main idea of this Data Science project is to develop a real-time machine learning model that can correctly detect social media news authenticity. ‘TF’, commonly known as ‘Term Frequency’, is the total number of times any word will appear in a single document. Whereas, ‘IDF’ or ‘Inverse Document Frequency’ is a calculative measure of the value of a word & it is based on the reputational frequency of its occurrence appearing in the various documents.   The theory is on the ‘Common words’, if these common words happen to appear in multiple documents with a high frequency then they are considered as less important words. So, what ‘TFIDFVectorizer’ does is to analyze the collection of these documents and then accordingly create a ‘TF-IDF’ matrix to it.  Along with this, a ‘PassiveAggressive’ classifier will remain ‘passive’ in case the ‘classification outcome’ is correct; but on the other hand, it will change aggressively if the ‘classification outcome’ is incorrect. So, you can create a machine learning model to detect social media news to be genuine or fake news using this Data Science Project idea. Source Code: Fake News Detection Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses 1.3 Human Action Recognition This is a Data Science project on the human action recognition model. It will look at the short videos made on human beings where they are performing specific actions. This model tries to do a classification that is based on actions performed. In this Data science project, you need to use a complex neural network. This neural network is then trained on a specific dataset that contains these short videos. Then there is an accelerometer data that is associated with the dataset. The accelerometer data conversion is done first along with a ‘time-sliced’ representation. Thereafter, you have to use the ‘Keras’ library so that you can do training, validation, and testing of the network based on these datasets. Source Code: Human Action Recognition 1.4 Forest Fire Prediction One of the alarming & common disasters happening in today’s world is forest fires. These disasters are highly damaging to the ecosystem. To deal with such a disaster, a lot of money on infrastructure & controlling and handling is required. We can build a Data Science project using ‘k-means clustering’- it can identify any forest fires hotspots along with the severity of the fire at that particular spot. It can be alternatively used for better resource allocation with the faster response time. Hence, using the meteorological data such as those seasons around which these kinds of fires tragedies are more likely to happen and various weather conditions that worsen them may increase these results’ accuracy levels. Source Code: Forest Fire Prediction 1.5 Road Lane Line Detection Another Data Science project ideas for beginners include a Live Lane-Line Detection Systems built-in Python language. In this project, a human driver receives guidance on lane detections through lines drawn on the road. Not only this, it further refers to which direction the driver should steer their vehicle. This Data Science Project application is vital for the development of driverless cars. Hence, you can also develop an application with the powerful capability to identify a track line through the input images or via a continuous video frame. Source Code: Road Lane Line Detection Read: Top 4 Data Analytics Project Ideas: Beginner to Expert Level 2. Data Science Projects Ideas |Intermediate Level 2.1 Recognition of Speech Emotion  Source One of the popular Data Science project ideas is recognition of the speech emotion. If you want to learn the usage of different libraries, this project is perfect for you. You must have seen a lot of editor tools that can tell us how our speech emotion is appearing. This program model can be built as a Data Science project. In this Data Science project, we will use ‘librosa’ that will perform a ‘Speech Emotion Recognition’ for us. The SER process is a trial process that can recognize human emotion. It can also recognise the speech from the affective states. As we use a combination of a tone and a pitch for expressing emotions through our voice. The Speech Emotion Recognition model is absolutely possible. However, it can be a challenging project to perform as human emotions are very subjective. The annotation of the human audio is also quite challenging. So, here you will use the mfcc, mel & the chroma features. With this, you will also use the dataset known as ‘RAVDESS’ for the emotion recognition process. In this Data Science project, you will also learn how to develop an ‘MLPClassifier’ for this model. Source Code: Recognition of Speech Emotion 2.2 Gender and Age Detection with Data Science Source So, one of the impressive project ideas on Data Science is the ‘Gender and Age Detection with OpenCV’. With this kind of real-time project, you can easily grab your recruiter’s attention in a Data Science interview. Talking about the project, the ‘Gender and Age Detection’ is a machine learning project based on computer visioning. Through this Data Science Project, you can learn the practical application of CNN i.e, the convolutional neural networks. Down the line, you will also use models that are trained by ‘Tal Hassner’ and ‘Gil Levi’ for ‘Adience’ dataset. Along with this, you will also use some files such as – .pb, .prototxt, .pbtxt, & .caffemodel files. Heard about these terms? Read about these files? Understand models too? But do you know how to implement them? Well, you can learn it if you opt to develop a Data Science Project on it.  It’s a very practical project as you will create a model that can detect any human being’s age & gender through analyses of single face detection via an image. So, with this gender classification in a man or a woman can be classified. Also, the age can be classified among the ranges of 0-2/ 4-6/ 8- 2/ 15-20/ 25-32/ 38-43/ 48-53/ 60-100.  But due to various factors such as makeup, or brighter dim lighting, or an unusual facial expression, the recognition of the gender and the age from a single source can become challenging. Therefore, in this Data Science project, you will use a classification model instead of a regression model. A lot of practical & technical learning can be grabbed to upscale your technical skills with these kinds of projects. So, take up the challenge & work hard towards it to make an impressive Data Science Resume. Source Code: Gender and Age Detection with Data Science Top Data Science Skills to Learn Top Data Science Skills to Learn 1 Data Analysis Course Inferential Statistics Courses 2 Hypothesis Testing Programs Logistic Regression Courses 3 Linear Regression Courses Linear Algebra for Analysis 2.3 Driver Drowsiness Detection in Python An excellent Data Science project idea for intermediate levels is the ‘Keras & OpenCV Drowsiness Detection System’. Driving overnight is not only tough but a risky job too. We have heard of a lot of cases where accidents happen because the driver fell asleep while driving. Thus, this project can help prevent numerous road accidents that happen due to such cases. This project’s main aim is to recognize whenever the driver may get drowsy & fall asleep while driving. This project uses Python language where you can build a model that can timely detect the sleepy driver behavior and raises an alert alarm through a high beeping alarm. In this project, you can implement a ‘deep learning model’ & with its use, you can do a classification among images where a human eye is open or close. Not just this, in this model another formula line is to calculate the score. This score is based on the time period of how long the eyes remain closed. The score is maintained throughout the driving session. If that score increases & crosses a specified threshold, this model will throw workflow automation through which the alarm will start buzzing heavily. So, with these kinds of Data Science projects implementations, you will learn all the basics of Data Science projects. You will implement it using ‘Keras’ and ‘OpenCV’. So, why are these used? Well, you are using ‘OpenCV’ to detect face & eye movements. Whereas, with ‘Keras’, you can classify the eye’s state whether it is open or close while using techniques of the Deep neural network. Source Code: Driver Drowsiness Detection in Python Data Science Advanced Certification, 250+ Hiring Partners, 300+ Hours of Learning, 0% EMI 2.4 Chatbots  Source Chatbots are increasingly becoming popular these days. So, for a Data Science project, it is a high on-demand requirement by almost all organizations. It is an essential segment of the business nowadays. These days, chatbots are playing a very crucial role in businesses. They are helping business lines to save an enormous amount of time on their human resources. It is used to provide an improved and personalized business service simultaneously. There are many businesses who are offering services to their customers. To provide customer service on a large scale, it requires a lot of human resources, ample time, and many efforts to handle each customer on time. On the other hand, these chatbots can provide automation for customer interaction services simply by answering a set of frequent questions commonly inquired by the customers.  There are 2 types of chatbots available in today’s time: Domain-specific chatbot and Open-domain chatbot. The domain-specific chatbot is most often used for a particular problem solution. These are customized in a very strategic & smart manner so that they work strategically & effectively in relation to domain specifications. The second one, ‘Open-domain’ chatbots, needs a lot of training materials that are too continuously because, as per the name, it is developed to answer any kind of question. Technically speaking, the chatbots are trained using the ‘Deep Learning’ techniques. They need a dataset with vocabulary listing, lists consisting of a common sentence, an intent which is behind them, and then the appropriate responses. This is one of the trending data science project ideas.  The ‘Recurring Neural Networks’ (The RNN’s) are the common methodologies to train chatbots. These bots contain encoders that can update the states as per the input sentences alongside intent. It then passes the specified state to the Chatbot. Thereafter, the chatbot uses the decoder to search an appropriate & subsequent response according to inputted words & also besides the intent. With this Data Science project, you can easily learn Python language implementation as the complete project is itself made in Python. You can upscale your Python technical skills to a certain extent. Learn: How to Make a Chatbot in Python Step By Step 2.5 Handwritten Digit & Character Recognition Project Source With this Data Science Project idea on ‘Handwritten Digit & Character Recognition with the help of CNN, you will practically learn Deep Learning concepts. So, if you are a budding Data Scientist or an enthusiast of machine learning then this is the perfect Data Science project idea for you. For this project development, you will use the ‘MNIST dataset’ of hand-written digits. This is a great project to get hands-on experience with Data Science as you will learn amazing ways that are involved in the process of project building.  As discussed, this project is implemented through the ‘Convolutional Neural Networks’. After this, for a real-time prediction, you will build a creative graphical- based user interface for drawing digits on the canvas, and thereafter you will build a model that will be used for the prediction of the digits. The project’s focus is on developing the computer’s ability & to empower the computer system so that it can recognize characters in hand-written formats by humans. It will then evaluate it further to understand it with reasonable accuracy. With this project implementation, you can learn the practical implementation of the ‘Keras’ and also ‘Tkinter’ libraries. These are some intermediate data science project ideas on which you can work. If you still like to test your knowledge and take on some tough projects. Source Code: Handwritten Digit & Character Recognition Project 3. Advance Level Data Science Projects Ideas 3.1 Credit Card Fraud Detection Project Source After implementing easy projects, you can now move to some advanced Data Science project ideas to learn more concepts. One such idea is Credit card Fraud Detection. With this project, you will learn how to use the R with different algorithms such as Decision Tree, Artificial Neural Networks, Logistic Regression, and the Gradient Boosting Classifier. You can also learn to use the ‘Card Transactions’ datasets to classify the credit card transaction as a fraudulent activity or a genuine transaction. You will also learn to fit all the different types of models along with the plot performance curve for all of them. This is one of the best data science project ideas one can find.  Source Code: Credit Card Fraud Detection Project 3.2 Customer Segmentations Source This is one of the most popular Data Science projects in the field of Data Science. Digital Marketing is an up & advanced way to target an audience for the companies through their online marketing activities for marketing purposes nowadays. So before running a marketing campaign, different customer segmentation is first done. Customer Segmentation is among very popular applications of indeed unsupervised learning. Hereby, using clustering methods, companies can now easily identify the customers’ various segments for targeting the potential user-base. There are divisions made on customers & groups are formed according to the common characteristics such as gender, interest areas, age, and habits. Based on these details they can effectively market each customer group. The project uses the ‘K-means clustering’ and you will learn how to perform visualizations on distributions such as gender and age. Customers annual incomes & average score values can also be analysed. Source Code: Customer Segmentations 3.3 Traffic Signs Recognition Source  This project aims to develop a model to achieve high accuracy in self-driving car technologies using CNN techniques. Traffic signs and traffic rules are of utmost importance for every driver and it must be followed to avoid accidents. To follow these rules, the user must understand how the traffic signals appear to be.  It’s a general rule that to obtain a driving license, an individual has to learn all the driving signals. But for autonomous vehicles, there are programs developed such as the ‘Traffic signs recognition’ using CNN, where you can learn how to program a model that can precisely identify various kinds of traffic signals by the input of an image. There is a dataset called the ‘German Traffic signs recognition benchmark’. It is commonly known as the GTSRB that is used in the development of a Deep Neural Network for recognizing the class of all the traffic signs belonging to which class type. You will also learn practical knowledge of building a GUI for application interaction. Source Code: Traffic Signs Recognition Know more: 10 Exciting Python GUI Projects & Topics For Beginners To find a data science project, consider identifying a problem or question that interests you, locate relevant datasets, and leverage various tools and techniques to analyze the data and derive insights. Online platforms like Kaggle, data repositories, or collaborating with organizations can offer opportunities to work on real-world projects. Top Data Analytics Projects Now that you have learned some of the best data science project topics, let’s take a look at some of the top data analytics projects ideas and data science topics that are currently trending in the market. Data analytics projects span a wide range of industries and applications, each with its unique challenges and insights. Here are some top data science projects for beginners that showcase the diversity and impact of data analysis: Customer Segmentation for E-commerce: Analyze customer behavior and purchasing patterns to segment customers based on preferences, demographics, and buying habits. This segmentation can help tailor marketing strategies, improve product recommendations, and enhance customer experiences. Predictive Maintenance in Manufacturing: Utilize sensor data from machinery to predict maintenance needs and prevent unplanned downtime. This can optimize maintenance schedules, reduce costs, and enhance production efficiency. Healthcare Fraud Detection: Analyze medical claims data to identify patterns indicative of fraudulent activities. Building predictive models can help healthcare providers and insurers detect fraudulent claims and mitigate financial losses. Energy Consumption Optimization: Analyze energy usage patterns in buildings to identify opportunities for energy efficiency improvements. This can lead to reduced energy costs and a smaller carbon footprint. Financial Portfolio Optimization: Analyze historical financial data to optimize investment portfolios. Applying techniques like Modern Portfolio Theory can help investors balance risk and return. Traffic Pattern Analysis: Analyze traffic data to understand congestion patterns, optimize traffic flow, and improve urban planning for transportation infrastructure. Predicting Disease Outbreaks: Analyze health data and historical disease outbreaks to build predictive models that can forecast and mitigate the spread of diseases. Real Estate Market Analysis: Analyze real estate data to identify trends, forecast property values, and assist buyers, sellers, and investors in making informed decisions. 1. Web Scraping Knowing how to scrape data not only adds that boost to your portfolio, but also with the help of this, you can actually explore and use data sets that match with your interests, without the need for compiling the same. Various tools like Beautiful Soup or Scrapy are actually available with the help of which you can crawl the web for interesting data.  Source Code: Web Scraping 2. Data Cleaning One of the most important tasks for every data analyst is cleaning data to make it ready to analyze. Data cleaning, also called data scrubbing is basically ensuring that the data is consistent, by removing any duplicate or incorrect data and managing the holes in the data. This is one of the best data science topics that is boun dto add value to your candidature.  Source Code: Data Cleaning 3. Exploratory Data Analysis To put it simply, data analysis is all about answering questions with data. With the help of EDA, you can explore different questions that you want to ask.  Source Code: Exploratory Data Analysis 4. Sentiment Analysis Last but not least is sentiment analysis, which is basically a technique in natural language processing that determines whether the data is neutral, positive, or negative. They are especially useful for public review sites and social media platforms. Furthermore, with the help of sentiment analysis, you can also detect a particular emotion based on the list of words, and their corresponding emotions. This is known as a lexicon.  Source Code: Sentiment Analysis Data analytics projects for final-year students Here are some data science project ideas for final year students: Predictive Modeling for Student Performance: Analyze historical academic data to predict student performance based on various factors like attendance, study habits, socioeconomic background, etc. Customer Segmentation for E-commerce: Cluster customers based on their purchasing behavior and demographics to provide targeted marketing strategies. Movie Recommendation System: Build a recommendation system that suggests movies to users based on their viewing history and preferences. Healthcare Analytics: Analyze patient records to identify trends, predict disease outbreaks, or assess the impact of different treatments. Social Media Sentiment Analysis: Analyze sentiment on social media platforms regarding a specific topic, brand, or event. Predicting Stock Prices: Use historical stock data to build a model that predicts future stock prices. Energy Consumption Analysis: Analyze energy consumption patterns in a specific region and suggest strategies for more efficient energy use. Crime Pattern Analysis: Analyze crime data to identify patterns and trends in criminal activities for better resource allocation in law enforcement. Sports Analytics: Analyze player performance, team strategies, and historical game data to gain insights into sports dynamics. Real Estate Market Analysis: Analyze housing market data to predict property values, identify investment opportunities, or understand market trends.  Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? Bottom Line In this article, we have covered top data science project ideas. We started with some beginner projects which you can solve with ease. Once you finish with these simple data science projects, I suggest you go back, learn a few more concepts and then try the intermediate projects. When you feel confident, you can then tackle the advanced projects. If you wish to improve your data science skills, you need to get your hands on these data science project ideas. Now go ahead and put to test all the knowledge that you’ve gathered through our data science project ideas guide to build your very own data science project! We wish that you will drastically improve all the skills of Data Science with the project ideas we presented to you here in this blog. But in case you are new to the Data Science field & would love to learn the Data Science & build similar models for the technological advancements, we recommend you to check out the online course on upGrad & IIIT-B’s PG Diploma programs to learn & upskill in the Data Science world with experienced & expert professionals. With the right set of knowledge, guidance & tools, you can learn any Data Science project. No level is difficult for learners. That’s why all these live projects are a perfect way to enhance one’s skills and fast progress in attaining mastery. At upGrad, we offer 3 Data Science Online Certification: 1. Executive PG Programme in Data Science (12 months) From IIIT Bangalore 2. Master of Science in Data Science (18 months) From Liverpool John Moores University 3. Advanced Certificate Programme in Data Science (7 months) From IIIT Bangalore Try these Data science online certifications by upGrad as we are sure that they will help you in your Data Science career path. Therefore, don’t delay! Start your practice now!

by Rohit Sharma

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16 May 2024

Binary Tree vs Binary Search Tree: Difference Between Binary Tree and Binary Search Tree
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Binary Tree vs Binary Search Tree: Difference Between Binary Tree and Binary Search Tree

Introduction Sorting is the process of arranging the data in a systematic order so that it can be analysed more effectively. The process of identifying a particular record is called searching. If the data is properly sorted in a particular order, then searching becomes an easy and efficient task. This article deals with one of the most important non-linear data structures, i.e., trees. Trees are primarily used to represent data by demonstrating a hierarchical relationship between the elements. For example, table of contents, family tree. Technically, a tree may be defined as a finite set ‘T’ of one or more nodes such that a node is assigned as the root of the tree and the other nodes are divided into n>=0 disjoint sets T1, T2, T3, T4 …. Tn and are called as the subtrees or children of that root node. Now that we have discussed what trees essentially are, let us delve into binary search tree and binary tree. They play crucial computational roles in software development. Binary trees permit nodes to have a maximum of two children, offering versatility in node arrangements without inherent ordering. However, a binary search tree (BST) is a specialized tree enforcing a specific order. This structural distinction between binary tree and binary search tree greatly impacts functionality. BSTs excel in efficient search, insertion, and deletion due to their ordered layout, whereas binary trees offer more flexibility in organizing data hierarchies.  Recognizing the difference between binary search tree and binary tree is vital for optimizing their use cases. Learn in detail about the difference between BST and BT in this blog. What is a Binary Tree? A binary tree is a non-linear data structure wherein a node can have either 0, 1 or 2 nodes. Each node in the binary tree is termed as either a parent node or as a child node. The topmost node of the Binary Tree is referred to as the root node. Each parent node can have at most 2 child nodes which are the left child node and the right child node. Binary trees can look different from one another. A balanced binary tree, also known as an AVL tree in Java, is well-proportioned, where the difference in the heights of the left and right parts of each node is small. The balanced structure os an AVL tree in data structure helps with faster searches and organized data. However, an unbalanced binary tree can be skewered, causing slower operations like searching and adding new data.  A node in a binary tree has three fields: Each node comprises three essential fields that define its structure and connections within the tree. Data Element – It stores the data value that is to be stored by the node.  It could represent any information the tree is designed to hold, such as numbers, strings, objects, or other types of data. Pointer to the left child – It stores the reference (or address) to the left-child node.  In computer memory, this pointer enables the node to link itself to its left child node. If a node doesn’t have a left child, this pointer typically holds a null value, indicating the absence of a left child. Pointer to the right child – It store the reference to the right-child node.  It helps establish the connection between the current node and its right child node. Like the left child pointer, if a node lacks a right child, this pointer usually holds a null value, signifying no right child node. These pointers or references are fundamental in constructing the hierarchical structure of a binary tree. They establish relationships between parent nodes and their respective child nodes, enabling navigation through the tree from one node to another. In this way, several nodes are combined together to build a Binary Tree. These pointers connect nodes based on their relationships (parent-child), helping organize a binary tree and implement traversal algorithms (like in-order, pre-order, or post-order traversal). This allows efficient access, manipulation, and management of data within the tree structure. A Binary Tree can be represented as: Source From the above figure, the root node 2 has two children (or child nodes), 7 and 5. 7 is referred to as the left-child node and 5 is called as the right-child node. In this way, each of the child nodes act as a parent node to several other nodes and together represent the Binary Tree. Check out: Types of Binary Tree Terminologies used in Binary Tree Understanding the terminologies used in a binary tree is fundamental to better grasp its structure and operations. Here’s a detailed list of these terms with their explanations: Nodes are fundamental units within a tree data structure, representing individual elements or data points. Each node contains data (such as a value or an object) and pointers or references to its child nodes, if any. Nodes are interconnected through edges to form the hierarchical structure of the tree. Root Node: The topmost node of a Binary Tree.  The root node serves as the starting or entry point for accessing and traversing the entire tree structure. All other nodes are descendants of the root node, and its depth is always 0 as it is the starting point of the tree. Parent Node: If a node is connected to another node through edges, it is known as a parent node. In a binary tree, a parent node can have a maximum of 2 child-nodes.  Child Node: If a node has a predecessor, it is known as child node.  Each parent node can have up to two child nodes, one positioned to its left and the other to its right in a binary tree. Leaf Node: A node that does not have any child node is called as a leaf node.  They exist at the lowest level of the tree hierarchy and represent endpoints or final elements within the tree structure. Depth of a node: It is the distance from the root node to that particular node whose depth is to be measured.  Depth signifies the number of edges traversed along the path from the root node to the target node. Depth of a node: It is the distance from the root node to that particular node whose depth is to be measured. Height of the tree: It is the longest distance from the root node to the leaf node. These are a few basic terminologies of a Binary Tree. With this basic understanding of a Binary Tree, let us move on to an advancement of Binary Tree known as the Binary Search Tree. Understanding these terminologies provides a foundational understanding of the relationships between nodes and their positions within a binary tree. These concepts are essential in performing operations within the tree structure, such as traversal, searching, insertion, and deletion. Also Read: Binary Search Algorithm: Function, Benefits, Time & Space Complexity What is a Binary Search Tree  As the name suggests, a Binary Search Tree or BST is a tree that is used in sorting, retrieving and searching data. It is also a type of non-linear data structure in which the nodes are arranged in a particular order. Hence, it is also called as “Ordered Binary Tree”. It has the following properties: Additionally, a crucial part of this tree structure is its ‘balanced’ nature, ensuring an even distribution of nodes. This enables efficient operations such as search, insertion, and deletion with time complexities typically near O(log n).  A balanced binary search tree (BST) is a structured data representation that follows a hierarchical format, where each node can have a maximum of two children. In this tree, the left child of a node holds values smaller than the node, while the right child holds values greater than the node.  All in all, a binary search tree is extensively used because it facilitates operational efficiency which aids in the ease of data management. It has the following properties: The left subtree of a node has nodes which are only lesser than that node’s key. This property ensures that all nodes to the left of a parent node have values smaller than the parent node’s value, establishing a sorted order within the left subtree. The right subtree of a node has nodes which are only greater than that node’s key. This property guarantees that all nodes to the right of a parent node have values larger than the parent node’s value, maintaining a sorted order within the right subtree. Each node has distinct keys and duplicates are not allowed in Binary Search Tree. This ensures that no two nodes within the tree have the same key. This uniqueness facilitates unambiguous searching and retrieval of specific values within the tree.  The left and right subtree must also be a binary search tree. Another critical property of a BST is that both the left and right subtrees of any node in the tree must also qualify as binary search trees. This recursive property ensures that the BST structure applies not only to the root node but to every subtree within the entire tree. Every sub-tree abides by the rules of a binary search tree, with its own left and right subtrees following the criteria of lesser and greater values, respectively. The properties of a binary search tree (BST) make it ideal for organizing data. The left side of a node has smaller values, the right side has larger values, and no duplicates are allowed. Each part of the tree, whether left or right, follows these rules, too. Because of this arrangement, finding, adding, and removing items in a BST is faster and easier. These properties ensure a balanced and ordered arrangement of elements, enhancing the speed and efficiency of various operations performed on the tree. Must read: Learn excel online free! Let us visualize this concept to get a clear understanding of Binary Search Trees. Source In the above figure, we see that the value of the root node is 8. With further scrutiny, it is observed that all the values in the left subtree are lesser than the value of the root node and all the values in the right subtree have values that are greater than the root node. Furthermore, it is noted that each value in the Binary Search Tree is unique and there are no duplicates. Thus, the properties of Binary Search Tree stated above are verified. Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses In yet another example, we see that though the left and right subtrees are binary search trees with unique values throughout the tree. The value at the leaf node in the left subtree is 12 which is greater than the root node value which is 12. Thus, this does not satisfy the property of the BST and hence, it is not a Binary Search Tree. upGrad’s Exclusive Data Science Webinar for you – How to Build Digital & Data Mindset document.createElement('video'); https://cdn.upgrad.com/blog/webinar-on-building-digital-and-data-mindset.mp4   Our learners also read: Free Python Course with Certification Top Data Science Skills to Learn Top Data Science Skills to Learn 1 Data Analysis Course Inferential Statistics Courses 2 Hypothesis Testing Programs Logistic Regression Courses 3 Linear Regression Courses Linear Algebra for Analysis Search operation in a BST –  Consider a Binary Search Tree with the values given below. Let us understand how the search operation is performed to get 9 from the Binary Search Tree. Source In order to perform the binary search, we shall initially arrange all the integers in a sorted array. This is called as the search space. This search space shall consist of two pointers, called as the start and end pointers. The array of the above given Binary Search Tree is represented as: The first step is to calculate the middle value of the array and compare it with the value that is to be searched, 9 in our case. This is done by calculating n/2, where n is the total number of elements in the array (BST) and it is 6. Thus, the 3rd element is the middle element which is 5.  Now that the middle element is compared with 9 and as it is not equal (greater), the searching operation will be performed on the right array. In this way, the search operation is reduced to half, as shown below: In the next step, the middle element is calculated and is found to be 9, which matches our element to be searched. Binary Tree Vs. Binary Search Tree: Key Differences A binary tree and a binary search tree differ mainly in how they organize their elements. In a binary tree, there’s no specific rule for arranging nodes. Each node can have at most two children, but there’s no particular order in which the elements are placed.  This lack of ordering means that when you’re looking for something in a binary tree, you might have to check many nodes before finding what you need, which can take searching longer. On the other hand, a binary search tree (BST) is all about order. It has a specific arrangement where the left child of a node holds values smaller than the node, and the right child holds values greater than the node. This organized structure makes searching much faster.  For example, if you’re searching for a value, you can quickly decide whether to go left or right based on the comparison with the current node. This ordered arrangement significantly reduces the time it takes to find elements, making operations like search, insertion, and deletion faster and more efficient. Because of these key distinctions between binary search tree vs binary tree, BSTs are commonly used in scenarios where quick retrieval based on an ordered arrangement is crucial, like in databases or symbol tables. They are excellent for tasks where finding specific values rapidly is essential.  That being said, binary trees offer more flexibility and are used when ordered data is not the primary concern, such as in expression trees or file systems. Therefore, the core distinction between BST vs. binary tree lies in their organization method – one follows a specific order, while the other doesn’t – impacting how efficiently you can locate items within them. Binary Tree vs Binary Search Tree – Table of Differences Now that we have a basic understanding of both the Binary Tree and Binary Search Trees, let us quickly summarize some of the differences between both of them. Basis for Comparison Binary Tree Binary Search Tree Definition A Binary Tree is a non-linear data structure in which a node can have 0, 1 or 2 nodes. Individually, each node consists of a left pointer, right pointer and data element.  A Binary Search Tree is an organized binary tree with a structured organization of nodes. Each subtree must also be of that particular structure. Structure There is no required organization structure of the nodes in the tree. The values of left subtree of a particular node should be lesser than that node and the right subtree values should be greater. Operations Performed The operations that can be performed are deletion, insertion and traversal As these are sorted binary trees, they are used for fast and efficient binary search, insertion and deletion. Types There are several types. Most common ones are the Complete Binary Tree, Full Binary Tree, Extended Binary Tree The most popular ones are AVL Trees, Splay Trees, Tango Trees, T-Trees. Conclusion Thus, we infer that though both the Binary Tree and Binary Search Tree have a common hierarchical structure with a collection of nodes, they have several differences in their application. A Binary Tree is a basic structure with a simple rule that no parent must have more than 2 children whereas the Binary Search Tree is a variant of the binary tree following a particular order with which the nodes should be organized. Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? The main binary search tree and binary tree difference boils down to their organization rules. A binary tree doesn’t follow a specific order, allowing nodes to have up to two children and offering flexibility. In contrast, a binary search tree arranges nodes so smaller values are on the left and larger ones on the right, making searching faster.  Binary search trees work well for organized data tasks like in databases, while binary trees are more flexible for various data representation needs. Understanding these differences helps choose the right tree structure based on specific requirements. If you are curious to learn about Binary tree vs Binary search tree, check out IIIT-B & upGrad’s PG Diploma in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms. 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by Rohit Sharma

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16 May 2024

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