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Top 10 Real-Time SQL Project Ideas: For Beginners & Advanced
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Thanks to the big data revolution, the modern business world collects and analyzes millions of bytes of data every day. However, regardless of the business sector or industrial field, the key to making data-driven decisions requires proper skills and tools that ease data collection, storage, and analysis. In other words, if a business wants to make strategic decisions that solve real-life problems while boosting profits, it must leverage data science skills, one of them being SQL.  But before we dive into SQL projects with real-life applications, let’s take a quick look at the basic concept behind SQL. Let’s Examine Some of SQL’s Most Desirable Qualities From an Employer’s Perspective: OLAP refers to a category of database applications that facilitates speedy data analysis with the use of a two-dimensional spreadsheet. This knowledge is crucial if you plan on using SQL in a professional setting. Data may be gathered to better understand a company’s needs.   Any competent SQL developer should also be fluent in PHP. Because of how well it works, interacting with SQL databases like MySQL will become much simpler. It’s what you use when you need to make a website.  Capability with database indexing. Database indexes make querying significantly faster for technical professionals. These indexes also make it simpler for a query to zero down on the desired details. Having a deep understanding of indexes will make you a more proficient SQL user and program architect. The ability to do joins makes it simple to combine information from many tables. It also becomes much simpler to look at datasets coming from multiple places. Left, right, inner, and outer joins are the four most common forms of joins. You may improve your SQL abilities by learning when to use each. You can use a subquery in a query or a query in a statement. They serve largely as a means of linking information found in separate tables. This ability is essential for SQL experts since it speeds up data retrieval. What is SQL? SQL stands for Structured Query Language, a standard database language used for creating, maintaining, and retrieving relational databases. All Relational Database Management Systems (RDMS) such as MS Access, MySQL, SQL Server, Sybase, Oracle, Postgres, and Informix use SQL as their established database language.  Since its inception in the 1970s, SQL has become an essential resource in every data scientist’s toolkit. It allows data scientists to access, update, insert, manipulate, and modify data and facilitates communication with relational databases to properly understand datasets. Today, SQL finds application in most web applications and websites and is undoubtedly a critical skill that all budding data science professionals should master.  Why SQL Projects Matter? The value of structured query language (SQL) in modern data analysis is immeasurable. The capacity to effectively sort through massive amounts of data using a potent tool like SQL has become essential for making well-informed decisions in the age of big data. The uses of SQL go well beyond data retrieval. Modifying databases and organizing large data sets are only two examples of how SQL helps turn raw data into useful insights. Because of this, SQL is now a crucial tool for analyzing data. Because of SQL’s widespread availability, analysts may easily query, alter, and visualize data, which in turn reveals patterns and trends that can inform important business decisions. In addition, SQL’s value extends to other areas of the corporate world. SQL is ubiquitous in fields as diverse as banking and healthcare due to its scalability and portability across a wide variety of database management systems. By automating data administration and increasing data accessibility, SQL may make corporate processes more straightforward. Businesses may more effectively manage massive data stores with the help of SQL, which in turn improves their ability to make decisions and formulate strategies. However, this recognition of SQL’s significance is merely the beginning. The difference between a competent SQL user and a rookie lies in their hands-on project experience. By working on SQL projects, you may learn how SQL queries work in practice and how to address difficult business issues. By working on these projects, you may practice what you’ve learned about SQL, cement your comprehension, and build the self-assurance to take on more challenging data analysis assignments. Your portfolio will represent your ability to apply SQL in a business setting and produce outcomes, giving you an advantage in the employment market. Therefore, working on SQL projects is a good way to educate yourself and advance your career. It gives you the knowledge and expertise you need to succeed in any data-centric job, setting you up for a prosperous career. Top 10 Real-Time SQL Projects for Practice Theory on one side, the real test of understanding is when you can successfully design and build a database with real-life applications. A great way to hone your conceptual knowledge of SQL is to implement real-time SQL projects. So, here are some exciting SQL projects for practice that will help you boost your confidence and problem-solving skills in relational databases. 1. Student Database Management A simple project for beginners, creating a student database management system, is to keep a record of students’ general information. An automated student database system streamlines the admission process and subsequent student information management of an educational institution to a considerable degree. The project should handle general student information such as name, address, contact details, year of admission, course admitted in, etc., and other essential particulars like results, attendance, scholarships, fees, etc. 2. Centralized College/University Database A college or university has several academic departments. Now, each department offers varied courses, and a professor can teach more than one course. For example, a professor that takes a class on poetry may also teach literary criticism. As a student of English, you can enrol in both of these courses, and as such, a particular course within a department can have any number of students. The purpose of a centralized college/university database is to manage data related to the departmental courses, students enrolled in them, the instructors involved, etc.  3. Railway System Database A railway system database is for the record-keeping and management of railway data such as trail details, schedules, train routes, passenger booking information, railway stations, etc. To make this SQL project more simplified, you can assume that all trains run regularly and undertake a one-day journey to their respective destinations. Hence, you can use this system database for recording various details for each station in a rail route, such as the time of arrival and departure of trains and the sequential number of the station. 4. e-Ticket Booking Suppose you want to create a database for railway e-ticket booking that allows customers to book tickets over the internet. Many factors are involved in ticket reservation, such as train details, station details, route details, passenger details, booking details, etc. While designing the project, keep in mind that once a seat is booked, it should be blocked so that no other customer can book it again. Also, if a user is already booking, another user cannot access the same. A similar idea can be applied to flight and bus ticket bookings as well. 5. Library Management System A classic SQL project idea, a library management system is a database designed to issue books and let users browse various books under different titles and genres. It is one of the most fundamental SQL projects for beginners and uses Asp.Net C#, Visual Studio C#, and a local database. Typically, a Library Management System has an entry for every book issued, its unique ID, who issued it, the issue duration, any fine, etc. 6. Hospital Management Database A hospital management system is a software or web-based system for managing the day-to-day functioning of a hospital or any medical set up. It is a systematic and reliable record of patients, doctors, rooms, duties of staff, patients’ discharge summary, and other details which the administrator can control. In such a database, all persons have a unique ID linked with their roles and duties. In addition, it gives access to the necessary details such as the availability of doctors and nurses, vacant rooms for patients, etc. 7. Online Retail Application Database With e-commerce businesses experiencing an unprecedented surge in growth, online retail application databases have become one of the most popular SQL projects among beginners and advanced learners alike. Such a database allows a customer to register and make a purchase on an online shopping website. The registration process typically collects customer name, address, contact information, and bank details to generate a unique order ID and a bill with the necessary details of the purchase.  8. Book Store Inventory Management Creating and managing an online book store inventory is a pretty simple SQL project idea for beginners. Here, users can browse books online and also order books, in which case, the inventory gets updated, and the book count gets reduced. In addition, users can filter their search for a particular book based on the genre, author name, price, book name, etc. Users can also search books of a specific author. The admin of the inventory can add or delete books. The inventory management system keeps stocks at an optimal level to meet customer demands without delay. 9. Payroll Management System This is one of the most popular SQL projects that find applications across almost every industry. The payroll management system of an organization records the monthly payments, social security, and taxes of its employees. It computes their monthly salaries using data such as name, designation, benefits, pay scale, etc. The management system also keeps a record of employees’ attendance, including the leaves taken. The software then uses specific formulas to generate output in the form of payslips and bank files.  10. Carbon Emission Calculator At a time when almost every nation is taking steps to reduce its carbon footprint, carbon emission calculators are bound to receive global attention. It is an SQL server web application that measures the carbon footprint of buildings based on such data as the type of building, floor area, the climatic zone it is located in, the kind of water fixtures, etc. The project emphasizes the hot and trending topic of environmental protection, and the emissions given as outputs can be correlated to water and energy use, solid waste disposal, transportation, etc.  Way Forward upGrad is South Asia’s largest higher EdTech company. With a 40,000+ global learner base spread over 85 countries, upGrad offers an immersive learning experience by leveraging the best of technology, pedagogy, industry partnerships, and a world-class faculty.  upGrad’s courses have impacted over 500,000 working professionals globally. The Masters in Data Science course is an engaging and rigorous 24-month online program tailor-made for working professionals who want to develop the skills and knowledge to enter a data science career. Here are some of the course highlights about what the program entails: Master of Science in Data Science degree from The University of Arizona. 25+ case studies and 30+ live sessions with ten weeks of capstone project. Comprehensive coverage of programming languages and tools such as Tableau, GitHub, Python, Spark, MySQL, and more. Peer learning and industry networking opportunities. Personalized industry mentorship, learning support, and 360-degree career assistance. Learn data science courses online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career.
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by Pavan Vadapalli

28 Aug 2023

Python Free Online Course with Certification [US 2024]
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Data Science is now considered to be the future of technology. With its rapid emergence and innovation, the career prospects of this course are increasing. Python, being one of its building blocks, is the primary skill recruiters are seeking in their potential recruits, so why not obtain a  free Python certification? But the question arises, what is Python?  In simpler words, Python is one of the most prominent programming languages used to construct web applications. Python’s convenience is what makes it a user-friendly language. Learn Software Development Courses online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs or Masters Programs to fast-track your career. Let’s learn about the usage and importance of Python and how it facilitates modern data science. You can also learn about different Python free certification courses that can further help you pave a path in data science! What Is Python?  Python is a high-level computer programming language often used to build web applications and test software. It is known as the future of coding, which also serves to conduct data analysis. However, it is not specialized for any specific problem. The versatility of Python is what sets it apart from other programming languages. Following its convenience and ease, Python ranks third (38.87%) as the most demanded language by recruiters. Why Is Learning Python Important?  Python is a highly versatile programming language that can be used for both beginner-friendly tasks and complex tasks. It is one of the most accessible programming languages that sets it apart from other languages. Even if somebody is looking to switch careers into the tech world via coding, Python provides opportunities to start as a beginner.  Here we have listed why learning Python is integral and how this coding language will set you on the correct educational path to obtain an edge over others. Reading these will further convince you to apply for Python certification free of cost. Beginner-Friendly  Learning a new language might initially seem daunting, one of the biggest hurdles aspiring coders face. However, Python consists of English syntax that is concise and easily decipherable. As it is an interpreted programming language, candidates can easily learn it and make adjustments to it if needed.  Supportive Community  Python has a robust and supportive community where users discuss everything, from workflow to application development online. After you complete your Python online course certification free of cost, you get to be a part of the online community where you can freely ask questions about any issue. As per the 1.5 million GitHub community repositories, Python has made the coding community the second largest among other repositories.  Versatility  Python is known to be highly versatile, with its use applicable to both simple and complex tasks across different industries. Even though it is commonly used in data science and software engineering environments, it has a multifaceted use with many libraries available. These codes are easily accessible. Python Developers Are In High Demand  Major companies like Dropbox and Netflix seek Python developers with lucrative salaries. Even Google has made its personalized guide for Python for its high demand. The average salary of Python developers is $75,708 a year. It is considered the world’s fourth most lucrative programming language, after Ruby and JavaScript. According to Stackoverflow, Python developers have lucrative opportunities to earn a high salary with less experience.  Usage Of Python Python is the most versatile and commonly used programming language that facilitates software automation, developing websites, data analysis, and data visualization. It is easy to learn and can be quickly adopted by non-programmers as well for its simplicity in syntax. The use case of Python is versatile, as researchers and scientists use it to organize their operations. However, some technical uses of this particular programming language make it one of a kind.  Before you choose a Python free certification course, here we have listed down integral use cases of Python in different fields.  1. Machine Learning and Data Analytics Python is considered a staple in Data Science. With the help of Python, data scientists and data analysts can finally use this language to create complex data visualization reports, build concise Machine Learning algorithms, Data Analytics with manipulation, and complete other data-related tasks. Python can construct a wide spectrum of data visualization through bar and line graphs, with histograms, pie charts, and 3D plots. Python also consists of a wide range of libraries that help coders to write vivid programs for Data Analysis and Machine Learning with utmost efficiency.  2. Scripting or Automation  If you are performing a repetitive task, Python can accomplish your work efficiently with the help of automation. In simple words, writing codes used to build these automation procedures is called scripting in the coding world. This automation can be checked for errors across multiple files and converted to execute simple maths by removing duplicate data.  3. Web Development  Python is often used to develop the backend of any website application. Python plays a vital role in web development, including sending data from different servers and processing data. Python also plays an integral role in communication with different databases alongside URL routing, thus ensuring complete security. Some web development jobs that use Python are backend engineers, full-stack engineers, and DevOps engineers.  4. Daily Tasks  Because of its versatility, Python is not only used to conduct web development and software testing. It opens a great window for people not in data-heavy professions like small business owners and marketers to track the market. Here are some tasks that Python aces at doing. They are –  Tracking stock markets and crypto markets.  Updating shopping list.  Renaming bulk files.  Converting word files into spreadsheets.  Filling out online forms.  5. Test and prototype software Python aids in tasks for building control, bug tracking, and testing in software development. Python tools like Green and Requestium are used for software testing to automate new products and tests.  Why Learn Python Course Free With Certificate? One of the primary reasons to learn a Python free course with a certificate is the job opportunities that come with it in the field of Data Science. As discussed earlier, the jobs of Python software developers are highly in demand. Hence, if you are starting your career with Python, this would be a grand opportunity for you to flourish.  Python also makes you a stronger candidate in Data Analytics. Python facilitates teaching you a wide array of skills essential to initiate a career in data science.  How to Avail Python Certification Free of Cost on upGrad  A Python course free certificate from a reputed platform like upGrad can significantly impact your career. Here’s how you can obtain it. Visit the upGrad Free Courses page. Select your choice of Python course from the long list of courses. Click on Register. Enter the required details to finish registration. You are ready to start your Python journey with upGrad! The Python free certification course enables you to learn the fundamentals of Python at your own pace, making it an exceptional fuel for your skill as well as career development! Conclusion  Python is a general-purpose self-sufficient language that has a huge spectrum of purposes. Due to its versatility, it has gradually gained popularity and is in high demand in the market. Unlike JavaScript and HTML, Python can be used outside the purposes of web development for its readability and flexibility. However, obtaining the right certification is vital to utilize Python’s offered benefits. The best way to kickstart your journey is by applying for a Python free course with certification.  upGrad’s Executive PG Program In Data Science With IIIT-B  Python’s significance in data science is remarkable. The best way to jumpstart a career in data science is by obtaining a free Python course with certificate and suitable credentials.  If you’re interested in data science, check out upGrad’s Executive PG Program in Data Science with the International Institute of Information Technology Bangalore. The course is curated under the guidance of industry experts, comprising in-demand skills and a strong emphasis on practical application.  Enroll now to initiate your data science journey with upGrad! 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by Pavan Vadapalli

14 Apr 2023

13 Exciting Data Science Project Ideas & Topics for Beginners in US [2024]
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Data Science projects are great for practicing and inheriting new data analysis skills to stay ahead of the competition and gain valuable experience. They allow you to work with different types of data, apply different techniques and tools, and gain a better understanding of the data science domain. Here are 13 exciting data science projects for beginners that you can check out to kick off your journey.  Data Science Project Ideas & Topics 1. Web Scraping with Machine Learning Web scraping with machine learning is one of the relatively new data science project ideas that combine the power of both web scraping and ML. You can quickly and accurately gather data from websites and use it to generate business insights. In this data science project, you can extract structured and unstructured data from websites, store it in a database or structured formats such as a CSV or JSON file, and then use machine-learning algorithms written in R or Python to identify patterns, trends, and insights from the web page data. 2. Analyzing and Visualizing US Census Data  Machine learning can be used to analyze and visualize US census data. It can be used to identify patterns and trends in the data and to develop predictive models used to forecast population trends. It is one of the most interesting data science research topics you can have on your resume. Gather US Census Data from the US census bureau. Pre-process the data by cleaning and organizing it. Create a model to analyze the data using machine learning algorithms. Visualize the results with charts, graphs, and other visualizations. 3. Handwritten Digit Classification using the MNIST Dataset The MNIST dataset is a database of handwritten digits used as a benchmark for testing various machine-learning algorithms. It has 60,000 training images and 10,000 testing images. The images are 28×28 pixels and are grayscale.  Download the MNIST dataset and split it into training and test sets. Normalize the pixel values, convert them to floating-point numbers, and reshape the data into the correct format. Create a convolutional neural network (CNN) model to classify the digits. Train the model on the training set using an appropriate optimizer and loss function. Evaluate the model on the test set and measure its accuracy. Tune the model’s parameters and hyperparameters to improve its accuracy. 4. Understanding and Predicting Stock Market Movement The use of machine learning to understand and predict stock market movements is one of the best data analysis project ideas. By leveraging the power of data science and machine learning, investors and traders can build more sophisticated strategies for trading stocks and gain an edge in the market Collect data from financial markets, such as stock prices, volume, and news. Normalize the data and remove any outliers. Build models using machine learning techniques such as regression, decision trees, and neural networks. Evaluate the models by testing the models on a test set of data and measuring the performance of each model. Refine the models by tweaking the hyperparameters of the models or by adding more features to the data. Learn data science courses online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career. 5. Credit Card Fraud Detection with Machine Learning Data Science and Machine Learning can be used to identify suspicious and fraudulent transactions, such as credit card fraud. Collect the data, including information about fraudulent and non-fraudulent credit-card transactions, such as the time and date of the transaction, the amount, and the merchant involved. Remove any irrelevant data, normalize the data, and remove any outliers. Use techniques such as feature selection, feature engineering, and dimensionality reduction. Train the model using techniques such as decision trees, support vector machines, logistic regression, and neural networks. Evaluate the model using cross-validation, precision, and recall techniques. 6. Building a Recommendation System with Collaborative Filtering  Collaborative filtering is a recommendation system that uses the preferences of other users to recommend items to a given user. It is commonly used in e-commerce and streaming platform applications, such as Netflix and Amazon, to suggest items that the user may find interesting based on what other users with similar interests have liked or watched Collect user data about items they have liked or interacted with. Create a user-item matrix, a table containing information about each user and what items they have interacted with. Generate item-to-item similarity scores by calculating how similar the items are to each other based on the preferences of users who have interacted with both items. Use these similarity scores to generate recommendations for each user by matching them with items in the user-item matrix similar to those with whom they have already interacted. Check our US - Data Science Programs Professional Certificate Program in Data Science and Business Analytics Master of Science in Data Science Master of Science in Data Science Advanced Certificate Program in Data Science Executive PG Program in Data Science Python Programming Bootcamp Professional Certificate Program in Data Science for Business Decision Making Advanced Program in Data Science 7. Analyzing and Visualizing Real Estate Data  Real estate data in the US can be analyzed and visualized using machine learning techniques. This is one of the data analytics project ideas where machine learning can predict future trends in real estate, helping investors and buyers make informed decisions.  Collect data from real estate listings and public records. This includes location, size, amenities, prices, and other pertinent characteristics. Clean and prepare the data for analysis. This includes removing any outliers, normalizing the data, and transforming it into a format suitable for analysis. Use descriptive and inferential statistics to analyze the data and uncover insights. This includes calculating summary statistics, creating visualizations, and performing tests to detect correlations and other patterns. Use data visualizations to communicate insights. This includes creating charts, maps, and other visualizations to help illustrate the data and convey key findings. 8. Face Recognition using CNN Convolutional Neural Networks (CNNs) can be used for facial recognition by taking pictures of faces and then learning the features of each face. CNN will learn each face’s features and then recognize a face when it is presented. Gather a dataset of labeled images. This dataset should contain images of people’s faces with labels for each image indicating which person is in the image. Pre-process the pictures by resizing, converting them to grayscale, and normalizing the pixel values. Split the dataset into training, validation, and testing sets. Design a Convolutional Neural Network (CNN) architecture. This may involve choosing the number of layers, the size of the kernels, the type of activation functions, and other hyperparameters. Train the model on the training set. Monitor the validation set performance to determine when to stop training. Evaluate the model on the training set. 9. Analyzing Social Network Data Using Sentiment Analysis  Sentiment analysis is a powerful tool for analyzing social network data. It can help us understand how people feel about specific topics or products. With Machine Learning, we can build powerful models that can analyze large amounts of data to identify sentiment accurately. Collect the data from social network websites. This can be done by using APIs. Transform the data into a suitable format using natural language processing (NLP) techniques to extract relevant features from the text or apply other data transformation techniques. Apply machine learning models to it. Common models used for sentiment analysis include support vector machines, logistic regression, and neural networks. Evaluate the results of the analysis to understand how accurately the model works. Read our Popular US - Data Science Articles Data Analysis Course with Certification JavaScript Free Online Course With Certification Most Asked Python Interview Questions & Answers Data Analyst Interview Questions and Answers Top Data Science Career Options in the USA SQL Vs MySQL – What’s The Difference An Ultimate Guide to Types of Data Python Developer Salary in the US Data Analyst Salary in the US: Average Salary 10. Image Classification with Deep-Learning  This project aims to create a deep-learning model that can classify and identify images using various techniques. The data set chosen for this project is the ImageNet database. The images will be labeled with the appropriate categories, such as animals, plants, objects, and people.  Collect and pre-process data:  Collect the images you want to classify. Pre-process images (resize, normalize, etc.). This can be done with the Keras Library. Define a model architecture:  Choose a convolutional neural network (CNN) model. Configure layers, activation functions, optimizers, etc.  Train the model:  Feed images into the model. Monitor the training process.   Adjust model parameters as needed. Test the model: Feed in unseen data as test data. Review test results. 11. Anomaly Detection with Unsupervised Machine Learning  Anomaly detection with unsupervised machine learning refers to the process of using unsupervised machine learning algorithms to detect outliers or anomalies in a dataset. The most common unsupervised machine learning algorithms for anomaly detection include clustering algorithms such as k-means, density-based algorithms such as DBSCAN, and outlier detection algorithms such as Isolation Forest. These algorithms can be used to detect anomalies in a variety of datasets, such as financial data, time series data, and image data. 12. Analyzing and Visualizing Air Pollution Data  Air pollution is a major global health concern and can seriously impact human health, the environment, and the climate. One way to monitor and assess air quality is by collecting and analyzing air pollution data.  Collect the air pollution data that includes information about air quality, temperature, humidity, wind speed, and other variables relevant to the analysis. Clean and pre-process the data. Use statistical and machine learning algorithms to analyze the data and identify patterns or correlations between air pollution and other environmental variables. Visualize the data using various visualization tools, such as charts, scatter plots, and heat maps. Interpret the results of the analysis and conclude the air pollution data. 13. Time Series Forecasting with Machine Learning This project aims to develop a machine-learning model for time series forecasting.  Collect time series data that you want to forecast. This could include data related to sales, customers, or inventory. Use data visualization techniques to understand underlying trends and patterns in the data. Prepare the data by transforming it into a format suitable for modeling. Select a machine learning model appropriate for the forecasting problem you are trying to solve. Train the model using the prepared data. Evaluate the performance of the model and identify areas that can be improved. Tune the parameters of the model to improve its performance. Conclusion Data science projects are invaluable in helping to understand and interpret data more efficiently and effectively. By engaging in data science project topics, you can gain insights, a competitive advantage in the market, and make better, more informed decisions. Additionally, data science projects can help uncover hidden trends and relationships that can optimize processes and maximize resources. Are you looking to build your career in Data Science? IIITB’s Advanced Certification Programme in Data Science and Machine Learning is a comprehensive program designed to turn you into a master of the fundamentals of Data Science and Machine Learning. This course includes Interactive lectures Hands-on labs Real-world case studies Exclusive job portal for placements and much more 
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by Rohit Sharma

07 Apr 2023

4 Types of Data: Nominal, Ordinal, Discrete, Continuous
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Data refers to the collection of information that is gathered and translated for specific purposes. With over 2.5 quintillion data being produced every day, it is crucial to utilize it in a particular way so that it serves its value to computers and humans for better practices. Data is organized and stored in charts, graphs, and tables. Data is an invaluable commodity with the potential to reap exceptional benefits for any organization if utilized wisely. In order to be utilized at its maximum efficiency, data has to be stored error-free. The best way to do so is by segmenting it into different sections or types.  There are two forms of data, Quantitative Data and Qualitative Data. These forms of data are further classified into four categories: Nominal, Ordinal, Discrete, and Continuous.    Let us read along to know more about types of data in detail.   Different Types of Data in Statistics There are two types of data listed below: 1. Qualitative Data Also known as categorical data, this type of data is neither measured nor counted in the form of numbers. This form of data is assembled by category, not by numbers and usually consists of text and photographs. Qualitative data talks about the emotions of a person. It helps market researchers know and understand their customers’ tastes and preferences, so they can further create new strategies per customers’ demands. Qualitative data helps to provide in-depth analysis.   Qualitative Data is divided into two parts:  Nominal Data The first type of qualitative data is Nominal Data which labels variables without the numerical value. It is a form of data that cannot be measured.  Let us understand nominal data with an example. For instance, the color of a car can be black, red, or orange. Here we need to realize that no color is greater than the other. It just represents a particular color of a car. Nominal data does not follow any hierarchy. This kind of data is used in fields such as education, healthcare, and business to collect and make relevant assessments. Analysts collect nominal data with the help of public surveys to further analyze and obtain results.  Ordinal Data Ordinal data values are also grouped into categories, just like nominal data. This kind of data is considered a mixture of quantitative and qualitative data while it follows a specific pattern of hierarchy. Let us take an example to understand ordinal data even better. For instance, the economic status of people within a society. There are usually three socioeconomic status categories: High, Middle, and Lower. Here we can identify that the high class is considered superior to the middle class. Therefore, ordinal data follows a specific pattern of hierarchy.  Nominal Data versus Ordinal Data Here are the differences between nominal and ordinal data listed below:  Nominal data cannot be compared with one another, whereas ordinal data can be used to compare different items by natural ordering.  Nominal data is one type of qualitative data, whereas ordinal data type is referred to be in-between qualitative data and quantitative data.  Nominal data does not use numerical values, whereas ordinal data uses numerical values.  Learn data science courses online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career. 2. Quantitative Data  In quantitative data, information is countable and measured. This kind of data can be used for statistical manipulations and are represented on pie charts, histogram, and bar graphs. Data like these play an important role in statistics. The researcher uses quantitative data to solve “what” and “how” problems. This form of data is used frequently in math calculations or statistical analysis.  Quantitative data is further divided into two parts:  Discrete Data The word discrete means separate. A discrete data value can be counted but cannot be measured as its holds only fixed values. The data is easy to visualize and calculate since a numerical is usually a whole number. The discrete data tends to be easily calculated as it is organized in graphs and scatter plots.   For instance, the number of chocolates in a box or students in a class are examples of discrete data. Discrete data is represented in bar charts and pie charts, which are further used to compare different and unique data points.  Continuous Data Continuous data is a type of quantitative data that represents information in smaller parts that are easy to measure. Continuous data is more accurate as compared to other data types. Height, weight, and temperature are examples of continuous data. Although offering accuracy, continuous data type tends to change over time. For instance, the height of an individual will change over the coming years.  Discrete data versus Continuous data  Some of the differences between discrete and continuous data are given below:  Discrete data is countable, whereas continuous data is measurable.  The values in discrete data cannot be separated into smaller pieces, whereas in continuous data, the complex values can be segmented into smaller, more consumable parts.  Discrete data is represented by bar graphs, whereas continuous data is represented mainly by histograms.  Check our US - Data Science Programs Professional Certificate Program in Data Science and Business Analytics Master of Science in Data Science Master of Science in Data Science Advanced Certificate Program in Data Science Executive PG Program in Data Science Python Programming Bootcamp Professional Certificate Program in Data Science for Business Decision Making Advanced Program in Data Science Significance of Data The role of data in fueling growth for several businesses is pretty apparent. Data plays a prominent role in establishing a thriving organization that consistently works on its inefficiencies with data-driven insights to better understand its business and customers. Here’s how data influences varying domains to mark its importance.  Informed Decision-Making Data helps an organization gather information and knowledge related to different business aspects, from customer behavior to the latest trends. This further helps the leader to make well-informed decisions. Therefore, clean and structured data is vital for better reasoning and less risky decisions based on data-driven facts.   In-depth Understanding An in-depth understanding of data helps gain accurate insights for individuals and organizations. Leaders should understand how every aspect of a business is performing in order to accomplish its target and goals, which is only possible with the help of data-led analysis of organizational processes.  Effective Problem Solving     When any product fails to promote sales, data helps organizations to discover the cause and underlying reasons for its failure. Along with a report card of aspects that need improvement, data assists organizations in reaching a valid conclusion as well, extending possible solutions.  Comprehending User Behavior   For a successful organization, it is essential to understand customers’ needs and the company’s stance on how well they can perform on the requirements. Data can help a business to build better knowledge and understanding of their potential customers. This will enable them to identify the specific needs of their audience. Along with user experience, personal growth is another aspect data can influence. For instance, keeping track of the annual objectives and successful goals of specific employees enables managing entities to evaluate their yearly performance.  Read our Popular US - Data Science Articles Data Analysis Course with Certification JavaScript Free Online Course With Certification Most Asked Python Interview Questions & Answers Data Analyst Interview Questions and Answers Top Data Science Career Options in the USA SQL Vs MySQL – What’s The Difference An Ultimate Guide to Types of Data Python Developer Salary in the US Data Analyst Salary in the US: Average Salary Exhibiting Process Improvement    Data helps to reduce the wastage of resources, thereby improving the processes. Further, it helps the leaders to know the organization’s weaknesses, hurdles, or breakdowns in a process. Hence, data plays a vital role in every business. With the help of insights redeemed from varying databases, companies can supervise each step of an ongoing process, along with its vulnerabilities, which can be further improved to make the project a success.  Conclusion  The significance of data in different fields is apparent following its use to fuel diverse organs of a business. From understanding employee performance to encouraging marketing teams to absorb in-demand techniques for better customer experience. Knowing data types and their importance in assessment is essential to adopt innovative data analysis tools, which can help you reap maximum value for your business.  One such field that heavily leverages data is Business Analytics, which uses statistical models built on large databases to reap insightful information for organizations. If you’re interested in opting for a career in Business Analytics, check out upGrad’s Masters of Science in Business Analytics. Offered under the guidance of San Francisco’s leading institute Golden Gate University, this program covers topics such as Business Intelligence, Machine Learning, Predictive Analytics, Natural Language Processing, and more to help you obtain lucrative opportunities. 
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by Rohit Sharma

06 Apr 2023

Best Python Free Online Course with Certification You Should Check Out [2024]
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Data Science is now considered to be the future of technology. With its rapid emergence and innovation, the career prospects of this course are increasing. Python, being one of its building blocks, is the primary skill recruiters are seeking in their potential recruits, so why not obtain a  free python certification? But the question arises, what is Python?  In simpler words, Python is one of the most prominent programming languages used to construct web applications. Python’s convenience is what makes it a user-friendly language. Let’s learn about the usage and importance of Python and how it facilitates modern data science. You can also learn about different Python free certification courses that can further help you pave a path in data science! What Is Python?  Python is a high-level computer programming language often used to build web applications and test software. It is known as the future of coding, which also serves to conduct data analysis. However, it is not specialized for any specific problem. The versatility of Python is what sets it apart from other programming languages. Following its convenience and ease, Python ranks third (38.87%) as the most demanded language by recruiters. Why Is Learning Python Important?  Python is a highly versatile programming language that can be used for both beginner-friendly tasks and complex tasks. It is one of the most accessible programming languages that sets it apart from other languages. Even if somebody is looking to switch careers into the tech world via coding, Python provides opportunities to start as a beginner.  Here we have listed why learning Python is integral and how this coding language will set you on the correct educational path to obtain an edge over others. Reading these will further convince you to apply for Python certification free of cost. Beginner-Friendly  Learning a new language might initially seem daunting, one of the biggest hurdles aspiring coders face. However, Python consists of English syntax that is concise and easily decipherable. As it is an interpreted programming language, candidates can easily learn it and make adjustments to it if needed.  Supportive Community  Python has a robust and supportive community where users discuss everything, from workflow to application development online. After you complete your Python online course certification free of cost, you get to be a part of the online community where you can freely ask questions about any issue. As per the 1.5 million GitHub community repositories, Python has made the coding community the second largest among other repositories.  Versatility  Python is known to be highly versatile, with its use applicable to both simple and complex tasks across different industries. Even though it is commonly used in data science and software engineering environments, it has a multifaceted use with many libraries available. These codes are easily accessible. Learn data science courses online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career. Python Developers Are In High Demand  Major companies like Dropbox and Netflix seek Python developers with lucrative salaries. Even Google has made its personalized guide for Python for its high demand. The average salary of Python developers is $75,708 a year. It is considered the world’s fourth most lucrative programming language, after Ruby and JavaScript. According to Stackoverflow, Python developers have lucrative opportunities to earn a high salary with less experience.  Usage Of Python  Python is the most versatile and commonly used programming language that facilitates software automation, developing websites, data analysis, and data visualization. It is easy to learn and can be quickly adopted by non-programmers as well for its simplicity in syntax. The use case of Python is versatile, as researchers and scientists use it to organize their operations. However, some technical uses of this particular programming language make it one of a kind.  Before you choose a Python free certification course, here we have listed down integral use cases of Python in different fields.  Machine Learning and Data Analytics Python is considered a staple in Data Science. With the help of Python, data scientists and data analysts can finally use this language to create complex data visualization reports, build concise Machine Learning algorithms, Data Analytics with manipulation, and complete other data-related tasks. Python can construct a wide spectrum of data visualization through bar and line graphs, with histograms, pie charts, and 3D plots. Python also consists of a wide range of libraries that help coders to write vivid programs for Data Analysis and Machine Learning with utmost efficiency.  Check our US - Data Science Programs Professional Certificate Program in Data Science and Business Analytics Master of Science in Data Science Master of Science in Data Science Advanced Certificate Program in Data Science Executive PG Program in Data Science Python Programming Bootcamp Professional Certificate Program in Data Science for Business Decision Making Advanced Program in Data Science Scripting or Automation  If you are performing a repetitive task, Python can accomplish your work efficiently with the help of automation. In simple words, writing codes used to build these automation procedures is called scripting in the coding world. This automation can be checked for errors across multiple files and converted to execute simple maths by removing duplicate data.  Web Development  Python is often used to develop the backend of any website application. Python plays a vital role in web development, including sending data from different servers and processing data. Python also plays an integral role in communication with different databases alongside URL routing, thus ensuring complete security. Some web development jobs that use Python are backend engineers, full-stack engineers, and DevOps engineers.  Daily Tasks  Because of its versatility, Python is not only used to conduct web development and software testing. It opens a great window for people not in data-heavy professions like small business owners and marketers to track the market. Here are some tasks that Python aces at doing. They are –  Tracking stock markets and crypto markets.  Updating shopping list.  Renaming bulk files.  Converting word files into spreadsheets.  Filling out online forms.  Test and prototype software Python aids in tasks for building control, bug tracking, and testing in software development. Python tools like Green and Requestium are used for software testing to automate new products and tests.  Why Learn Python Course Free With Certificate? One of the primary reasons to learn a Python free course with certificate is the job opportunities that come with it in the field of Data Science. As discussed earlier, the jobs of Python software developers are highly in demand. Hence, if you are starting your career with Python, this would be a grand opportunity for you to flourish.  Python also makes you a stronger candidate in Data Analytics. Python facilitates teaching you a wide array of skills essential to initiate a career in data science.  Read our Popular US - Data Science Articles Data Analysis Course with Certification JavaScript Free Online Course With Certification Most Asked Python Interview Questions & Answers Data Analyst Interview Questions and Answers Top Data Science Career Options in the USA SQL Vs MySQL – What’s The Difference An Ultimate Guide to Types of Data Python Developer Salary in the US Data Analyst Salary in the US: Average Salary How to Avail Python Certification Free of Cost on upGrad  A Python course free certificate from a reputed platform like upGrad can significantly impact your career. Here’s how you can obtain it. Visit the upGrad Free Courses page. Select your choice of Python course from the long list of courses. Click on Register. Enter the required details to finish registration. You are ready to start your Python journey with upGrad! The Python free certification course enables you to learn the fundamentals of Python at your own pace, making it an exceptional fuel for your skill as well as career development! Conclusion  Python is a general-purpose self-sufficient language that has a huge spectrum of purposes. Due to its versatility, it has gradually gained popularity and is in high demand in the market. Unlike JavaScript and HTML, Python can be used outside the purposes of web development for its readability and flexibility. However, obtaining the right certification is vital to utilize Python’s offered benefits. The best way to kickstart your journey is by applying for a Python free course with certification.  upGrad’s Executive PG Program In Data Science With IIIT-B  Python’s significance in data science is remarkable. The best way to jumpstart a career in data science is by obtaining a free Python course with certificate and suitable credentials.  If you’re interested in data science, check out upGrad’s Executive PG Program in Data Science with the International Institute of Information Technology Bangalore. The course is curated under the guidance of industry experts, comprising in-demand skills and a strong emphasis on practical application. 
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by Rohit Sharma

05 Apr 2023

5 Types of Binary Tree in Data Structure Explained
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A binary tree is a non-linear tree data structure that contains each node with a maximum of 2 children. The binary name suggests the number 2, so any binary tree can have a left and right child. A pointer depicts a binary tree to the uppermost node, usually known as the tree’s root. Every node in a binary tree contains data, a pointer to the left child, and a pointer to the right child. Pointers are used to implement a binary tree. The root pointer denotes the first node in the tree. In order to create a binary tree, you need to first create a node. After being familiar with what is binary tree in data structure, you also need to know the basic operations performed on a binary tree. You can perform functions like inserting an element, removing an element, searching for an element, and traversing the binary tree. Any binary tree in data structure is used in two different ways in computing. Firstly, they are used to access nodes depending on specific labels or values linked with each node. Secondly, they are used as data representations with a relevant branched structure. Before going through various binary tree types, let’s first get familiar with the terminologies used in a binary tree. Node: It holds a data value in a binary tree. Root: Topmost node in any binary tree is called the root of a tree. Size: It denotes the number of nodes in a binary tree. Parent node: A node in a binary tree with a child. Child node: It is a node that originates from a parent node moving away from the root of the binary tree. Internal node: It is a node with at least one child in a binary tree. Leaf node: It is a node with no child. It is alternatively an external node. Depth of a node tree: It is calculated in the context of a particular node. It is referred to as the number of edges from a particular node to the root. Internal path length of a tree: It refers to the sum of the levels of every internal node in a binary tree. External path length of a tree: It is defined as the sum of the levels of every external node in a binary tree. Height of node in a tree: It is the number of edges from a specific node to the deepest leaf node of the binary tree. The height of the root will always be more than that of other nodes in the binary tree. Now let’s check out the details of 5 types of binary tree. Types of Binary Tree  1. Full Binary Tree This binary tree in data structure is also known by the names -Proper binary tree and Strict binary tree. It is one of the most fundamental types of binary tree in data structure. A Full binary tree is defined as a binary tree where each node should have two or no children at all. It is alternatively characterized as a binary tree in which every node comprises two children except the leaf nodes. The nodes in which the address of the root node is stored are called children nodes of the root. Those nodes devoid of children are called leaf nodes. You need to travel all the nodes to check whether a tree is a binary tree. The code will return “False” if any node has one child. It will return “True” if all nodes have 0 or 2 children.  Here are the properties of a full binary tree: The number of leaf nodes equals the number of internal nodes + 1. For instance, if the number of internal nodes is 4, the number of leaf nodes equals 5. The maximum number of nodes and the number of nodes in a binary tree are equal. It is represented as 2h+1 -1. The minimum number of nodes in a full binary tree is 2h-1. The minimum height of a full binary tree is log2(n+1) – 1. The maximum height of a full binary tree is h = (n+1)/2. 2. Perfect Binary Tree A Perfect binary tree is one of those types of binary trees wherein every node must have 0 or 2 children, and each leaf node’s level must be the same. In other words, perfect binary trees in data structure are defined as those trees where all interior nodes possess two branches and those nodes with no branches (leaf nodes) exist at the same level. In this context, the level of a node is the distance or height from the root node. You can consider a perfect binary tree as a complete binary tree wherein the last level is also completely occupied. Learn data science courses online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career. 3. Complete Binary Tree A Complete binary tree is one of those types of binary tree in data structure wherein all the levels of the tree are fully filled. The binary tree’s last level may or may not be completely filled. However, every node must be located at the leftmost position in the node’s last level. The nodes must be included from the left. They are one of the essential types of binary trees because they offer the best ratio between the number of nodes and a tree’s height. Here are the key properties of a complete binary tree: The maximum number of nodes is 2h+1 – 1. The minimum number of nodes is 2h. The minimum height is log2(n+1) – 1. 4. Balanced Binary Tree In Balanced binary trees, a tree’s height is log2 of the total number of nodes. Suppose the tree’s height is h and the tree’s total number of nodes is n. The equation to calculate height is h = log(n). The maximum difference in height between a right sub-tree and a left sub-tree must be “1”. The difference can have other values like 0 and -1. These types of binary trees are also called AVL trees. One of the well-known examples of balanced binary trees is the Red-Black tree.  You can implement the following code to run a balanced binary tree. private class Node { private int value;   private Node left;    private Node right; } public boolean isBalanced(Node n) { if (balancedtreeHeight(n) > -1) return true; return false; } public int balancedtreeHeight(Node n) { if (n == null) return 0; int h1 = balancedtreeHeight(n.right); int h2 = balancedtreeHeight(n.left); if (h1 == -1 || h2 == -1) return -1; if (Math.abs(h1 – h2) > 1) return -1; if (h1 > h2) return h1 + 1; return h2 + 1; } Check our US - Data Science Programs Professional Certificate Program in Data Science and Business Analytics Master of Science in Data Science Master of Science in Data Science Advanced Certificate Program in Data Science Executive PG Program in Data Science Python Programming Bootcamp Professional Certificate Program in Data Science for Business Decision Making Advanced Program in Data Science 5. Degenerate Binary Tree A Degenerate binary tree is one of the less frequently used types of binary search tree. It is a binary tree in which each node has only one child, i.e., a left or right child. No node should have both left and right children. Read our Popular US - Data Science Articles Data Analysis Course with Certification JavaScript Free Online Course With Certification Most Asked Python Interview Questions & Answers Data Analyst Interview Questions and Answers Top Data Science Career Options in the USA SQL Vs MySQL – What’s The Difference An Ultimate Guide to Types of Data Python Developer Salary in the US Data Analyst Salary in the US: Average Salary Conclusion It is essential to understand what is binary tree in data structure if you want to use it for various applications. Implementing different functions on binary trees can assist you in efficiently organizing and storing data. Studying multiple types of binary trees helps you to perform operations more effectively in time complexity. The data science fundamentals, including binary tree data structure, help you start your data science journey easily. Subsequently, you can work on advanced data science projects to enhance your skills and portfolio. Get Started With Your Machine Learning Journey on upGrad If you want to learn Data Science, you can pursue upGrad’s Master of Science in Data Science program. This 24-month course imparts skills like Python, Deploy ML Models, BD processing using Spark, Predictive Analytics & Statistics, and Supervised and Unsupervised ML Models. The course is suitable for ambitious managers, MBA graduates, engineers, and professionals in various domains.  Completing this course will help you to work as Data Engineer, Big Data Analyst, Machine Learning Engineer, and Data Scientist. 
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by Rohit Sharma

03 Apr 2023

42 Exciting Python Project Ideas & Topics for Beginners [2024]
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Python is an interpreted, high-level, object-oriented programming language and is prominently ranked as one of the top 5 most famous programming languages. Various practical use cases for Python projects are AI & Machine Learning, Data Analytics, Web Development, Search Engine Optimization (SEO), Game Development, Automation, and Blockchain. Working on Python projects as such allows you to expand your horizon and strengthen your programming skills for advanced-level projects.  Let’s look at some exciting Python project ideas and topics for beginners to improve their skills and enhance their portfolios.  Top Python Project Ideas & Topics 1. Currency Converter using Python library It is one of the extensively used Python projects because it converts one country’s currency to another. It uses the Tkinter library of Python. 2. Traffic Signs Identifying in Python This Python project automatically identifies the image, and then the vehicle functions as per the image captured. It uses the Keras framework, which is a Python library. 3. Counting the frequency of elements in a list It is one of the easiest Python projects for beginners. You need to accept an input list from the user and determine the number of every unique element. This project prioritizes using a method that doesn’t go through the whole list in each iteration. 4. Quiz This Python project involves creating a question bank with MCQ (multiple choice questions). A scoring mechanism is implemented for the individuals playing the quiz. 5. Expenditure tracker Python Program It uses PySimpleGUI, which helps you to design an expense-tracking app interface. The concepts of Matplotlib and Pandas are also used. 6. Contact Book It is one of those Python projects for beginners that execute from the command line. It involves creating a contact book app that enables users to save and access contact information. 7. Create a ToDo List In this project, you can include various features like adding a new task to your ToDo list, actions to do after completing a specific task, and more. 8. Horoscope prediction It involves importing the DateTime module that lets you obtain the existing date and time from your PC. You also need to import the pytz module to transform your local time into UTC (Universal Coordinated Time) for predicting the horoscope of any individual. 9. Mad Libs Generator This Python project imparts how to handle user-input data. It focuses on variables, strings, and concatenation. After you’ve entered the inputs, the application organizes them into a story template format. Learn data science courses online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career. 10. Number Prediction This project allows the computer to randomly select a number between 1 to 10, 1 to 100, or another range. The users then get a hint to predict the number. 11. Dice Rolling Simulator When the program executes, it creates a random number between 1 and 6 for each die. The users can repeatedly use the dice. 12. Text-based Adventure Game It allows users to move across various rooms in a single setting. It presents each room’s description according to the user input. 13. Hangman It uses core Python concepts like integer, variables, char, strings, input, output, and Boolean. The users need to enter letter guesses. Every user will own a fixed number of guesses. Check our US - Data Science Programs Professional Certificate Program in Data Science and Business Analytics Master of Science in Data Science Master of Science in Data Science Advanced Certificate Program in Data Science Executive PG Program in Data Science Python Programming Bootcamp Professional Certificate Program in Data Science for Business Decision Making Advanced Program in Data Science 14. Email Slicer The users need to create a Python program to access the domain and username of the email in the Email Slicer project. You can also deliver a message to the host. 15. Desktop Notifier App The desktop notifier apps deliver you some information after a pre-determined duration. You can use requests, notify2, etc., libraries for this project. 16. Binary search algorithm It is one of those Python project ideas that explains several core concepts of Python. It allows the user to input a random number, and then the Python program checks if that number is available in the list. Read our Popular US - Data Science Articles Data Analysis Course with Certification JavaScript Free Online Course With Certification Most Asked Python Interview Questions & Answers Data Analyst Interview Questions and Answers Top Data Science Career Options in the USA SQL Vs MySQL – What’s The Difference An Ultimate Guide to Types of Data Python Developer Salary in the US Data Analyst Salary in the US: Average Salary 17. YouTube video downloader It comes in handy when you want to download YouTube videos. However, YouTube doesn’t support that option. You can create an app to download YouTube videos in various video formats. 18. Python Story Generator It helps you to generate a random story every time the user executes the program. Its Python program asks users for inputs like action, place’s name, etc. 19. Python Website Blocker It allows you to create a Python program that blocks unwanted websites from being displayed. It helps you to master many fundamentals of Python. 20. Live Weather Notifications It extracts data from the provided URL to display live weather notifications. It converts the data into HTML code, finds the necessary details, and filters them. 21. Spin a Yarn First, the corresponding Python program asks the user to provide several inputs. The entered inputs are stored in a pre-built story template with the help of concatenation. Finally, the entire story is printed out. 22. Leap it! This Python project requests the user to input a year, and then it checks whether the entry is a leap year or not. It requires you to define a function that identifies the pattern of leap years. 23. Rock, Paper, Scissors This Python project proves to be useful if you don’t have any playmates. Usually, its stint is 5 minutes long, and it is designed to uplift your mood. 24. Number check in the Fibonacci sequence This project asks you to input a number and then determines whether that number fits the Fibonacci sequence. 25. Countdown Clock and Timer Its Python code collects the input related to the countdown’s length within seconds. Once the inputs are received, the countdown starts and will display on your screen. 26. Calculator It allows you to quickly build a graphically appealing UI calculator. It uses the Tkinter library and makes you more familiar with the features of this library. You can make buttons to do various operations and show output on a screen.  27. Random Password Generator It allows you to build a Python program to input a few words from a user. Subsequently, it generates a random password with the help of those words. Moreover, the user can recollect the password using their entered words. 28. Reddit Bot Reddit is an easily accessible platform, and many people wish to be online for as long as possible. In this project, you can configure a bot that supervises subreddits and informs them whenever they find anything helpful. 29. Random Wikipedia Article Initially, it may feel complex, but it is an easy Python program. It searches across Wikipedia and retrieves a random article. Subsequently, it asks the user whether they want to read a particular article or not. If the answer is affirmative, the article is displayed. Else, the following random report is shown. 30. Alarm Clock It is one of the easy-to-implement Python project ideas that involve an easy CLI (command line interface) Python application. You can set a specific time on the alarm clock. Subsequently, it will choose a random YouTube link from a particular text link and finally plays the YouTube video. 31. Steganography It allows you to build a program that protects messages hidden within pictures. It can quickly encode and decode images. 32. Tic-Tac-Toe It typically involves two players playing Tic-Tac-Toe games at a time. The game players can easily create a 3×3 sized square grid. The Pygame library helps build this project. 33. Post-it Notes This project involves creating a simulated version of the physical post-it notes. Its key objective is to permit users to take their post-it notes anywhere they go. 34. Directory Tree Generator It allows you to envisage the relationship between directories and files. Hence, it makes understanding the location of directories and files simpler. 35. Site Connectivity Checker Its primary job is to visit a URL and show the status of the particular URL. It displays status like whether the URL is live or not. You can use relevant frameworks for incorporating commands, allowing users to add and delete specific URLs from the displayed list of URLs they wish to check. 36. Speed Typing Test The foremost step in building this project is creating a UI via a Python library called Tkinter. It then creates a typing test that indicates the user speed, words entered per minute, and typing accuracy.  37. Image Resize App If you are interested in machine learning, this project idea can be quite valuable. It then carries out certain tasks like resizing and renaming images. 38. Content Aggregator You can develop a content aggregator for this project, and it will automatically search famous websites. It searches for relevant content, compiles it, and allows users to decide which content they need. 39. Plagiarism Checker Content is the kin, but the content writing market doesn’t have lots of free tools to check plagiarism in content. In this project, you can use an NLP library and the Google search API to build a Python program that searches the leading few pages of Google. Subsequently, it detects plagiarism. 40. Music Player This project involves developing a music player app that boasts a neat interface. It allows users to surf through tracks, alter the volume, and check the song’s name and other relevant information. To work on this project, you need to be well-versed in Python programming fundamentals. 41. Price Comparison in Python It allows you to develop a Python program that searches a few remarkable websites for the product’s price. It then displays the best available deal. This project comes in handy for comparing the prices of various day-to-day products. 42. Web Crawler It involves creating a web crawler that browses the internet and saves the content of a specific webpage. You need to implement a multi-thread fundamental. You can use Scrapy or Python’s request module to create the crawler bot. Get Started With Your Data Science Journey on upGrad Begin your Machine Learning and Data Science journey with upGrad’s Advanced Certificate Programme in Data Science program. This 8-month course provides excellent benefits like Complimentary Python Programming Bootcamp, Advanced Certification from IIIT Bangalore, Career Essential Soft Skills Program, AI-Powered Profile Builder, and more. It covers programming languages and tools like Excel, Python, and MySQL, making it suitable for engineers, IT professionals, sales and marketing professionals, managers, and BFSI professionals. Completing this course can help you work in job roles like ML Engineer, Data Scientist, Data Analyst, Business Analyst, Product Analyst, and Chief Architect.  Conclusion Being extremely versatile and easily accessible, Python is used for various practical applications. You can work on any Python project ideas to explore more features. Initiate your journey by working on Python projects for beginners and slowly work on more advanced ones to strengthen your skill and portfolio. 
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by Rohit Sharma

02 Apr 2023

5 Reasons Why Python Continues To Be The Top Programming Language
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Introduction Python is an all-purpose high-end scripting language for programmers, which is easy to understand and replicate. It has a massive base of pre-built structures, i.e., a set of words that programmers may use easily and conveniently. Python’s compatibility with all the leading Operating Systems, like Windows, Android, Mac, Linus, etc., makes it one of the most prominent programming languages in the software development industry. Although a high-end language, Python is relatively simpler than its peers, particularly for new beginners with little or no prior coding knowledge. Python’s ease of use and popularity has turned it into one of the fastest-growing and top programming languages.  Let’s dive in to learn more about Python’s features and what makes it more desirable among developers.  Uses of Python Python is regularly used by big and small organizations alike for the following activities – Constructing websites. Quick prototyping. Building mobile, tablet, and desktop applications. Machine learning and artificial intelligence. Data Science and Data analysis. Software testing in a wide range of industries. Product automation by the process of creating recommendation algorithms and building suitable software. For instance, the one used for self-driven cars.  Process automation in the finance and insurance sectors to improve performance. Assisting ethical hacking techniques to strengthen the cyber security domain. Why is Python Popular? Python stands out among the various available programming languages used in today’s world. Besides needing less coding, Python is affordable for all businesses, making it a trendy choice. Let’s discuss the five reasons why Python is considered one of the top programming languages.  1. Use of Python in Artificial Intelligence, Machine Learning, and Data Science  Python is the most widely used language in the artificial intelligence, machine learning, and Data Science field. The above subjects require a lot of algorithms in their processes. Using Python, a programmer may go on coding the algorithms and check the errors, if any, simultaneously. They get the opportunity of correcting errors on the go. However, using other languages does not give this advantage to the programmer. Python coding procedures are somewhat similar to those using the English language. Thus, beginners in this field also feel at ease using Python. Essentially, Python is the best language to be implemented for artificial intelligence, machine learning models, and deep learning models, generally in the field of Data Science, following its simple yet dynamic nature.  2. Python is Object Oriented & Procedure Oriented with Built-in data structures Another reason Python is the best programming language to learn is that it is objective and procedure-oriented. The vast dictionary data structures and the built-in list of Python are often used to build up fast, run-time data structures effortlessly. Another feature that makes this language attractive is its high-level data typing option that helps to reduce the required length of support codes. The in-built functions are helpful for new learners on their way forward to creating a program and veteran users who can use this feature to build programs effortlessly and quickly. Thus, though Python is a bit slower than other languages like C++ or Java, this excellent feature helps it overcome this disadvantage. Ultimately, Python emerges as a winner amongst programmers in terms of popularity. These built-in structures help a programmer to develop powerful web applications in a much easier and quicker manner. As a programming language, Python has an orientation that is a function of objects and data. The procedure of using standard library codes in Python language is an outstanding feature. 3. Diverse application range Another point that explains why Python is popular is its comprehensive application range coverage. The application range is broad in terms of profession. Engineers, scientists, and mathematicians use it extensively across the industry for various applications. It is used in several industries specializing in product automation, process automation, entertainment, infotainment, information technology, and even finance. It is also used for animation in movies, image processing, and graphic design applications. Learn data science courses online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career. 4. Diverse job opportunities Generally, there is a myth that when more and more professionals adopt a particular field, the same gets saturated easily. Hence, learning the subject becomes less rewarding when translated in terms of compensation or, in other words, job opportunity gets reduced.  However, Python language proves this myth to be wrong. Not only has the demand for Python-learned professionals increased daily, but learning this language has also proved to be the most rewarding in terms of building a dream career. Top companies have embraced Python for its simplicity and flexibility. The prospective candidate can be a freelancer as a Python Developer who may develop websites and tailor-made portals for clients. The career of a software engineer is one of the smartest applications of Python. For this, the professional has to code as per the user or client’s requirement, test the same and then further modify it as per the consultation with the client. After learning Python, choosing to be a Full Stack Developer or a Data Scientist is also a valuable career. Being a DevOps Engineer is another rewarding career after learning Python.  Check out all trending Python tutorial concepts in 2024. 5. Compatibility with various platforms This particular feature in Python has transformed it into the best programming language to learn. Compatibility is an issue faced by most programmers while using other programming languages. The wide range of operating systems used in desktops, tablets, and mobile applications covers Windows, Android, Mac, Linux, etc. All these come under the ambit of Python. This feature makes it highly favorable. Most of the available operating systems across a plethora of devices are in great sync with Python. Read our Popular US - Data Science Articles Data Analysis Course with Certification JavaScript Free Online Course With Certification Most Asked Python Interview Questions & Answers Data Analyst Interview Questions and Answers Top Data Science Career Options in the USA SQL Vs MySQL – What’s The Difference An Ultimate Guide to Types of Data Python Developer Salary in the US Data Analyst Salary in the US: Average Salary Conclusion Though the importance of other programming languages is not ruled out, Python continues to be the top performer. It is the first high-end language that every aspiring programmer or ethical hacker should learn at the very beginning. The extensive usage of this language has made it more and more popular among beginners since they may easily find a guide to their queries. Since Python is a well-written language, it can simplify business activities. Its easy availability makes it affordable even for smaller business ventures. The codes in this language are well structured, as well.  Most languages have a high expenditure on maintenance, i.e., updating their useful features and getting rid of bugs and viruses. In the case of Python, it may be noted that its bug removal expense is much lesser. Netflix, an Entertainment Giant, is also using Python officially to deliver content to its subscribers. Google prefers Python to simplify its search function as well. The more it is getting popular, the more enthusiastic prospective job seekers are getting about it. There is perfectly no question of professional saturation as far as the language Python is concerned.  upGrad’s Executive PG Program In Data Science  Proficiency in Python can lead you to explore some of the most lucrative career opportunities, and Data Science is one of them. upGrad’s Executive PG Program in Data Science offered under IIIT-Bangalore is an excellent course to achieve your desired goal. Some of the key highlights of this course are as follows –  This course will teach you around 14+ programming tools and languages. The duration of this course is 12 months. You can have a one-to-one conversation with the industry mentors. This course offers you exposure to the Kaggle competitions. It will help you in the job market as this course is designed to be suitable for working professionals.  In case of any query, you can ask the customer support system, which is available 24 hours a day.
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by Rohit Sharma

01 Apr 2023

Why Should One Start Python Coding in Today’s World?
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Python is the world’s most popular programming language, used by both professional engineer developers and non-designers. It is a highly demanded language following its advantage of accessibility on multiple platforms, such as Windows, macOS, Raspberry Pi, and Linux. It has a straightforward syntax that can be learned by non-professionals too. This programming language is used from Netflix’s suggestion algorithm to self-driving automated cars like Tesla, proving its versatility and efficiency.  It can be used for many functions like automation, data science, software designing, and website development. Unlike other programming languages that have complex codings and take long hours to execute a simple program, Python is easy to learn and beginner-friendly. Not to forget, it is easily accessible and is an open-sourced language with a verified license.  Considering all these reasons and more, Python should be your topmost preferred language. Let’s dive in deep to learn all about this high-level language.  Where Can You Use Python? The reason why Python is one of the most used programming languages is it doesn’t have any specific specialization towards particular programming of software. It can be used as a general-purpose software language that solves all operating systems problems. As a software developer or a web designer, if you’re constantly thinking, “why learn Python?” or whether it would be the right step, then know that it’s very beginner-friendly. It allows users to write programs using shorter lines against other programming languages.  Origins of Python The foundations of Python came in the late 1980s. A dutch person named Guido Van Rossum was in the process of creating a programming language for educational purposes. He intended to name it ABC. A fun fact about Python is that it was named after a BBC show. Guido Van Rossum was watching a BBC comedy series called ‘Monty Python’s Flying Circus.’ He wanted to choose a creative name for the programming language, and this series left a mark on him. He was so inspired by it that he named the language ‘Python.’  Rather than focusing more on the mathematical side of programming, he paid more attention to the language and design aspects. That’s when he created Python. His unusual choices to make a very different programming language resulted in the unique characteristics of Python.  In 1991, the first version of Python was released with the name Python 1.0. It included features of the map and location. After three years, in 1994, the second version of Python was released. More features like comprehension, internal bugs, and trouble-collection techniques were included. After 14 years, in 2008, the third version of Python was also launched. It was also known as Py3K. It had developments focusing on the language foundations for the software. The consistent evolution of Python reflects its improved features and efficient implementations across platforms.   Advantages of Using Python Coding in Today’s World In light of questioning Python’s ability and significance, let’s take a look at its various advantages.  Python is a better programming experience than other programming languages. If you compare the codings of Python, the numbers and letters are much easier compared to Java, C++, etc. Unlike other programming languages, Python is continuously developing. It has frequent updates, allowing it to resolve internal problems and add additional features whenever necessary.  It consists of an automated self-preserved memory system with comprehensive features and a substantial spaced library.  Using Python helps developers and beginners who want to try coding to learn it very quickly. It consists of easy, readable syntax which is securely executed. Many scholars and learned professionals share problem-solving ideas while using Python. Any beginner can find these resources efficiently and can develop their skills rapidly.  It is an open-sourced programming language with an OSI-approved license. Developers can use this language to create new operating codes, edit existing codes, make website developments, etc. Its free-of-cost feature allows it to be easily taught and learned by senior and junior programmers.  If you’re ever wondering why use Python to encrypt large amounts of data, then you must know that it has a broad selection of libraries and frameworks that store a vast amount of stacked data. It helps the developers to have access to such large libraries, which increases their productivity. The developers can manage web browsers, applications, and databases and can use them for data visualization, data analysis, scripting, prototyping, machine learning, data science, website optimization, and much more. It has a built-in dictionary with many data sets that can be used to build quick, less time-consuming, and efficient data structures. To create solutions for complex network applications, developers need speedy processing features which can increase text conversions and improve complex integrations. This programming language is easy to interpret. Since it does not require complex codings, it is easily interpreted. Unlike C++ and Java, which require separate codings to run them separately, Python follows line-by-line encryption. It can be coded and executed simultaneously. Learn data science courses online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career. Features of Python Language It is used to create mobile and desktop applications. It is used to develop website applications and cloud-based applications. It is used to build frameworks used on the web and run operating systems. It is used to structurize data science and data analysis applications.  It provides developmental support to software programs that run Artificial Intelligence (AI), Virtual Reality Assistance (VRA), Augmented Reality (AR), Deep Learning (DL), and Machine Learning (ML). Reasons to Learn Python Programming Language ‘Why learn Python?’ you must have wondered when so many other programming languages exist. Python offers several conveniences over other languages. It includes: 1. High-paying Job Opportunities Unlike other programming languages, which do not guarantee much stability with their learning and execution, Python, on the other hand, is a globally popular programming language. This means that this skill will provide opportunities in many good companies. Some companies that use Python programming language are as follows: Google Microsoft Yahoo Spotify Facebook Youtube Dropbox Quora Check our US - Data Science Programs Professional Certificate Program in Data Science and Business Analytics Master of Science in Data Science Master of Science in Data Science Advanced Certificate Program in Data Science Executive PG Program in Data Science Python Programming Bootcamp Professional Certificate Program in Data Science for Business Decision Making Advanced Program in Data Science 2. Easy Learning and Execution It is an open-sourced and free-of-cost programming language. It is accessible to all developers and budding design professionals. Its simple and easy-to-understand syntax allows developers to grasp its features quickly. It has an efficient execution as it does not require a large number of coded inputs. Instead, it uses codes line by line while executing them simultaneously. 3. It Covers Many General-purpose Functions Rather than having specialized inputs for specific outputs, Python uses a generic coding system that provides a wide range of executions. The functions are: Computation of mathematics and science. Finance and merchandising. Software developments for games, computer graphics, website applications, data analyst applications, etc. Administration of system structures and automation. Security strengthening and penetration testing. Geography and network mapping. Job Opportunities That You Can Get if You Learn Python Web Developer Individuals with Python skills can become Web Developers and Software Engineers. They will be required to write coding programs and test their execution. Data Scientist Their primary job is to find data sources and automate them. They will implement data visualizations, design models predictive of the trending market, and create predictive algorithms.  Development and Operations (DevOps) Engineer They will update the system and fix any recurring bugs. They will ensure that the programs run smoothly by checking their maintenance and analyzing them. They will create scripts for automation. They will also offer technical security and support for level 2.  Read our Popular US - Data Science Articles Data Analysis Course with Certification JavaScript Free Online Course With Certification Most Asked Python Interview Questions & Answers Data Analyst Interview Questions and Answers Top Data Science Career Options in the USA SQL Vs MySQL – What’s The Difference An Ultimate Guide to Types of Data Python Developer Salary in the US Data Analyst Salary in the US: Average Salary Machine Learning Engineer The primary aim of a Machine Learning Engineer is to create programs that can be run and tested on machines. Python is used for all computer-based systematic structures to create predicted algorithms and capable data automation. Full Stack Developer This requires the developer to be thorough in their concepts. They need to know all the functions of a front-end and a back-end developer. They should be well-versed in all the user experiences, developmental programs, codings, data visualizations, etc.  Conclusion The reason why Python is the best choice for learning programming languages is due to its efficient learning system and great execution results. Unlike other programming languages, which are too complex to understand for basic budding developers, Python is an easy and simple language that is free of cost. Developers should set a goal while learning Pthon so that each criterion is well touched upon.  For better assistance with efficient learning, you should check out upGrad’s Master of Science in Data Science course, offered under IIIT-Bangalore. Strengthening your foundation must be your number one priority while learning Python language, and upGrad intends to offer the same with this expert-led course. 
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by Rohit Sharma

16 Feb 2023

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