Home
Blog
Data Science
Binary Tree vs Binary Search Tree: Difference Between Binary Tree and Binary Search Tree

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

Updated on 17 May, 2024

64.23K+ views

• 15 min read

Rohit Sharma

Table of Contents

Introduction
What is a Binary Tree?
Terminologies used in Binary Tree
What is a Binary Search Tree
Binary Tree Vs. Binary Search Tree: Key Differences
Conclusion

Introduction

Sorting is the process of arranging the data in a systematic order so that it can be analysed more effectively. The process of identifying a particular record is called searching. If the data is properly sorted in a particular order, then searching becomes an easy and efficient task. This article deals with one of the most important non-linear data structures, i.e., trees.

Trees are primarily used to represent data by demonstrating a hierarchical relationship between the elements. For example, table of contents, family tree. Technically, a tree may be defined as a finite set ‘T’ of one or more nodes such that a node is assigned as the root of the tree and the other nodes are divided into n>=0 disjoint sets T1, T2, T3, T4 …. Tn and are called as the subtrees or children of that root node.

Now that we have discussed what trees essentially are, let us delve into binary search tree and binary tree. They play crucial computational roles in software development. Binary trees permit nodes to have a maximum of two children, offering versatility in node arrangements without inherent ordering. However, a binary search tree (BST) is a specialized tree enforcing a specific order.

This structural distinction between binary tree and binary search tree greatly impacts functionality. BSTs excel in efficient search, insertion, and deletion due to their ordered layout, whereas binary trees offer more flexibility in organizing data hierarchies.

Recognizing the difference between binary search tree and binary tree is vital for optimizing their use cases. Learn in detail about the difference between BST and BT in this blog.

What is a Binary Tree?

A binary tree is a non-linear data structure wherein a node can have either 0, 1 or 2 nodes. Each node in the binary tree is termed as either a parent node or as a child node. The topmost node of the Binary Tree is referred to as the root node. Each parent node can have at most 2 child nodes which are the left child node and the right child node.

Binary trees can look different from one another. A balanced binary tree, also known as an AVL tree in Java, is well-proportioned, where the difference in the heights of the left and right parts of each node is small. The balanced structure os an AVL tree in data structure helps with faster searches and organized data. However, an unbalanced binary tree can be skewered, causing slower operations like searching and adding new data.

A node in a binary tree has three fields:

Each node comprises three essential fields that define its structure and connections within the tree.

Data Element – It stores the data value that is to be stored by the node.

It could represent any information the tree is designed to hold, such as numbers, strings, objects, or other types of data.

Pointer to the left child – It stores the reference (or address) to the left-child node.

In computer memory, this pointer enables the node to link itself to its left child node. If a node doesn’t have a left child, this pointer typically holds a null value, indicating the absence of a left child.

Pointer to the right child – It store the reference to the right-child node.

It helps establish the connection between the current node and its right child node. Like the left child pointer, if a node lacks a right child, this pointer usually holds a null value, signifying no right child node.

These pointers or references are fundamental in constructing the hierarchical structure of a binary tree. They establish relationships between parent nodes and their respective child nodes, enabling navigation through the tree from one node to another.

In this way, several nodes are combined together to build a Binary Tree.

These pointers connect nodes based on their relationships (parent-child), helping organize a binary tree and implement traversal algorithms (like in-order, pre-order, or post-order traversal). This allows efficient access, manipulation, and management of data within the tree structure.

A Binary Tree can be represented as:

Source

From the above figure, the root node 2 has two children (or child nodes), 7 and 5. 7 is referred to as the left-child node and 5 is called as the right-child node. In this way, each of the child nodes act as a parent node to several other nodes and together represent the Binary Tree.

Check out: Types of Binary Tree

Terminologies used in Binary Tree

Understanding the terminologies used in a binary tree is fundamental to better grasp its structure and operations. Here’s a detailed list of these terms with their explanations:

Nodes are fundamental units within a tree data structure, representing individual elements or data points. Each node contains data (such as a value or an object) and pointers or references to its child nodes, if any. Nodes are interconnected through edges to form the hierarchical structure of the tree.

Root Node: The topmost node of a Binary Tree.

The root node serves as the starting or entry point for accessing and traversing the entire tree structure. All other nodes are descendants of the root node, and its depth is always 0 as it is the starting point of the tree.

Parent Node: If a node is connected to another node through edges, it is known as a parent node. In a binary tree, a parent node can have a maximum of 2 child-nodes.

Child Node: If a node has a predecessor, it is known as child node.

Each parent node can have up to two child nodes, one positioned to its left and the other to its right in a binary tree.

Leaf Node: A node that does not have any child node is called as a leaf node.

They exist at the lowest level of the tree hierarchy and represent endpoints or final elements within the tree structure.

Depth of a node: It is the distance from the root node to that particular node whose depth is to be measured.

Depth signifies the number of edges traversed along the path from the root node to the target node.

Depth of a node: It is the distance from the root node to that particular node whose depth is to be measured.

Height of the tree: It is the longest distance from the root node to the leaf node.

These are a few basic terminologies of a Binary Tree. With this basic understanding of a Binary Tree, let us move on to an advancement of Binary Tree known as the Binary Search Tree.

Understanding these terminologies provides a foundational understanding of the relationships between nodes and their positions within a binary tree. These concepts are essential in performing operations within the tree structure, such as traversal, searching, insertion, and deletion.

Also Read: Binary Search Algorithm: Function, Benefits, Time & Space Complexity

What is a Binary Search Tree

As the name suggests, a Binary Search Tree or BST is a tree that is used in sorting, retrieving and searching data. It is also a type of non-linear data structure in which the nodes are arranged in a particular order. Hence, it is also called as “Ordered Binary Tree”. It has the following properties:

Additionally, a crucial part of this tree structure is its ‘balanced’ nature, ensuring an even distribution of nodes. This enables efficient operations such as search, insertion, and deletion with time complexities typically near O(log n).

A balanced binary search tree (BST) is a structured data representation that follows a hierarchical format, where each node can have a maximum of two children. In this tree, the left child of a node holds values smaller than the node, while the right child holds values greater than the node.

All in all, a binary search tree is extensively used because it facilitates operational efficiency which aids in the ease of data management. It has the following properties:

The left subtree of a node has nodes which are only lesser than that node’s key.

This property ensures that all nodes to the left of a parent node have values smaller than the parent node’s value, establishing a sorted order within the left subtree.

The right subtree of a node has nodes which are only greater than that node’s key.

This property guarantees that all nodes to the right of a parent node have values larger than the parent node’s value, maintaining a sorted order within the right subtree.

Each node has distinct keys and duplicates are not allowed in Binary Search Tree.

This ensures that no two nodes within the tree have the same key. This uniqueness facilitates unambiguous searching and retrieval of specific values within the tree.

The left and right subtree must also be a binary search tree.

Another critical property of a BST is that both the left and right subtrees of any node in the tree must also qualify as binary search trees. This recursive property ensures that the BST structure applies not only to the root node but to every subtree within the entire tree. Every sub-tree abides by the rules of a binary search tree, with its own left and right subtrees following the criteria of lesser and greater values, respectively.

The properties of a binary search tree (BST) make it ideal for organizing data. The left side of a node has smaller values, the right side has larger values, and no duplicates are allowed. Each part of the tree, whether left or right, follows these rules, too. Because of this arrangement, finding, adding, and removing items in a BST is faster and easier. These properties ensure a balanced and ordered arrangement of elements, enhancing the speed and efficiency of various operations performed on the tree.

Must read: Learn excel online free!

Let us visualize this concept to get a clear understanding of Binary Search Trees.

Source

In the above figure, we see that the value of the root node is 8. With further scrutiny, it is observed that all the values in the left subtree are lesser than the value of the root node and all the values in the right subtree have values that are greater than the root node. Furthermore, it is noted that each value in the Binary Search Tree is unique and there are no duplicates. Thus, the properties of Binary Search Tree stated above are verified.

Explore our Popular Data Science Courses

Executive Post Graduate Programme in Data Science from IIITB	Professional Certificate Program in Data Science for Business Decision Making	Master of Science in Data Science from University of Arizona
Advanced Certificate Programme in Data Science from IIITB	Professional Certificate Program in Data Science and Business Analytics from University of Maryland	Data Science Courses

In yet another example, we see that though the left and right subtrees are binary search trees with unique values throughout the tree. The value at the leaf node in the left subtree is 12 which is greater than the root node value which is 12. Thus, this does not satisfy the property of the BST and hence, it is not a Binary Search Tree.

upGrad’s Exclusive Data Science Webinar for you –

How to Build Digital & Data Mindset

Our learners also read: Free Python Course with Certification

Top Data Science Skills to Learn to upskill

1	Data Analysis Online Courses	Inferential Statistics Online Courses
2	Hypothesis Testing Online Courses	Logistic Regression Online Courses
3	Linear Regression Courses	Linear Algebra for Analysis Online Courses

Search operation in a BST –

Consider a Binary Search Tree with the values given below. Let us understand how the search operation is performed to get 9 from the Binary Search Tree.

Source

In order to perform the binary search, we shall initially arrange all the integers in a sorted array. This is called as the search space. This search space shall consist of two pointers, called as the start and end pointers. The array of the above given Binary Search Tree is represented as:

The first step is to calculate the middle value of the array and compare it with the value that is to be searched, 9 in our case. This is done by calculating n/2, where n is the total number of elements in the array (BST) and it is 6. Thus, the 3rd element is the middle element which is 5.

Now that the middle element is compared with 9 and as it is not equal (greater), the searching operation will be performed on the right array. In this way, the search operation is reduced to half, as shown below:

In the next step, the middle element is calculated and is found to be 9, which matches our element to be searched.

Binary Tree Vs. Binary Search Tree: Key Differences

A binary tree and a binary search tree differ mainly in how they organize their elements. In a binary tree, there’s no specific rule for arranging nodes. Each node can have at most two children, but there’s no particular order in which the elements are placed.

This lack of ordering means that when you’re looking for something in a binary tree, you might have to check many nodes before finding what you need, which can take searching longer.

On the other hand, a binary search tree (BST) is all about order. It has a specific arrangement where the left child of a node holds values smaller than the node, and the right child holds values greater than the node. This organized structure makes searching much faster.

For example, if you’re searching for a value, you can quickly decide whether to go left or right based on the comparison with the current node. This ordered arrangement significantly reduces the time it takes to find elements, making operations like search, insertion, and deletion faster and more efficient.

Because of these key distinctions between binary search tree vs binary tree, BSTs are commonly used in scenarios where quick retrieval based on an ordered arrangement is crucial, like in databases or symbol tables. They are excellent for tasks where finding specific values rapidly is essential.

That being said, binary trees offer more flexibility and are used when ordered data is not the primary concern, such as in expression trees or file systems. Therefore, the core distinction between BST vs. binary tree lies in their organization method – one follows a specific order, while the other doesn’t – impacting how efficiently you can locate items within them.

Binary Tree vs Binary Search Tree – Table of Differences

Now that we have a basic understanding of both the Binary Tree and Binary Search Trees, let us quickly summarize some of the differences between both of them.

Basis for Comparison	Binary Tree	Binary Search Tree
Definition	A Binary Tree is a non-linear data structure in which a node can have 0, 1 or 2 nodes. Individually, each node consists of a left pointer, right pointer and data element.	A Binary Search Tree is an organized binary tree with a structured organization of nodes. Each subtree must also be of that particular structure.
Structure	There is no required organization structure of the nodes in the tree.	The values of left subtree of a particular node should be lesser than that node and the right subtree values should be greater.
Operations Performed	The operations that can be performed are deletion, insertion and traversal	As these are sorted binary trees, they are used for fast and efficient binary search, insertion and deletion.
Types	There are several types. Most common ones are the Complete Binary Tree, Full Binary Tree, Extended Binary Tree	The most popular ones are AVL Trees, Splay Trees, Tango Trees, T-Trees.

Conclusion

Thus, we infer that though both the Binary Tree and Binary Search Tree have a common hierarchical structure with a collection of nodes, they have several differences in their application. A Binary Tree is a basic structure with a simple rule that no parent must have more than 2 children whereas the Binary Search Tree is a variant of the binary tree following a particular order with which the nodes should be organized.

Read our popular Data Science Articles

Data Science Career Path: A Comprehensive Career Guide	Data Science Career Growth: The Future of Work is here	Why is Data Science Important? 8 Ways Data Science Brings Value to the Business
Relevance of Data Science for Managers	The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have	Top 6 Reasons Why You Should Become a Data Scientist
A Day in the Life of Data Scientist: What do they do?	Myth Busted: Data Science doesn’t need Coding	Business Intelligence vs Data Science: What are the differences?

The main binary search tree and binary tree difference boils down to their organization rules. A binary tree doesn’t follow a specific order, allowing nodes to have up to two children and offering flexibility. In contrast, a binary search tree arranges nodes so smaller values are on the left and larger ones on the right, making searching faster.

Binary search trees work well for organized data tasks like in databases, while binary trees are more flexible for various data representation needs. Understanding these differences helps choose the right tree structure based on specific requirements.

If you are curious to learn about Binary tree vs Binary search tree, check out IIIT-B & upGrad’s PG Diploma in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.

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.

Frequently Asked Questions (FAQs)

1. How can we traverse a Binary Search Tree?

Unlike linear data structures like arrays, linked lists, stacks, and queues, where we can traverse the data structure in a single way only, a Binary Search Tree gives us the liberty to traverse it in multiple ways. The most popular tree traversals are as follows: In an inorder traversal, we first traverse the left node of the tree, then the root node of the tree, and finally the right node of the tree. We follow the same fashion across all the subtrees as well. In a preorder traversal, we first traverse the root node of the tree and then the left and right node respectively. We follow the same fashion across all the subtrees as well. In a postorder traversal, we first traverse the left and right node of the tree respectively, and finally the root node of the tree. We follow the same fashion across all the subtrees as well.

2. What are the advantages and disadvantages of a BST?

The following are the advantages and disadvantages of a Binary Search Tree. The advantages are - Operations like insertion, deletion, and lookup can be performed in O(log n) time where n is the number of nodes. All the elements in a BST are sorted so we can easily traverse through a BST by simply using inorder traversal. BST can efficiently be used to design memory allocators to speed up the search process of memory blocks. The biggest disadvantage of a Binary Search Tree is that we must always use a balanced BST such as AVL and Red-Black Tree because if we do not use a balanced BST, our tree operations will not be performed in logarithmic time.

3. Differentiate between a full binary tree and a complete binary tree.

A full binary tree is a binary tree where all the nodes have child nodes between 0 and 2. In other words, a binary tree where all the nodes have at least 2 children nodes except leaf nodes is known as a full binary tree. On the other hand, a complete binary tree is a binary tree where every node is completely filled (exactly two children nodes) and the leaf nodes are located as left as possible.

4. What is the difference between binary tree and binary search tree?

A binary tree is a hierarchical data structure where each node has at most two children. A binary search tree (BST) is a specific type of binary tree where the left child of a node contains values smaller than the node, and the right child contains values larger than the node.

Did you find this article helpful?

Rohit Sharma

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

Our Trending Courses

MS in Data Science Post Graduate Programme in Data Science & AI (Executive) DBA in Emerging Technologies with concentration in Generative AI

Our Trending Skill

Data Analysis Inferential Statistics Logistic Regression Linear Regression Linear Algebra for Analysis Hypothesis Testing

Get Free Consultation

By clicking "Submit" you Agree toupGrad's Terms & Conditions

FREE COURSES

Start Learning For Free

Case Study using Tableau, Python and SQL

10.58K+ learners

10 hrs of learning

Learn now

Introduction to Tableau

8.5K+ learners

8 hrs of learning

Learn now

Data Science in E-commerce

SUGGESTED BLOGS

5.64K+

Announcing PG Diploma in Data Analytics with IIIT Bangalore

Data is in abundance and for corporations, big or small, investment in data analytics is no more a discretionary spend, but a mandatory investment for competitive advantage. In fact, by 2019, 90% of large organizations will have a Chief Data Officer. Indian data analytics industry alone is expected to grow to $2.3 billion by 2017-18. UpGrad’s survey also shows that leaders across industries are looking at data as a key growth driver in the future and believe that the data analytics wave is here to stay. Learn Data Science Courses online at upGrad This growth wave has created a critical supply-demand imbalance of professionals with the adequate know-how of making data-driven decisions. The scarcity exists across Data Engineers, Data Analysts and becomes more acute when it comes to Data Scientists. As a result of this imbalance, India will face an acute shortage of at least 2 lac data skilled professionals over the next couple of years. upGrad’s Exclusive Data Science Webinar for you – Transformation & Opportunities in Analytics & Insights document.createElement('video'); https://cdn.upgrad.com/blog/jai-kapoor.mp4 In pursuit of bridging this gap, UpGrad has partnered with IIIT Bangalore, to deliver a first-of-its-kind online PG Diploma program in Data Analytics, which over the years will train 10,000 professionals. Offering a perfect mix of academic rigor and industry relevance, the program is meant for all those working professionals who wish to accelerate their career in data analytics. Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? Top Data Science Skills to Learn SL. No Top Data Science Skills to Learn 1 Data Analysis Programs Inferential Statistics Programs 2 Hypothesis Testing Programs Logistic Regression Programs 3 Linear Regression Programs Linear Algebra for Analysis Programs The Advanced Certificate Programme in Data Science at UpGrad will include modules in Statistics, Data Visualization & Business Intelligence, Predictive Modeling, Machine Learning, and Big Data. Additionally, the program will feature a 3-month project where students will work on real industry problems in a domain of their choice. The first batch of the program is scheduled to start on May 2016. Explore our Popular Data Science Certifications Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Certifications Our learners also read: Learn Python Online Course Free

by Rohit Sharma

08 Feb'16

5.09K+

How Organisations can Benefit from Bridging the Data Scientist Gap

Note: The article was originally written for LinkedIn Pulse by Sameer Dhanrajani, Business Leader at Cognizant Technology Solutions. Data Scientist is one of the fastest-growing and highest paid jobs in technology industry. Dr. Tara Sinclair, Indeed.com’s chief economist, said the number of job postings for “data scientist” grew 57% year-over-year in Q1:2015. Yet, in spite of the incredibly high demand, it’s not entirely clear what education someone needs to land one of these coveted roles. Do you get a degree in data science? Attend a bootcamp? Take a few Udemy courses and jump in? Learn data science to gain edge over your competitors It depends on what practice you end up it. Data Sciences has become a widely implemented phenomenon and multiple companies are grappling to build a decent DS practice in-house. Usually online courses, MOOCs and free courseware usually provides the necessary direction for starters to get a clear understanding, quickly for execution. But Data Science practice, which involves advanced analytics implementation, with a more deep-level exploratory approach to implementing Data Analytics, Machine Learning, NLP, Artificial Intelligence, Deep Learning, Prescriptive Analytics areas would require a more establishment-centric, dedicated and extensive curriculum approach. A data scientist differs from a business analyst ;data scientist requires dwelling deep into data and gathering insights, intelligence and recommendations that could very well provide the necessary impetus and direction that a company would have to take, on a foundational level. And the best place to train such deep-seeded skill would be a university-led degree course on Data Sciences. It’s a well-known fact that there is a huge gap between the demand and supply of data scientist talent across the world. Though it has taken some time, but educationalists all across have recognized this fact and have created unique blends of analytics courses. Every month, we hear a new course starting at a globally recognized university. Data growth is headed in one direction, so it’s clear that the skills gap is a long-term problem. But many businesses just can’t wait the three to five years it might take today’s undergrads to become business-savvy professionals. Hence this aptly briefs an alarming need of analytics education and why universities around the world are scrambling to get started on the route towards being analytics education leaders. Obviously, the first mover advantage would define the best courses in years to come i.e. institutes that take up the data science journey sooner would have a much mature footing in next few years and they would find it easier to attract and place students. Strategic Benefits to implementing Data Science Degrees Data science involves multiple disciplines The reason why data scientists are so highly sought after, is because the job is really a mashup of different skill sets and competencies rarely found together. Data scientists have tended to come from two different disciplines, computer science and statistics, but the best data science involves both disciplines. One of the dangers is statisticians not picking up on some of the new ideas that are coming out of machine learning, or computer scientists just not knowing enough classical statistics to know the pitfalls. Even though not everything can be taught in a Degree course, universities should clearly understand the fact that training a data science graduate would involve including multiple, heterogeneous skills as curriculum and not one consistent courseware. They might involve computer science, mathematics, statistics, business understanding, insight interpretation, even soft skills on data story telling articulation. Beware of programs that are only repackaging material from other courses Because data science involves a mixture of skills — skills that many universities already teach individually — there’s a tendency toward just repackaging existing courses into a coveted “data science” degree. There are mixed feelings about such university programs. It seems to me that they’re more designed to capitalize on the fact that the demand is out there than they are in producing good data scientists. Often, they’re doing it by creating programs that emulate what they think people need to learn. And if you think about the early people who were doing this, they had a weird combination of math and programming and business problems. They all came from different areas. They grew themselves. The universities didn’t grow them. Much of a program’s value comes from who is creating and choosing its courses. There have been some decent course guides in the past from some universities, it’s all about who designs the program and whether they put deep and dense content and coverage into it, or whether they just think of data science as exactly the same as the old sort of data mining. The Theories on Theory A recurring theme throughout my conversations was the role of theory and its extension to practical approaches, case studies and live projects. A good recommendation to aspiring data scientists would be to find a university that offers a bachelor’s degree in data science. Learn it at the bachelor’s level and avoid getting mired in only deep theory at the PostGrad level. You’d think the master’s degree dealing with mostly theory would be better, but I don’t think so. By the time you get to the MS you’re working with the professors and they want to teach you a lot of theory. You’re going to learn things from a very academic point of view, which will help you, but only if you want to publish theoretical papers. Hence, universities, especially those framing a PostGrad degree in Data Science should make sure not to fall into orchestrating a curriculum with a long drawn theory-centric approach. Also, like many of the MOOCs out there, a minimum of a capstone project would be a must to give the students a more pragmatic view of data and working on it. It’s important to learn theory of course. I know too many ‘data scientists’ even at places like Google who wouldn’t be able to tell you what Bayes’ Theorem or conditional independence is, and I think data science unfortunately suffers from a lack of rigor at many companies. But the target implementation of the students, which would mostly be in corporate houses, dealing with real consumer or organizational data, should be finessed using either simulated practical approach or with collaboration with Data Science companies to give an opportunity to students to deal with real life projects dealing with data analysis and drawing out actual business insights. Our learners also read: Free Python Course with Certification upGrad’s Exclusive Data Science Webinar for you – ODE Thought Leadership Presentation document.createElement('video'); https://cdn.upgrad.com/blog/ppt-by-ode-infinity.mp4 Explore our Popular Data Science Online Certifications Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Online Certifications Don’t Forget About the Soft Skills In an article titled The Hard and Soft Skills of a Data Scientist, Todd Nevins provides a list of soft skills becoming more common in data scientist job requirements, including: Manage teams and projects across multiple departments on and offshore. Consult with clients and assist in business development. Take abstract business issues and derive an analytical solution. Top Data Science Skills You Should Learn SL. No Top Data Science Skills to Learn 1 Data Analysis Online Certification Inferential Statistics Online Certification 2 Hypothesis Testing Online Certification Logistic Regression Online Certification 3 Linear Regression Certification Linear Algebra for Analysis Online Certification The article also emphasizes the importance of these skills, and criticizes university programs for often leaving these skills out altogether: “There’s no real training about how to talk to clients, how to organize teams, or how to lead an analytics group.” Data science is still a rapidly evolving field and until the norms are more established, it’s unlikely every data scientist will be following the same path. A degree in data science will definitely act as the clay to make your career. But the part that really separates people who are successful from that are not is just a core curiosity and desire to answer questions that people have — to solve problems. Don’t do it because you think you can make a lot of money, chances are by the time you’re trained, you either don’t know the right stuff or there’s a hundred other people competing for the same position, so the only thing that’s going to stand out is whether you really like what you’re doing. Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences?

by Ashish Korukonda

03 May'16

5.13K+

Computer Center turns Data Center; Computer Science turns Data Science

(This article, written by Prof. S. Sadagopan, was originally published in Analytics India Magazine) There is an old “theory” that talks of “power shift” from “carrier” to “content” and to “control” as industry matures. Here are some examples In the early days of Railways, “action” was in “building railroads”; the “tycoons” who made billions were those “railroad builders”. Once enough railroads were built, there was more action in building “engines and coaches” – General Electric and Bombardier emerged; “power” shifted from “carrier” to “content”; still later, action shifted to “passenger trains” and “freight trains” – AmTrak and Delhi Metro, for example, that used the rail infrastructure and available engines and coaches / wagons to offer a viable passenger / goods transportation service; power shifted from “content” to “control”. The story is no different in the case of automobiles; “carrier” road-building industry had the limelight for some years, then the car and truck manufacturers – “content” – GM, Daimler Chrysler, Tata, Ashok Leyland and Maruti emerged – and finally, the “control”, transport operators – KSRTC in Bangalore in the Bus segment to Uber and Ola in the Car segment. In fact, even in the airline industry, airports become the “carrier”, airplanes are the “content” and airlines represent the “control” Learn data science courses from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career. It is a continuum; all three continue to be active – carrier, content and control – it is just the emphasis in terms of market and brand value of leading companies in that segment, profitability, employment generation and societal importance that shifts. We are witnessing a similar “power shift” in the computer industry. For nearly six decades the “action” has been on the “carrier”, namely, computers; processors, once proprietary from the likes of IBM and Control Data, then to microprocessors, then to full blown systems built around such processors – mainframes, mini computers, micro computers, personal computers and in recent times smartphones and Tablet computers. Intel and AMD in processors and IBM, DEC, HP and Sun dominated the scene in these decades. A quiet shift happened with the arrival of “independent” software companies – Microsoft and Adobe, for example and software services companies like TCS and Infosys. Along with such software products and software services companies came the Internet / e-Commerce companies – Yahoo, Google, Amazon and Flipkart; shifting the power from “carrier” to “content”. Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses This shift was once again captured by the use of “data center” starting with the arrival of Internet companies and the dot-com bubble in late nineties. In recent times, the term “cloud data center” is gaining currency after the arrival of “cloud computing”. Though interest in computers started in early fifties, Computer Science took shape only in seventies; IITs in India created the first undergraduate program in Computer Science and a formal academic entity in seventies. In the next four decades Computer Science has become a dominant academic discipline attracting the best of the talent, more so in countries like India. With its success in software services (with $ 160 Billion annual revenue, about 5 million direct jobs created in the past 20 years and nearly 7% of India’s GDP), Computer Science has become an aspiration for hundreds of millions of Indians. With the shift in “power” from “computers” to “data” – “carrier” to “content” – it is but natural, that emphasis shifts from “computer science” to “data science” – a term that is in wide circulation only in the past couple of years, more in corporate circles than in academic institutions. In many places including IIIT Bangalore, the erstwhile Database and Information Systems groups are getting re-christened as “Data Science” groups; of course, for many acdemics, “Data Science” is just a buzzword, that will go “out of fashion” soon. Only time will tell! As far as we are concerned, the arrival of data science represents the natural progression of “analytics”, that will use the “data” to create value, the same way Metro is creating value out of railroad and train coaches or Uber is creating value out of investments in road and cars or Singapore Airlines creating value out of airport infrastructure and Boeing / Airbus planes. More important, the shift from “carrier” to “content” to “control” also presents economic opportunities that are much larger in size. We do expect the same from Analytics as the emphasis shifts from Computer Science to Data Science to Analytics. Computers originally created to “compute” mathematical tables could be applied to a wide range of problems across every industry – mining and machinery, transportation, hospitality, manufacturing, retail, banking & financial services, education, healthcare and Government; in the same vein, Analytics that is currently used to summarize, visualize and predict would be used in many ways that we cannot even dream of today, the same way the designers of computer systems in 60’s and 70’s could not have predicted the varied applications of computers in the subsequent decades. We are indeed in exciting times and you the budding Analytics professional could not have been more lucky. Announcing PG Diploma in Data Analytics with IIT Bangalore – To Know more about the Program Visit – PG Diploma in Data Analytics. Top Data Science Skills to Learn to upskill SL. No Top Data Science Skills to Learn 1 Data Analysis Online Courses Inferential Statistics Online Courses 2 Hypothesis Testing Online Courses Logistic Regression Online Courses 3 Linear Regression Courses Linear Algebra for Analysis Online Courses upGrad’s Exclusive Data Science Webinar for you – ODE Thought Leadership Presentation document.createElement('video'); https://cdn.upgrad.com/blog/ppt-by-ode-infinity.mp4 Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? Our learners also read: Free Online Python Course for Beginners About Prof. S. Sadagopan Professor Sadagopan, currently the Director (President) of IIIT-Bangalore (a PhD granting University), has over 25 years of experience in Operations Research, Decision Theory, Multi-criteria optimization, Simulation, Enterprise computing etc. His research work has appeared in several international journals including IEEE Transactions, European J of Operational Research, J of Optimization Theory & Applications, Naval Research Logistics, Simulation and Decision Support Systems. He is a referee for several journals and serves on the editorial boards of many journals.

by Prof. S. Sadagopan

11 May'16

5.19K+

Enlarge the analytics & data science talent pool

Note: The articlewas originally written by Sameer Dhanrajani, Business Leader at Cognizant Technology Solutions. A Better Talent acquisition Framework Although many articles have been written lamenting the current talent shortage in analytics and data science, I still find that the majority of companies could improve their success by simply revamping their current talent acquisition processes. 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. We’re all well aware that strong quantitative professionals are few and far between, so it’s in a company’s best interest to be doing everything in their power to land qualified candidates as soon as they find them. It’s a candidate’s market, with strong candidates going on and off the market lightning fast, yet many organizational processes are still slow and outdated. These sluggish procedures are not equipped to handle many candidates who are fielding multiple offers from other companies who are just as hungry (if not more so) for quantitative talent. Here are the key areas I would change to make hiring processes more competitive: Fix your salary bands – It (almost) goes without saying that if your salary offerings are outdated or aren’t competitive to the field, it will be difficult for you to get the attention of qualified candidates; stay topical with relevant compensation grids. Consider one-time bonuses – Want to make your offer compelling but can’t change the salary? Sign-on bonuses and relocation packages are also frequently used, especially near the end of the year, when a candidate is potentially walking away from an earned bonus; a sign-on bonus can help seal the deal. Be open to other forms of compensation – There are plenty of non-monetary ways to entice Quants to your company, like having the latest tools, solving challenging problems, organization-wide buy-in for analytics and more. Other things to consider could be flexible work arrangements, remote options or other unique perks. Pick up the pace – Talented analytics professionals are rare, and the chances that qualified candidates will be interviewing with multiple companies are very high. Don’t hesitate to make an offer if you find what you’re looking for at a swift pace – your competitors won’t. Court the candidate – Just as you want a candidate who stands out from the pack, a candidate wants a company that makes an effort to stand apart also. I read somewhere, a client from Chicago sent an interviewing candidate and his family pizzas from a particularly tasty restaurant in the city. I can’t say for sure that the pizza was what persuaded him to take the company’s offer, but a little old-fashioned wooing never hurts. Button up the process – Just as it helps to have an expedited process, it also works to your benefit is the process is as smooth and trouble-free as you can make it. This means hassle-free travel arrangements, on-time interviews, and quick feedback. Network – make sure that you know the best of the talent available in the market at all levels and keep in touch with them thru porfessional social sites on subtle basis as this will come handy in picking the right candidate on selective basis Redesigned Interview Process In the old days one would screen resumes and then schedule lots of 1:1’s. Typically people would ask questions aimed at assessing a candidate’s proficiency with stats, technicality, and ability to solve problems. But there were three problems with this – the interviews weren’t coordinated well enough to get a holistic view of the candidate, we were never really sure if their answers would translate to effective performance on the job, and from the perspective of the candidate it was a pretty lengthy interrogation. So, a new interview process need to be designed that is much more effective and transparent – we want to give the candidate a sense for what a day in the life of a member on the team is like, and get a read on what it would be like to work with a company. In total it takes about two days to make a decision, and there be no false positives (possibly some false negatives though), and the feedback from both the candidates and the team members has been positive. There are four steps to the process: Resume/phone screens – look for people who have experience using data to drive decisions, and some knowledge of what your company is all about. On both counts you’ll get a much deeper read later in the process; you just want to make sure that moving forward is a good use of either of both of your time. Basic data challenge – The goal here is to validate the candidate’s ability to work with data, as described in their resume. So send a few data sets to them and ask a basic question; the exercise should be easy for anyone who has experience. In-house data challenge – This is should be the meat of the interview process. Try to be as transparent about it as possible – they’ll get to see what it’s like working with you and vice versa. So have the candidate sit with the team, give them access to your data, and a broad question. They then have the day to attack the problem however they’re inclined, with the support of the people around them. Do encourage questions, have lunch with them to ease the tension, and check-in periodically to make sure they aren’t stuck on something trivial. At the end of the day, we gather a small team together and have them present their methodology and findings to you. Here, look for things like an eye for detail (did they investigate the data they’re relying upon for analysis), rigor (did they build a model and if so, are the results sound), action-oriented (what would we do with what you found), and communication skills. Read between the resume lines Intellectual curiosity is what you should discover from the project plans. It’s what gives the candidate the ability to find loopholes or outliers in data that helps crack the code to find the answers to issues like how a fraudster taps into your system or what consumer shopping behaviors should be considered when creating a new product marketing strategy. Data scientists find the opportunities that you didn’t even know were in the realm of existence for your company. They also find the needle in the haystack that is causing a kink in your business – but on an entirely monumental scale. In many instances, these are very complex algorithms and very technical findings. However, a data scientist is only as good as the person he must relay his findings to. Others within the business need to be able to understand this information and apply these insights appropriately. Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses Good data scientists can make analogies and metaphors to explain the data but not every concept can be boiled down in layman’s terms. A space rocket is not an automobile and, in the brave new world, everyone must make this paradigm shift. Top Data Science Skills You Should Learn SL. No Top Data Science Skills to Learn 1 Data Analysis Online Certification Inferential Statistics Online Certification 2 Hypothesis Testing Online Certification Logistic Regression Online Certification 3 Linear Regression Certification Linear Algebra for Analysis Online Certification upGrad’s Exclusive Data Science Webinar for you – Watch our Webinar on The Future of Consumer Data in an Open Data Economy document.createElement('video'); https://cdn.upgrad.com/blog/sashi-edupuganti.mp4 Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? Our learners also read: Free Python Course with Certification And lastly, the data scientist you’re looking for needs to have strong business acumen. Do they know your business? Do they know what problems you’re trying to solve? And do they find opportunities that you never would have guessed or spotted?

by upGrad

14 May'16

5.69K+

UpGrad partners with Analytics Vidhya

We are happy to announce our partnership with Analytics Vidhya, a pioneer in the Data Science community. Analytics Vidhya is well known for its impressive knowledge base, be it the hackathons they organize or tools and frameworks that they help demystify. In their own words, “Analytics Vidhya is a passionate community for Analytics/Data Science professionals, and aims at bringing together influencers and learners to augment knowledge”. Explore our Popular Data Science Degrees Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Degrees We are joining hands to provide candidates of our PG Diploma in Data Analytics, an added exposure to UpGrad Industry Projects. While the program already covers multiple case studies and projects in the core curriculum, these projects with Analytics Vidhya will be optional for students to help them further hone their skills on data-driven problem-solving techniques. To further facilitate the learning, Analytics Vidhya will also be providing mentoring sessions to help our students with the approach to these projects. Our learners also read: Free Online Python Course for Beginners Top Essential Data Science Skills to Learn SL. No Top Data Science Skills to Learn 1 Data Analysis Certifications Inferential Statistics Certifications 2 Hypothesis Testing Certifications Logistic Regression Certifications 3 Linear Regression Certifications Linear Algebra for Analysis Certifications This collaboration brings great value to the program by allowing our students to add another dimension to their resume which goes beyond the capstone projects and case studies that are already a part of the program. Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? Through this, we hope our students would be equipped to showcase their ability to dissect any problem statement and interpret what the model results mean for business decision making. This also helps us to differentiate UpGrad-IIITB students in the eyes of the recruiters. upGrad’s Exclusive Data Science Webinar for you – Transformation & Opportunities in Analytics & Insights document.createElement('video'); https://cdn.upgrad.com/blog/jai-kapoor.mp4 Check out our data science training to upskill yourself

by Omkar Pradhan

09 Oct'16

5.69K+

Data Analytics Student Speak: Story of Thulasiram

When Thulasiram enrolled in the UpGrad Data Analytics program, in its first cohort, he was not very different for us, from the rest of our students in this. While we still do not and should not treat learners differently, being in the business of education – we definitely see this particular student in a different light. His sheer resilience and passion for learning shaped his success story at UpGrad. Humble beginnings Born in the small town of Chittoor, Andhra Pradesh, Thulasiram does not remember much of his childhood given that he enlisted in the Navy at a very young age of about 15 years. Right out of 10th standard, he trained for four years, acquiring a diploma in mechanical engineering. Thulasiram came from humble means. His father was the manager of a small general store and his mother a housewife. It’s difficult to dream big when leading a sheltered life with not many avenues for exposure to unconventional and exciting opportunities. But you can’t take learning out of the learner. “One thing I remember about school is our Math teacher,” reminisces Thulasiram, “He used to give us lot of puzzles to solve. I still remember one puzzle. If you take a chessboard and assume that all pawns are queens; you have to arrange them in such a way that none of the eight pawns should die. Every queen, should not affect another queen. It was a challenging task, but ultimately we did it, we solved it.” Navy & MBA At 35 years of age, Thulasiram has been in the navy for 19 years. Presently, he is an instructor at the Naval Institute of Aeronautical Technology. “I am from the navy and a lot of people don’t know that there is an aviation wing too. So, it’s like a dream; when you are a small child, you never dream of touching an aircraft, let alone maintaining it. I am very proud of doing this,” says Thulasiram on taking the initiative to upskill himself and becoming a naval-aeronautics instructor. When the system doesn’t push you, you have to take the initiative yourself. Thulasiram imbibed this attitude. He went on to enroll in an MBA program and believes that the program drastically helped improve his communication skills and plan his work better. How Can You Transition to Data Analytics? Data Analytics Like most of us, Thulasiram began hearing about the hugely popular and rapidly growing domain of data analytics all around him. Already equipped with the DNA of an avid learner and keen to pick up yet another skill, Thulasiram began researching the subject. He soon realised that this was going to be a task more rigorous and challenging than any he had faced so far. It seemed you had to be a computer God, equipped with analytical, mathematical, statistical and programming skills as prerequisites – a list that could deter even the most motivated individuals. This is where Thulsiram’s determination set him apart from most others. Despite his friends, colleagues and others that he ran the idea by, expressing apprehension and deterring him from undertaking such a program purely with his interests in mind – time was taken, difficulty level, etc. – Thulasiram, true to the spirit, decided to pursue it anyway. Referring to the crucial moment when he made the decision, he says, If it is easy, everybody will do it. So, there is no fun in doing something which everybody can do. I thought, let’s go for it. Let me push myself — challenge myself. Maybe, it will be a good challenge. Let’s go ahead and see whether I will be able to do it or not. UpGrad Having made up his mind, Thulasiram got straight down to work. After some online research, he decided that UpGrad’s Data Analytics program, offered in collaboration with IIIT-Bangalore that awarded a PG Diploma on successful completion, was the way to go. The experience, he says, has been nothing short of phenomenal. It is thrilling to pick up complex concepts like machine learning, programming, or statistics within a matter of three to four months – a feat he deems nearly impossible had the source or provider been one other than UpGrad. Our learners also read: Top Python Free Courses Favorite Elements Ask him what are the top two attractions for him in this program and, surprising us, he says deadlines! Deadlines and assignments. He feels that deadlines add the right amount of pressure he needs to push himself forward and manage time well. As far as assignments are concerned, Thulasiram’s views resonate with our own – that real-life case studies and application-based learning goes a long way. Working on such cases and seeing results is far superior to only theoretical learning. He adds, “flexibility is required because mostly only working professionals will be opting for this course. You can’t say that today you are free, because tomorrow some project may be landing in your hands. So, if there is no flexibility, it will be very difficult. With flexibility, we can plan things and maybe accordingly adjust work and family and studies,” giving the UpGrad mode of learning, yet another thumbs-up. Amongst many other great things he had to say, Thulasiram was surprised at the number of live sessions conducted with industry professionals/mentors every week. Along with the rest of his class, he particularly liked the one conducted by Mr. Anand from Gramener. Top Data Science Skills to Learn to upskill SL. No Top Data Science Skills to Learn 1 Data Analysis Online Courses Inferential Statistics Online Courses 2 Hypothesis Testing Online Courses Logistic Regression Online Courses 3 Linear Regression Courses Linear Algebra for Analysis Online Courses What Kind of Salaries do Data Scientists and Analysts Demand? Get data science certification from the World’s top Universities. Learn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career. Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? upGrad’s Exclusive Data Science Webinar for you – ODE Thought Leadership Presentation document.createElement('video'); https://cdn.upgrad.com/blog/ppt-by-ode-infinity.mp4 Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses “Have learned most here, only want to learn..” Interested only in learning, Thulasiram made this observation about the program – compared to his MBA or any other stage of life. He signs off calling it a game-changer and giving a strong recommendation to UpGrad’s Data Analytics program. We are truly grateful to Thulasiram and our entire student community who give us the zeal to move forward every day, with testimonials like these, and make the learning experience more authentic, engaging, and truly rewarding for each one of them. If you are curious to learn about data analytics, data science, check out IIIT-B & upGrad’s PG Diploma in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.

by Apoorva Shankar

07 Dec'16

5.12K+

Decoding Easy vs. Not-So-Easy Data Analytics

Authored by Professor S. Sadagopan, Director – IIIT Bangalore. Prof. Sadagopan is one of the most experienced academicians on the expert panel of UpGrad & IIIT-B PG Diploma Program in Data Analytics. As a budding analytics professional confounded by jargon, hype and overwhelming marketing messages that talk of millions of upcoming jobs that are paid in millions of Rupees, you ought to get clarity about the “real” value of a data analytics education. Here are some tidbits – that should hopefully help in reducing your confusion. Some smart people can use “analytical thinking” to come up with “amazing numbers”; they are very useful but being “intuitive”, they cannot be “taught.” For example: Easy Analytics Pre-configuring ATMs with Data Insights “We have the fastest ATM on this planet” Claimed a respected Bank. Did they get a new ATM made especially for them? No way. Some smart employee with an analytical mindset found that 90% of the time that users go to an ATM to withdraw cash, they use a fixed amount, say Rs 5,000. So, the Bank re-configured the standard screen options – Balance Inquiry, Withdrawal, Print Statement etc. – to include another option. Withdraw XYZ amount, based on individual customer’s past actions. This ended up saving one step of ATM operation. Instead of selecting the withdrawal option and then entering the amount to be withdrawn, you could now save some time – making the process more convenient and intuitive. A smart move indeed, however, this is something known as “Easy Analytics” that others can also copy. In fact, others DID copy, within three months! A Start-Up’s Guide to Data Analytics Hidden Data in the Weather In the sample data-sets that used to accompany a spreadsheet product in the 90’s, there used to be data on the area and population of every State in the United States. There was also an exercise to teach the formula part of the spreadsheet to compute the population density (population per sq. km). New Jersey, with a population of 467 per sq. km, is the State with the highest density. While teaching a class of MBA students in New Jersey, I met an Indian student who figured out that in terms of population density, New Jersey is more crowded than India with 446 people per sq. km! An interesting observation, although comparing a State with a Country is a bit misleading. Once again, an Easy Analytics exercise leading to a “nice” observation! Some simple data analytics exercises can be routinely done, and are made relatively easier, thanks to amazing tools: B-School Buying Behavior Decoded In a B-School in India that has a store on campus, (campus is located far from the city center) some smart students put several years of sales data of their campus store. They were excited by the phenomenal computer power and near, idiot-proof analytics software. The real surprise, however, was that eight items accounted for 85% of their annual sales. More importantly, these eight items were consumed in just six days of the year! Everyone knew that a handful of items were the only fast-moving items, but they did not know the extent (85%) or the intensity (consumption in just six days) of this. It turns out that in the first 3 days of the semester the students would stock the items for the full semester! The B-School found it sensible to request a nearby store to prop up a temporary stall for just two weeks at the beginning of the semesters and close down the Campus Store. This saved useful space and costs without causing major inconvenience to the students. A good example of Easy Analytics done with the help of a powerful tool. Top 4 Data Analytics Skills You Need to Become an Expert! The “Not So Easy” Analytics needs deep analytical understanding, tools, an ‘analytical mindset’ and some hard work. Here are two examples, one taken from way back in the 70’s and the other occurring very recently: Not-So-Easy Analytics To Fly or Not to Fly, That is the Question Long ago, the American Airlines perfected planned overbooking of airline seats, thanks to SABRE Airline Reservation system that managed every airline seat. Armed with detailed past data of ‘empty seats’ and ‘no show’ in every segment of every flight for every day through the year, and modeling airline seats as perishable commodities, the American Airlines was able to improve yield, i.e., utilization of airplane capacity. They did this through planned overbooking – selling more tickets than the number of seats, based on projected cancellations. Explore our Popular Data Science Online Certifications Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Online Certifications If indeed more passengers showed up than the actual number of seats, American Airlines would request anyone volunteering to forego travel in the specific flight, with the offer to fly them by the next flight (often free) and taking care of hotel accommodation if needed. Sometimes, they would even offer cash incentives to the volunteer to opt-out. Using sophisticated Statistical and Operational Research modeling, American Airlines would ensure that the flights went full and the actual incidents of more passengers than the full capacity, was near zero. In fact, many students would look forward to such incidents so that they could get incentives, (in fact, I would have to include myself in this list) but rarely were they rewarded!) upGrad’s Exclusive Data Science Webinar for you – Transformation & Opportunities in Analytics & Insights document.createElement('video'); https://cdn.upgrad.com/blog/jai-kapoor.mp4 What American Airlines started as an experiment has become the standard industry practice over the years. Until recently, a team of well-trained (often Ph.D. degree holders) analysts armed with access to enormous computing power, was needed for such an analytics exercise to be sustained. Now, new generation software such as the R Programming language and powerful desktop computers with significant visualization/graphics power is changing the world of data analytics really fast. Anyone who is well-trained (not necessarily requiring a Ph.D. anymore) can become a first-rate analytics professional. Top Data Science Skills You Should Learn SL. No Top Data Science Skills to Learn 1 Data Analysis Online Certification Inferential Statistics Online Certification 2 Hypothesis Testing Online Certification Logistic Regression Online Certification 3 Linear Regression Certification Linear Algebra for Analysis Online Certification Unleashing the Power of Data Analytics Our learners also read: Free Python Course with Certification Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? Cab Out of the Bag Uber is yet another example displaying how the power of data analytics can disrupt a well-established industry. Taxi-for-sure in Bangalore and Ola Cabs are similar to Uber. Together, these Taxi-App companies (using a Mobile App to hail a taxi, the status monitor the taxi, use and pay for the taxi) are trying to convince the world to move from car ownership to on-demand car usage. A simple but deep analytics exercise in the year 2008 gave such confidence to Uber that it began talking of reducing car sales by 25% by the year 2025! After building the Uber App for iPhone, the Uber founder enrolled few hundreds of taxi customers in San Francisco and few hundreds of taxi drivers in that area as well. All that the enrolled drivers had to do was to touch the Uber App whenever they were ready for a customer. Similarly, the enrolled taxi customers were requested to touch the Uber App whenever they were looking for a taxi. Thanks to the internet-connected phone (connectivity), Mobile App (user interface), GPS (taxi and end-user location) and GIS (location details), Uber could try connecting the taxi drivers and the taxi users. The real insight was that nearly 90% of the time, taxi drivers found a customer, less than 100 meters away! In the same way, nearly 90% of the time, taxi users were connected with their potential drivers in no time, not too far away. Unfortunately, till the Uber App came into existence, riders and taxi drivers had no way of knowing this information. More importantly, they both had no way of reaching each other! Once they had this information and access, a new way of taxi-hailing could be established. With back-end software to schedule taxis, payment gateway and a mobile payment mechanism, a far more superior taxi service could be established. Of course, near home, we had even better options like Taxi-for-sure trying to extend this experience even to auto rickshaws. The rest, as they say, is “history in the making!” Deep dive courses in data analytics will help prepare you for such high impact applications. It is not easy, but do remember former US President Kennedy’s words “we chose to go to the Moon not because it is easy, but because it is hard!” Get data science certification from the World’s top Universities. Learn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career.

by Prof. S. Sadagopan

14 Dec'16

5.14K+

Launching UpGrad’s Data Analytics Roadshow – Are You Game?

We, at UpGrad, are excited to announce a brand new partnership with various thought leaders in the Data Analytics industry – IIIT Bangalore, Genpact, Analytics Vidhya and Gramener – to bring to you a one-of-a-kind Analytics Roadshow! As part of this roadshow, we will be conducting several back-to-back events that focus on different aspects of analytics, creating interaction points across India, to do our bit for a future ready and analytical, young workforce. Also Read: Analytics Vidhya article on the UpGrad Data Analytics Roadshow Here is the line-up for the roadshow, to give you a better sense of what to expect: 9 webinars – These webinars (remote) will be conducted by industry experts and are aimed at increasing analytics awareness, providing a way for aspirants to interact with industry practitioners and getting their tough questions answered. 11 workshops – The workshops will be in-person events to take these interactions to the next level. These would be spread across 6 cities – Delhi, Bengaluru, Hyderabad, Chennai, Mumbai and Pune. So, if you are in any of these cities, we are looking forward to interact with you. Featured Data Science program for you: Master of Science in Data Science from from IIIT-B 2 Conclaves – These conclaves are larger events with a pre-defined agendas and time for networking. The first conclave is happening on the 17th of December in Bengaluru. Explore our Popular Data Science Online Certifications Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Online Certifications Hackathon – Time to pull up your sleeves and showcase your nifty skills. We will be announcing the format of the event shortly. “We find that the IT industry is absorbing almost half of all of the analytics jobs. Banking is the second largest, but trails at almost one fourth of IT’s recruiting volume. It is interesting that data rich industries like Retail, Energy and Insurance are trailing near the bottom, lower than even construction or media, who handle less data. Perhaps these are ripe for disruption through analytics?” Our learners also read: Learn Python Online for Free Mr. S. Anand, CEO of Gramener, wonders aloud. Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? upGrad’s Exclusive Data Science Webinar for you – Watch our Webinar on The Future of Consumer Data in an Open Data Economy document.createElement('video'); https://cdn.upgrad.com/blog/sashi-edupuganti.mp4 Top Data Science Skills You Should Learn SL. No Top Data Science Skills to Learn 1 Data Analysis Online Certification Inferential Statistics Online Certification 2 Hypothesis Testing Online Certification Logistic Regression Online Certification 3 Linear Regression Certification Linear Algebra for Analysis Online Certification Get data science certification from the World’s top Universities. Learn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career.

by Apoorva Shankar

15 Dec'16

5.22K+

What’s Cooking in Data Analytics? Team Data at UpGrad Speaks Up!

Team Data Analytics is creating the most immersive learning experience for working professionals at UpGrad. Data Insider recently checked in to me to get my insights on the data analytics industry; including trends to watch out for and must-have skill sets for today’s developers. Here’s how it went: How competitive is the data analytics industry today? What is the demand for these types of professionals? Let’s talk some numbers, a widely-quoted McKinsey report states that the United States will face an acute shortage of around 1.5 million data professionals by 2018. In India, which is emerging as the global analytics hub, the shortage of such professionals could go up to as high as 200,000. In India alone, the number of analytics jobs saw a 120 percent rise from June 2015 to June 2016. So, we clearly have a challenge set out for us. Naturally, because of acute talent shortage, talented professionals are high in demand. Decoding Easy vs. Not-So-Easy Analytics What trends are you following in the data analytics industry today? Why are you interested in them? There are three key trends that we should watch out for: Personalization I think the usage of data to create personalized systems is a key trend being adopted extremely fast, across the board. Most of the internet services are removing the anonymity of online users and moving towards differentiated treatment. For example, words recommendations when you are typing your messages or destinations recommendations when you are using Uber. Our learners also read: Learn Python Online for Free End of Moore’s Law Another interesting trend to watch out for is how companies are getting more and more creative as we reach the end of Moore’s Law. Moore’s Law essentially states that every two years we will be able to fit double the number of transistors that could be fit on a chip, two years ago. Because of this law, we have unleashed the power of storing and processing huge amounts of data, responsible for the entire data revolution. But what will happen next? IoT Another trend to watch out for, for the sheer possibilities it brings. It’s the emergence of smart systems which is made possible by the coming together of cloud, big data, and IoT (internet of things). Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses What skill sets are critical for data engineers today? What do they need to know to stay competitive? A good data scientist sits at a rare overlap of three areas: Domain Knowledge This helps understand and appreciate the nuances of a business problem. For e.g, an e-commerce company would want to recommend complementary products to its buyers. Statistical Knowledge Statistical and mathematical knowledge help to inform data-driven decision making. For instance, one can use market basket analysis to come up with complementary products for a particular buy. Technical Knowledge This helps perform complex analysis at scale; such as creating a recommendation system that shows that a buyer might prefer to also buy a pen while buying a notebook. How Can You Transition to Data Analytics? Outside of their technical expertise, what other skills should those in data analytics and business intelligence be sure to develop? Ultimately, data scientists are problem solvers. And every problem has a specific context, content and story behind it. This is where it becomes extremely important to tie all these factors together – into a common narrative. Essentially all data professionals need to be great storytellers. In this respect, one of the key skills for analysts to sharpen would be, breaking down the complexities of analytics for others working with them. They can appreciate the actual insights derived – and work toward a common business goal. In addition, what is as crucial is getting into a habit of constantly learning. Even if it means waking up every morning and reading what’s relevant and current in your domain. Top Essential Data Science Skills to Learn SL. No Top Data Science Skills to Learn 1 Data Analysis Certifications Inferential Statistics Certifications 2 Hypothesis Testing Certifications Logistic Regression Certifications 3 Linear Regression Certifications Linear Algebra for Analysis Certifications What should these professionals be doing to stay ahead of trends and innovations in the field? Professionals these days need to continuously upskill themselves and be willing to unlearn and relearn. The world of work and the industrial landscape of technology-heavy fields such as data analytics is changing every year. The only way to stay ahead, or even at par with these trends, is to invest in learning, taking up exciting industry-relevant projects, participating in competitions like Kaggle, etc. How important is mentorship in the data industry? Who can professionals look toward to help further their careers and their skills? Extremely important. Considering how fast this domain has emerged, academia and universities, in general, have not had the chance to keep up equally fast. Hence, the only way to stay industry-relevant with respect to this domain is to have industry-specific learning. This can only be done in two ways – through real-life case studies and mentors who are working/senior professionals and hail from the data analytics industry. In fact, at UpGrad, there is a lot of stress on industry mentorship for aspiring data specialists. This is in addition to a whole host of case studies and industry-relevant projects. Get data science certification from the World’s top Universities. Learn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career. Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? Where are the best places for data professionals to find mentors? upGrad’s Exclusive Data Science Webinar for you – Transformation & Opportunities in Analytics & Insights document.createElement('video'); https://cdn.upgrad.com/blog/jai-kapoor.mp4 While it’s important for budding or aspiring data professionals to tap into their networks to find the right mentors, it is admittedly tough to do so. There are two main reasons that can be blamed for this. First, due to the nascent stage, the industry is at, it is extremely difficult to find someone with the requisite skill sets to be a mentor. Even if you find someone with considerable experience in the field, not everybody has the time and inclination to be an effective mentor. Hence most people don’t know where to go to be mentored. That’s where platforms like UpGrad come in, which provide you with a rich, industry-relevant learning experience. Nowhere else are you likely to chance upon such a wide range of industry tie-ups or associations for mentorship from very senior and reputed professionals. How Can You Transition to Data Analytics? What resources should those in the data analytics industry be using to ensure they’re educated and up-to-date on developments, trends, and skills? There are many. For starters, here are some good and pretty interesting blogs and resources that would serve aspiring/current data analysts well to keep up with Podcasts like Data Skeptic, Freakonomics, Talking Machines, and much more. This interview was originally published on Data Insider.

by Rohit Sharma

23 Dec'16