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5 Types of Binary Tree Explained [With Illustrations]

Updated on 14 May, 2024

69.84K+ views
17 min read
Types of Binary Tree

In computer science, various data structures help in arranging data in different forms. Among them, trees are widely used abstract data structures that simulate a hierarchical tree structure. A tree usually has a root value and subtrees that are formed by the child nodes from its parent nodes. Trees are non-linear data structures.

A general tree data structure has no limitation on the number of child nodes it can hold. Yet, this is not the case with a binary tree. This article will learn about a specific tree data structure – binary tree and types of binary tree.

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What is Binary Tree Data Structure?

binary tree is a tree-type non-linear data structure with a maximum of two children for each parent. Every node in a binary tree has a left and right reference along with the data element. The node at the top of the hierarchy of a tree is called the root node. The nodes that hold other sub-nodes are the parent nodes.

A parent node has two child nodes: the left child and right child. Hashing, routing data for network traffic, data compression, preparing binary heaps, and binary search trees are some of the applications that use a binary tree.

Terminologies associated with Binary Trees and Types of Binary Trees

  • Node: It represents a termination point in a tree.
  • Root: A tree’s topmost node. 
  • Parent: Each node (apart from the root) in a tree that has at least one sub-node of its own is called a parent node.
  • Child: A node that straightway came from a parent node when moving away from the root is the child node.
  • Leaf Node: These are external nodes. They are the nodes that have no child.
  • Internal Node: As the name suggests, these are inner nodes with at least one child.
  • Depth of a Tree: The number of edges from the tree’s node to the root is.
  • Height of a Tree: It is the number of edges from the node to the deepest leaf. The tree height is also considered the root height.

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As you are now familiar with the terminologies associated with the binary tree and types of binary tree, it is time to understand the binary tree components. Check out our data science courses to learn in-depth about binary structure and components. 

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Understanding Properties of Binary Tree Or What Is Binary Tree?

At every level of it, the maximum number allowed for nodes stands at 2i.

The height of a binary tree stands defined as the longest path emanating from a root node to the tree’s leaf node.

What Is Binary Tree– More Than The Binary Tree Definition

Say a binary tree placed at a height equal to 3. In that case, the highest number of nodes for this height 3 stands equal to 15, that is, (1+2+4+8) = 15. In basic terms, the maximum node number  possible for this height h is (20 + 21 + 22+….2h) = 2h+1 -1.

Now, for the minimum node number that is possible at this height h, it comes as equal to h+1.

If there are a minimum number of nodes, then the height of a binary tree would stand aa maximum. On the other hand, when there is a number of a node at its maximum, then the binary tree m height will be minimum. If there exists around ‘n’ number nodes in a binary tree, here is a calculation to clarify the binary tree definition.

The tree’s minimum height is computed as:

n = 2h+1 -1

n+1 = 2h+1

Taking log for both sides now,

log2(n+1) = log2(2h+1)

log2(n+1) = h+1

h = log2(n+1) – 1

The highest height will be computed as:

n = h+1

h= n-1

Binary Tree Components

There are three binary tree components. Every binary tree node has these three components associated with it. It becomes an essential concept for programmers to understand these three binary tree components: 

  1. Data element
  2. Pointer to left subtree
  3. Pointer to right subtree

Source

These three binary tree components represent a node. The data resides in the middle. The left pointer points to the child node, forming the left sub-tree. The right pointer points to the child node at its right, creating the right subtree. 

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Binary Tree Definition: An in-depth analysis

In case there exists n number of nodes in any binary tree, the height stands given by log n logn. This is due to the simple reason that for any given node in any binary tree, there will be two child nodes at the most. This drives us to an explanation to define binary tree: for every level or every height of any binary tree, the node number must be the same as around half the node numbers present at the next level.

In the form of an answer to define binary tree, the node number at every level is close to double the node number at its previous level. We hope this clears the answer to what is binary tree!

It means, that when a binary tree comes with height h, the number of nodes for define binary tree is  n = (2 ^ 0) + (2 ^ 1) + (2 ^ 2) + (2 ^ 3) + ….. + (2 ^ (h-1))(20)+(21)+(22)+(23)+…..+(2(h−1))

From the `mathematical induction point for what is a binary tree, this is what we know-

(2 ^ 0) + (2 ^ 1) + (2 ^ 2) + (2 ^ 3) + ….. + (2 ^ {(h-1)}) = (2 ^ h)-1(20)+(21)+(22)+(23)+…..+(2(h−1))=(2h)−1

Hence,

(2 ^ h)-1 = n => 2 ^ h = n + 1 => h = log2(n+1)(2h)−1=n=>2h=n+1=>h=log2(n+1)

Therefore, the minimum height for a binary tree is roughly equal to log(n) roughly. This helps you better understand what is a binary tree.

Also, the minimum number of nodes that are possible at height h of the binary tree can be known by h+1.

If the binary tree comes with an L number for leaf nodes, the height is represented by L + 1.

Types of Binary Trees

Binary trees come in various types, each suited for specific applications. Full Binary Trees have every non-leaf node with exactly two children. Complete Binary Trees are fully populated at all levels except possibly the last, which is filled from left to right. Perfect Binary Trees have all levels fully filled, with all leaves at the same level. Balanced Binary Trees minimize the height difference between left and right subtrees to optimize search operations. Finally, Binary Search Trees (BST) organize data such that each node’s left subtree contains only lesser values, and the right subtree only greater values, facilitating efficient search and navigation.

There are various types of binary trees, and each of these binary tree types has unique characteristics. Here are each of the binary tree types in detail:

1. Full Binary Tree

It is a special kind of a binary tree that has either zero children or two children. It means that all the nodes in that binary tree should either have two child nodes of its parent node or the parent node is itself the leaf node or the external node. 

In other words, a full binary tree is a unique binary tree where every node except the external node has two children. When it holds a single child, such a binary tree will not be a full binary tree. Here, the quantity of leaf nodes is equal to the number of internal nodes plus one. The equation is like L=I+1, where L is the number of leaf nodes, and I is the number of internal nodes.

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Here is the structure of a full binary tree:

2. Complete Binary Tree

A complete binary tree is another specific type of binary tree where all the tree levels are filled entirely with nodes, except the lowest level of the tree. Also, in the last or the lowest level of this binary tree, every node should possibly reside on the left side. Here is the structure of a complete binary tree:

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3. Perfect Binary Tree

A binary tree is said to be ‘perfect’ if all the internal nodes have strictly two children, and every external or leaf node is at the same level or same depth within a tree. A perfect binary tree having height ‘h’ has 2h – 1 node. Here is the structure of a perfect binary tree:

4. Balanced Binary Tree

A binary tree is said to be ‘balanced’ if the tree height is O(logN), where ‘N’ is the number of nodes. In a balanced binary tree, the height of the left and the right subtrees of each node should vary by at most one. An AVL Tree and a Red-Black Tree are some common examples of data structure that can generate a balanced binary search tree. Here is an example of a balanced binary tree:

5. Degenerate Binary Tree 

A binary tree is said to be a degenerate binary tree or pathological binary tree if every internal node has only a single child. Such trees are similar to a linked list performance-wise. Here is an example of a degenerate binary tree:

Benefits of a Binary Tree

  • The search operation in a binary tree is faster as compared to other trees
  • Only two traversals are enough to provide the elements in sorted order
  • It is easy to pick up the maximum and minimum elements
  • Graph traversal also uses binary trees
  • Converting different postfix and prefix expressions are possible using binary trees

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Special Types of Binary Trees

Binary trees can also be grouped according to node values. The types of binary tree according to node structure include the following:

  • Binary Search Tree

A binary search tree comes with the following properties:

  • In the left subtree of any node, you will find nodes with keys smaller than the node’s key.
  • The right subtree of any node will include nodes with keys larger than the node’s key.
  • The left, as well as the right subtree, will be types of binary search tree
  • AVL Tree

An AVL binary tree in DSA is self-balanced. In such a tree, the difference between the heights of the left and right subtrees for all nodes cannot be greater than one. So, the nodes in the right as well as left subtrees of the AVL tree will be one or less than that.

  • Red Black Tree

If you want us to explain the binary tree and its types, the red-black tree will definitely find a mention. This kind of binary tree is self-balancing, with each node having an extra bit. The extra bit gets represented as either black or red.

The colors in a red black binary tree in data structure are useful for keeping the whole tree balanced during deletions and insertions. The balance of a red black tree won’t be perfect. But these binary trees are perfect for bringing down the search time.

  • B- Tree

A B- Tree is a type of self-balanced search tree in data structures. These binary trees support smooth access, deletion, and insertion of data items. B- trees are particularly common in file systems and databases.

Among the different types of binary tree, a B- tree helps with efficient storage and retrieval of large volumes of data. A fixed maximum degree or order is a key characteristic of a B- tree. This fixed value helps determine the total number of child nodes in a parent node.

The nodes present in a B- binary tree can include several keys and child nodes. The keys of a B- binary tree in algorithm design can help in indexing and locating data items. 

  • B+ Tree

A binary tree in data structure can also be classified as B+, which is one variant of the B- tree. Since a B+ tree comes with a fixed maximum degree, it enables efficient insertion, access, and deletion of data items. But a B+ binary tree includes all data items inside the leaf nodes. 

The internal nodes of a B+ binary tree only include keys for locating and indexing data items. Due to this design, searches using a B+ tree will be a lot faster, and you will also be able to access data items sequentially. Moreover, the leaf nodes of a binary tree remain together in a linked list. 

  • Segment Tree

If you look into a binary tree and its types, you will come across one category called the segment or statistic tree. This type of binary tree is usually responsible for storing information related to different segments or intervals. With a segment tree, you will be able to perform querying of the stored segments in a specific point.

Among the different types of binary tree in data structure, you will realize that a segment tree is static. Therefore, you won’t be able to modify the structure of a segment tree after it has been built.

Applications of Binary Tree in Data Structure

If you want to read more on binary tree in data structure, you should learn about their applications. Binary search trees are quite suitable for the following purposes:

  • Search Algorithms: An algorithm for binary search tree can efficiently find a specific element. The search can be executed in O u(log n) time complexity, where n defines the number of nodes. A binary search tree is often useful for quickly finding particular elements in a sorted list. 
  • Database Systems: With each node of a binary tree representing a record, data can be stored in a database system. As a result, search operations may be completed quickly, and the database system can manage massive volumes of data.
  • Decision Trees: Binary trees are a sort of machine learning technique that may be used to create decision trees. These decision trees are highly useful for regression analysis and classification.
  • File Systems: File systems can be implemented using binary trees, in which every node corresponds to a directory or file. This enables quick and easy file system browsing and searching.
  • Compression Algorithms: An algorithm for binary search tree in data structure can be useful for Huffman coding. A compression algorithm is responsible for assigning variable-length codes to characters according to their occurrence frequency in the input data. 
  • Game AI: Game AI can be implemented using binary trees, where every node indicates a potential move in the game. The optimal move can be found by the AI algorithm searching the tree.
  • Sorting Algorithms: An algorithm of binary tree can also be used for efficient sorting. For instance, the search tree sort and heap sort are quite beneficial. 

What are some applications of binary tree?

Binary trees are versatile in computing applications: 1. Hierarchical Data Representation: Ideal for organizing structures like file systems. 2. Database Indexing: Used in databases for quick data retrieval. 3. Priority Queues: Binary heaps implement queues where elements are processed by priority. 4. Expression Parsing: Crucial in compilers for building parse trees that aid in syntax analysis. 5. Network Routing Algorithms: Facilitate efficient data packet routing in network protocols. Each application utilizes the tree structure to efficiently manage and organize data.

Why Should You Use a Binary Tree in Data Structure?

Once you learn about a binary tree and its types, you should try to figure out the benefits of these structures. Some key advantages of using a binary tree model include:

  • Ordered Traversal: Binary trees are structured in such a way that you will succeed in traversing them in a particular order, such as post-order, in-order, and pre-order. As a result, you will succeed in performing operations on the nodes in a particular order. For instance, you will be able to easily print nodes in a sorted order. 
  • Efficient Searching: A binary tree in data structure can be efficiently used to find a particular element. Each node comes with a maximum of two child nodes. So, search operations can be easily performed with the O(log n) time complexity. 
  • Fast Insertion and Deletion: Insertions and deletions can be done with binary trees in O(log n) time complexity. They are also a wise option for applications like database systems that need dynamic data structures.
  • Memory Efficient: Since binary trees only need two child pointers per node, they are comparatively memory-efficient when compared to other tree designs. This implies that they can be utilized to maintain effective search functions even when storing substantial volumes of data in memory.
  • Valuable for Sorting: If you understand the binary tree terminology in data structure, you will realize that it is extremely efficient for sorting. Therefore, you will find binary trees to be highly beneficial for heap sort and similar operations. 
  • Easy to Implement: It is quite simple and easy to understand and implement binary tree structures. That’s why binary tree algorithms are highly suitable for a large number of real-life applications. 

Disadvantages of Binary Tree Structures

While a binary tree in data structure is highly beneficial, it also has some shortcomings. A few reasons why binary trees might not be beneficial include:

  • Limited Structure: Every binary tree comes with a maximum of two children in each node. While it is a boon in many ways and makes these structures memory efficient, it is also a disadvantage. Due to their limited structure, binary trees cannot be used in certain cases. For instance, some trees require each node to have more than two children. In that case, a different tree format needs to be used in a data structure. 
  • Space Inefficiency: Binary trees are not as space efficient as some other types of data structures. Every node needs two child pointers. So, if it’s a large binary tree, a significant amount of memory will be required. 
  • Slow Performances: Binary trees are responsible for extremely slow performances in the worst-case scenarios. The worst-case scenario might even degenerate a binary tree. If that happens, every node will end up with just one child instead of two. As a result, search operations will degrade. 
  • Unbalanced Trees: In an unbalanced binary tree, one subtree appears to be considerably larger than the other. This difference can easily render search operations inefficient. The difference is even more prominent when the tree isn’t properly balanced, or data is inserted within it in a non-random manner. 
  • Complex Balancing Algorithms: Several balancing algorithms can be used to keep a binary tree balanced. But these algorithms are extremely difficult to implement. Some of these algorithms also demand extra overhead, which makes them incapable of certain applications. 

Operations to Perform on a Binary Tree

Some basic operations that can be implemented on a binary tree include:

  • Insertion of an element
  • Removal of an element
  • Looking for an element
  • Deletion of an element
  • Traversing an element (You can perform four types of traversals in a binary tree structure.)

A binary tree is also suitable for performing a host of auxiliary operations. Some auxiliary operations to implement on a binary tree include:

  • Detecting the height of the tree
  • Figuring out the level of the tree
  • Determining the right size of the whole tree

Conclusion

The binary tree is one of the most widely used trees in the data structure. Each of the binary tree types has its unique features. These data structures have specific requirements in applied computer science. We hope this article about types of binary trees was helpful. upGrad offers various courses in data science, machine learning, big data, and more.

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

Frequently Asked Questions (FAQs)

1. What are the drawbacks of using a binary search tree?

It uses a recursive method that takes up more stack space. The binary search method is error-prone and complex to programme. Binary search has a bad relationship with memory hierarchy, i.e. caching.

2. What is the use of a height-balanced binary tree?

Performing operations on balanced binary trees is computationally efficient. The following are the criteria for a balanced binary tree: At every given node, the absolute difference between the heights of the left and right subtrees is less than one. A balanced binary tree represents the left subtree of each node. Dealing with random values is frequently impossible in the real world, and the likelihood of dealing with non-random values (such as sequential) leads to skew trees, which is the worst case scenario. As a result, rotations are used to achieve height equilibrium.

3. What is a binary tree's maximum height?

A binary tree's height is equal to the height of the root node in the whole binary tree. It means that the maximum number of edges from the root to the farthest leaf node determines the height of a binary tree. In a binary search tree, a node's left child has a lower value than the parent, while the right child has a higher value. When there are n nodes in a binary search tree, the greatest height is n-1 and the least height is floor (log2n).

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Rohit Sharma

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

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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.
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by Apoorva Shankar

07 Dec'16
Decoding Easy vs. Not-So-Easy Data Analytics

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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.  
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by Prof. S. Sadagopan

14 Dec'16
Launching UpGrad’s Data Analytics Roadshow – Are You Game?

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.
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by Apoorva Shankar

15 Dec'16
What’s Cooking in Data Analytics? Team Data at UpGrad Speaks Up!

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.  
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by Rohit Sharma

23 Dec'16