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What is Linear Data Structure? List of Data Structures Explained

What is Linear Data Structure? List of Data Structures Explained

Updated on 29 May, 2024

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Table of Contents

What is Linear Data Structure: Definition and Characteristics
Types of Linear Data Structures
Differences Between Non-Primitive Data Structure: Linear and Non-Linear
Pros and Cons of Linear Data Structures
Real-world Applications of Linear Data Structures
Conclusion

Data structures are the data structured in a way for efficient use by the users. As the computer program relies hugely on the data and also requires a large volume of data for its performance, therefore it is highly important to arrange the data. This arrangement of data in organized structures is known as a data structure.

Storing of the data in data structures allows the access, modifications, and other operations that can be carried over the data elements. The arrangement of the data is mainly done in a computer and therefore proper algorithms are required to carry on operations with the data structures. Reducing space and decreasing the time complexity of different tasks is the main aim of data structures.

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The most important points in a data structure are:

A large amount of data is organized through every type of data structure.
A particular principle is followed by every data structure.
The basic principle of the data structure should be followed even if any operations are carried out over the data structure.

Arrangement of the data within a data structure can follow different orders. A data structure is therefore classified according to the way of arrangement of the data. Basically, there are two types of data structure.

Primitive data structure
Non-primitive data structure

The primitive type of data structure includes the predefined data structures such as char, float, int, and double.

The non-primitive data structures are used to store the collection of elements. This data structure can be further categorized into

Linear data structure
Non-Linear data structure.

Read: Learn the differences between linear and non linear data structure

What is Linear Data Structure: Definition and Characteristics

It is a type of data structure where the arrangement of the data follows a linear trend. The data elements are arranged linearly such that the element is directly linked to its previous and the next elements. As the elements are stored linearly, the structure supports single-level storage of data. And hence, traversal of the data is achieved through a single run only.

Characteristics

It is a type of data structure where data is stored and managed in a linear sequence.
Data elements in the sequence are linked to one after the other.
Implementation of the linear structure of data in a computer’s memory is easy as the data is organized sequentially.
Array, queue. Stack, linked list, etc. are examples of this type of structure.
The data elements stored in the data structure have only one relationship.
Traversal of the data elements can be carried out in a single run as the data elements are stored in a single level.
There is poor utilization of the computer memory if a structure storing data linearly is implemented.
With the increase in the size of the data structure, the time complexity of the structure increases.

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These structures can therefore be summarized as a type of data structure where the elements are stored sequentially and follow the order where:

Only one first element is present which has one next element.
Only one last element is present which has one previous element.
All the other elements in the data structure have a previous and a next element

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You can use linear data structure in C, C++, Python, JavaScript, or any other programming language you are familiar with.

If you get baffled when given a list of linear structures and wonder which of the following is a linear data structure, here’s a rundown of the types.

Types of Linear Data Structures

Operations performed on linear data structure include insertion, deletion, searching, traversing, and sorting. All these operations serve as the foundation for linear data structures.

Discussed below are the linear data structure types and the corresponding operations on linear data structures that can be performed. You can also look for what is linear data structure with examples to develop a robust idea.

1. Array

The array is that type of structure that stores homogeneous elements at memory locations which are contiguous. The same types of objects are stored sequentially in an array. The main idea of an array is that multiple data of the same type can be stored together. Before storing the data in an array, the size of the array has to be defined. Any element in the array can be accessed or modified and the elements stored are indexed to identify their locations.

An array can be explained with the help of a simple example of storing the marks for all the students in a class. Suppose there are 20 students, then the size of the array has to be mentioned as 20. Marks of all the students can then be stored in the created array without the need for creating separate variables for marks for every student. Simple traversal of the array can lead to the access of the elements.

Arrays incorporate the use of zero-based indexing techniques. This means users can access the first element with an index of 0, the second with an index of 1, and so on. Another remarkable feature of this linear data structure is that arrays provide a constant time of O(1) to access the elements, which means that it takes equal time to access any element in the series, disregarding the size of the array.

Types of array:

One-dimensional array: This is a simple form of array that contains elements, all of which are the same type of data, in a single row.
Two-dimensional array: This is also known as a matrix. This type of data structure has rows and columns and appears like a grid. The elements can be accessed using two indices- one for column and one for row.
Multi-dimensional array: These arrays have more than two dimensions.

Operations Performed on Arrays:

The following operations can be performed on arrays:

Accessing an element: Accessing an element by its index is an essential operation that can be performed on arrays. It is a constant time operation with a time complexity of O(1).
Inserting or deleting elements: Inserting elements at the end of an array is a constant-time operation having complexity O(1). But inserting an element at the beginning takes O(n) time since all the elements have to be shifted.

The same goes for deletion of elements in an array.

Searching for elements: For unsorted data, linear search takes O(n) time, and for sorted data, binary search takes O(logn) time.

2. Linked list

The linked list is that type of data structure where separate objects are stored sequentially. Every object stored in the data structure will have the data and a reference to the next object. The last node of the linked list has a reference to null. The first element of the linked list is known as the head of the list. There are many differences between a linked list to the other types of data structures. These are in terms of memory allocation, the internal structure of the data structure, and the operations carried on the linked list.

Getting to an element in a linked list is a slower process compared to the arrays as the indexing in an array helps in locating the element. However, in the case of a linked list, the process has to start from the head and traverse through the whole structure until the desired element is reached. In contrast to this, the advantage of using linked lists is that the addition or deletion of elements at the beginning can be done very quickly.

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There are three types of linked lists:

Single Linked List: This type of structure has the address or the reference of the next node stored in the current node. Therefore, a node which at the last has the address and reference as a NULL. Example: A->B->C->D->E->NULL.
A Double Linked List: As the name suggests, each node has two references associated with it. One reference directs to the previous node while the second reference points to the next node. Traversal is possible in both directions as reference is available for the previous nodes. Also, explicit access is not required for deletion. Example: NULL<-A<->B<->C<->D<->E->NULL.
Linked List which is circular: The nodes in a circular linked list are connected in a way that a circle is formed. As the linked list is circular there is no end and hence no NULL. This type of linked list can follow the structure of both singly or doubly. There is no specific starting node and any node from the data can be the starting node. The reference of the last node points towards the first node. Example: A->B->C->D->E.

Properties of a linked list are:

Access time: O(n)
Searching time: O(n)
Adding element: O(1)
Deleting an Element : O(1)

3. Stack

The stack is another type of structure where the elements stored in the data structure follow the rule of LIFO (last in, first out) or FILO (First In Last Out). Two types of operations are associated with a stack i.e. push and pop. Push is used when an element has to be added to the collection and pop is used when the last element has to be removed from the collection. Extraction can be carried out for only the last added element.

Types of stack:

There are two types of stacks:

Fixed-size stack: This kind of stack does not grow or shrink. Once full, any attempt to add an element will lead to an overflow error. Similarly, an attempt to remove an element will also display an underflow error.
Dynamic size stack: This kind of stack can grow or shrink. When a stack is full or empty, it can automatically resize to accommodate a new element or shrink in size.

Other operations are:

top(): This operation helps to return the element that has been inserted last and is at the top without removing it.
size(): This operation indicates the total number of elements the stack contains.
isEmpty(): This operation helps to identify if a stack is empty.

Properties of a stack are:

Adding element: O(1)
deleting element: O(1)
Accessing Time: O(n) [Worst Case]
Only one end allows inserting and deleting an element.

Examples of the stack include the removal of recursion. In scenarios where a word has to be reversed, or while using editors when the word that was last typed will be removed first (using an undo operation), stacks are used. If you want to try interesting data structure projects, click to read this article.

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4. Queue

Queue is the type of data structure where the elements to be stored follow the rule of First In First Out (FIFO). The particular order is followed for performing the required operations over the elements. The difference of a queue from that of a stack lies in the removal of an element, where the most recently added object is removed first in a stack. Whereas, in the case of a queue, the element that was added first is removed first.

Following is a list of the different types of queues:

Input restricted queue: In this kind of queue, one can only insert inputs from one end. Deletion, however, can be done from both ends.
Output restricted queue: This is just the reverse of input restricted queues. Here, the input can be taken from both ends, but deletion can only be done from one end.
Circular queue: In this kind of queue, the first and the last positions are connected to one another, resulting in a circular structure.
Double-ended queue: This kind of operation supports insertion and deletion from both ends.
Priority queue: In this kind of queue, elements can be accessed based on priority assigned to them.

Both the end of the data structure is used for the insertion and the removal of data. The two main operations governing the structure of the queue are enqueue, and dequeue. Enqueue refers to the process where inserting an element is allowed to the collection of data and dequeue refers to the process where removal of elements is allowed, which is the first element in the queue in this case.

Properties of a queue are:

Inserting an element: O(1)
Deleting an element: O(1)
Accessing Time: O(n)

Other queue operations are:

peek() or front(): This helps to acquire the data element available at the queue’s front node without actually eliminating it.
rear(): This operation returns an element at the rear without it being removed.
ifNull(): Finds out if a queue is empty.
ifFull(): Finds out if a queue is full.

Have a look at this linear data structure example.

Example of the queue: Similar to those queues made while waiting for the bus or anywhere, the data structure too follows the same pattern. We can imagine a person waiting for the bus and standing at the first position as the person that came to the queue first. This person will be the first one who will get onto a bus, i.e. exit the queue. Queues are applied when multiple users are sharing the same resources and they have to be served on the basis of who has come first on the server.

5. String

Although non-modifiable, Strings display characteristics similar to those that linear data structures possess. They allow operations such as searching, concatenation, and substring extraction.

Strings are used for input/output operations, data manipulation, and text processing.

Differences Between Non-Primitive Data Structure: Linear and Non-Linear

Now that you know the fundamentals of what is linear data structure, let’s look at the differences between linear and non-linear data structures:

Linear Data Structure	Non-linear Data Structure
The elements are arranged in a sequence.	The elements are arranged in the form of a hierarchy.
One can access the elements sequentially.	One can access the elements randomly or based on relationships.
Efficient memory usage.	Complex memory usage.
Searching and sorting operations can be performed simply.	Searching and sorting operations are complex.
Examples: Array, Lined List, Stack, Queue, String	Examples: Tree, Graphs

Pros and Cons of Linear Data Structures

Some advantages and disadvantages associated with linear data structures make them suitable or unsuitable for the scenarios in which they operate. Understanding these advantages and disadvantages helps to choose the most appropriate linear data structure.

The advantages of linear data structures are as follows:

Linear data structures help to access the elements sequentially.

For example, arrays facilitate efficient transversal with the help of loops. This makes it suitable for tasks requiring data processing in a linear pattern.

Data can be inserted or deleted efficiently.

Arrays make it easy to access elements by index, and linked lists help insert or delete data at the beginning or end of a given list.

Linear data structures are easy to understand and implement.

For instance, arrays help to store elements in a straightforward way, and linked lists use pointers that help to connect the nodes.

Linear data structures offer flexibility as they allow dynamic resizing.

This facilitates easy accommodation of the varying data amounts. It especially holds true for linked lists, which can easily grow or shrink in size by removing or adding the nodes as and when required.

Let us now have a look at the disadvantages associated with linear data structures:

During the initialization of arrays, they have a fixed size determined.

As a result, the flexibility is limited. If the size is too small to store any additional elements, the entire array may be resized.

There are insufficient search operations in linear data structures.

For instance, in the case of linked lists and arrays, one has to traverse the elements one by one until the desired result is obtained. The time complexity of O(n) in the case of linear search may lead to inefficient results if the data set is large.

Linked lists need more memory to store pointers than arrays.

This increases the overall memory usage of the data structure and causes the efficiency to decrease.

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Real-world Applications of Linear Data Structures

The vast usage of linear data structures in the real world is noteworthy. Listed below are a few instances stating the use of linear data structures:

Database systems: Linear data structures like linked lists and arrays are essential to database systems. Arrays help in efficient indexing and make sequential access possible. Linked lists facilitate dynamic storage and allow efficient deletion and insertion of elements.
Text editors: Linear data structures incorporate features like redo and undo functionality. A stack data comes in handy in this case, as it stores the operations performed, allowing the users to revert the actions they performed initially.
Browser history: The array and linked list linear data structures are used to maintain a list of all the web pages a user visits. This makes it easy for users to navigate the web pages.
Task management: Queue is a linear data structure that helps in easy task management. As mentioned earlier, queues allow the tasks to be performed in a FIFO (First-In-First-Out) manner. This ensures that the oldest task is performed first.
Data storage: Arrays, the simplest data structure, can store items with the same data type. Due to this reason, they find widespread usage in arranging a game’s leader board that displays the rank of the players in descending order, cell phone contacts, online ticket booking systems, and many more.

Conclusion

An increase in the size of the data has necessitated the efficient use of data structures in computer programs. Data if not organized in a structured manner, the performance of tasks over the elements becomes difficult.

For a hassle-free operation, it is always important to organize it so that easy and effective operations can be carried out by computer programs. If the data elements are organized in sequential order then it is known as a linear data structure whereas if the data elements are arranged in a non-linear way, it is termed a non-linear structure.

Wide application of data structure has been observed in machine learning languages, real-life problems, etc. People, who are dreaming to work in this field, should be able to master these concepts.

We hope this guide has been able to define linear data structure and the different types of linear data structures.

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Frequently Asked Questions (FAQs)

1. What is the difference between linear and non-linear data structures?

The following illustrates the significant differences between the linear and non-linear data structures:
Linear Data Structure -
1. In linear data structures, each element is linearly connected to each other having reference to the next and previous elements.
2. Implementation is quite easy as only a single level is involved.
3. Wastage of memory is much more common in linear data structures.
4. Stacks, Queues, Arrays, and Linked lists are all examples of linear data structures.
Non-Linear Data Structure -
1. In non-linear data structures, the elements are connected in a hierarchical manner.
2. Implementation is much more complex as multiple levels are involved.
3. Memory is consumed wisely and there is almost no wastage of memory.
4. Graphs and trees are examples of non-linear data structures.

2. In what ways are linked lists more efficient than arrays?

The following points elaborate the ways in which linked lists are much more efficient than arrays:
a. Dynamic Memory allocation
The memory of a linked list is dynamically located which means that there is no need to initialize the size and it can be expanded as well as shrink anytime without implying any exterior operation.
On the other hand, arrays are statically allocated and the size has to be initialized. Once created, the size cannot be altered.
b. Insertion and Deletion
Since a linked list is dynamically created, operations like insertion and deletion are much more convenient.
c. No memory wastage
There is no memory wastage in a linked list as all the elements are dynamically inserted. And after the deletion of an element, we can free its memory.

3. What are the most common operations performed in linear data structures?

The common possible operations that can be performed in all linear data structures include traversing, insertion, deletion, modification, search operation, and sort operation.
These operations are recognized by different names in different data structures. For example, the insertion and deletion operations are known as Push and Pop operations in Stack, whereas they are referred to as enqueue and dequeue operations in Queue.
There can be some other operations as well such as merging and the empty operation to check if the data structure is empty or not.

4. What is non linear data structure with example?

A non-linear data structure organizes data elements in a hierarchical or interconnected manner, allowing for multiple relationships. Examples include trees, such as binary trees, and graphs, where nodes can have multiple connections.

Did you find this article helpful?

Rohit Sharma

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

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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