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.

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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:


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.

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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?


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).

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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.

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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.

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Where are the best places for data professionals to find mentors?

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Transformation & Opportunities in Analytics & Insights

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.


How does upGrad’s student mentoring work?

A Student Mentor works with a learner for months, ensuring that he has a positive learning experience, assisting him with his questions, preparing measures to make a career transfer in the desired direction, and inspiring and encouraging him. At upGrad, a ‘Student Mentor’ performs a similar role in the lives of learners (upGrad students) who want to make a career change and hope to build a brighter future through a better profession. Student mentors are assigned to a group of students and are responsible for giving information and assisting them with non-academic issues too. They put forth a lot of effort to guarantee that students have a positive experience with upGrad.

What are the essential skills required for building a career in data engineering?

Basically, the role of data engineers is to create and manage database systems. For that, they need to be proficient in programming languages such as SQL, Python, and R. They should also be knowledgeable about warehousing solutions and ETL tools, and have a basic understanding of machine learning and algorithms.

Soft skills, such as communication and cooperation, should also be included in a data engineer's skill set. Data science is a highly collaborative discipline, and data engineers collaborate with a variety of stakeholders, ranging from data analysts to chief technology officers.

What is the function of Kaggle?

Users may use Kaggle to locate and publish data sets, study and develop models in a web-based data-science environment, collaborate with other data scientists and machine learning experts, and compete in data science contests. On Kaggle, you can get started for free. Both R and Python code are provided. There is a discussion board for each tournament where you may ask questions and upvote specific topics.

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