Hold the wheel. Learn the actual scenarios, challenges and factors that play out in the real world. Data Science needs you in the front seat. Here’s how to get there. Kanchana Kumar, an alumnus of IISc and TalentSprint’s Computational Data Science Programme, shares his experience. 

If data has become the new fuel that drives the world, imagine what this new reality means for people who can drive well with this fuel. Enterprises from almost all verticals are investing in the power and impact of Data Science. As a result, they are reaping huge gains with real-time insights, unprecedented customer intimacy and split-second decisions made well and with a competitive edge – all thanks to the vast pools of data that they can now use to their advantage.

The maths behind the science

Data Science is, indeed, changing the world drastically and irreversibly. It is creating exponential opportunities for professionals. As a result, the market is continuously on a northbound trajectory. As per MarketsandMarkets, the Data Science platform market size is rising from $37.9 billion in 2019 to touch $140.9 billion by 2024. Research Dive’s estimates put the size of the Data Science platform market at $224.3 billion by 2026 and attribute its growth to a lot of factors like how Data Science aids a user to assess, build, and control data. Among other boosters for companies as advantages of Data Science, we can observe many attractive facets like attracting new customers, updating business processes, and giving meaningful insights into the data. And all this potential has to translate into demand for professionals with relevant expertise.  

Look at a recent Dice report, and you will notice how demand for Data Scientists in 2020 surged by an average of 50 per cent- and this was seen across the berth, from healthcare, tele communications, media/entertainment, to the Banking, Financial Services, and Insurance (BFSI) sectors. Some time back, the Dice Q1 Tech Job Report unravelled that companies have started to look towards building for the future and hiring technologists again in considerable numbers. This is where Data Scientists (ranked 34th, improving by six slots) have gained enormously from this trend. In terms of job postings’ growth rate tracked here, Data Scientists registered a 27 per cent jump.

Thinking of Data Science? Think of this too

It is, indeed, a good time for aspirants to leverage this wave and be at the forefront of this opportunity. First, however, they need to tap this well by equipping themselves with relevant expertise. Well-stacked and excellently-executed programmes pave the path of accelerated growth. But it can be tough to spot the difference between ‘any port in the storm’ and ‘a programme that will make you travel to a bright future’. 

“ If you are an aspirant of Data Science and want to change the domain this is the right programme to start with. You will get all the concepts to get started with the Data Science journey.” ~ Kanchana Kumar, an alumnus of IISc and TalentSprint’s Computational Data Science Programme


Kanchana Kumar, an alumnus of IISc and TalentSprint’s Computational Data Science Programme, recently shared his learning experience with us. This is like hearing from a person with a hands-on perspective in building Data Science capabilities. Here are the excerpts. 

  1. Even if you know the brass tacks, a good programme won’t hurt because it will give you a strong foot forward in what matters out there right now. For example, people familiar with only a few python libraries specific to Data Science came out of the program with a wide berth of knowledge. In the programme, they could learn how machine learning algorithms work, the basic mathematical concept behind each algorithm, data engineering concepts and parallel programming concepts. But above all, they became proficient and confident in analysing raw data to derive hidden insights to solve business problems. That’s the ultimate value that a true Data Scientist can bring.
  2. It is not just what you learn in the programme, but what you learn from people. The programme at TalentSprint, for instance, ensured that the cohort is the most diverse one in terms of experience, domain and the organisation to which these participants belong. As a result, every day became learning for these programme participants – and practically and collaboratively.
  3. While the quality of faculty, trainers and session design are essential factors that can impact the actual value of the programme, the practical side also matters a lot. Like – how well-curated the programme is, how fast and responsive are the teams on support and query-resolution; and how well-managed the entire administrative part is. But, of course, good programmes like this one always take care of these BTS (Behind The Scene) details.
  4. It is an excellent option to consider for general aspirants, but more so for professionals who wish to change their domain. These programmes are great starting points to cover the domain fundamentals and enable a smooth transition.
  5. Just coverage of topics will not suffice. Unless the topics are taught in the context of actual industry needs that will play out outside the progr experience, the programme would be just another tick-in-the-box. Each discussion with faculty, cohort, mentor and the team helped the programme participants to master different minuscule aspects of the Data Science domain. And this makes a significant difference in the real takeaway of any Data Science programme.
  6. What also sets a programme apart from the crowd is the value amplified by the Capstone projects. The participants worked on the Computer Vision R&D domain. The objective of the project is to develop a super-resolution image from a low-resolution image using the GAN algorithm. The programme helps the learners confront practical issues like dataset availability and the resources needed to train the image dataset. With the mentor’s help, the participants applied the concepts learned in the last 10 months and completed the project.

So remember these guiding stars and start driving faster on the exciting lane of Data Science opportunities.