Data science, today, is the most preferred career jump for students and professionals with backgrounds in both business and IT. It is the key to many successful careers within business consulting, management consulting, entrepreneurship, research and academia.

Data science leverages data to gain actionable insights. Data science professionals combine the business knowledge and technology to build innovative products and drive businesses. Data science technologies are widely used in multinational corporations, public sectors, NGOs, innovative startups, scientific researches and academics, and this is just the beginning.

Data science is instrumental in shaping the future, and getting us closer to the evolution and adoption of artificial intelligence. Here are a few tips on how to become a data science professional.


There are several avenues to gain awareness of this fast evolving field. The best starting point is to read articles from resources that are available over the internet, watch YouTube videos  and follow blogs like Talking BiZness BlogsData Science Gyan, mUniversity Blogs, GreyAtom BlogsAnalytics Vidhya etc..

It helps to attend conferences and events related to data science – like data science congress. It is also a good idea to join local data science communities, if they exist, where you can meet a lot of data science enthusiasts in person, who are interested to learn, share and collaborate.


Your academic course or existing job may or may not provide you the opportunity to gain ‘hands-on’ experience in data science techniques, but it is something that you can do on your own, if you have access to a computer and internet connection.  All you need is time and motivation.

It takes about three to nine months to build your own portfolio, depending on your existing knowledge and time that you can afford to spend on the project. Building your own portfolio involves identifying an area of your interest, where you can show insights using publicly available data.

The steps involve acquiring data, crunching it to get insights, and building visualizations to communicate those insights effectively. Open source technologies like Python can be used to build very interesting insights, and open source javascript libraries like d3.js can be used to build visualizations on the web, so that everyone can view them. APIs can be used to get publicly available data from various data sources.


Although it may seem that the technicalities involved in obtaining data and crunching it for insights is a big challenge, but as a matter of fact – that’s the easy bit.  The real challenge is to identify data driven use cases that are useful and interesting to people, and the bigger challenge it to find ways of building business models around your data science project.

It is important to understand the business domain, brainstorm your project ideas and to lots of market research and validation, before starting the projects. Projects are experimental and iterative in nature, and a constant connect with your customers and investors is essential. Talking to people and getting guidance from industry experts will help in building useful and interesting projects.

Data scientists are not just technical professionals, but business professionals, with business domain understanding. Building expertise in both technical and business domains make it possible to iteratively design business use-cases and validate them.

Data scientists are disruptors, not mere enablers. They dont ask requirements from business, they suggest new innovative ideas. Data scientists have a startup mindset, and are continuously building data products that enable new business models, which may or may not be within the same industry of the parent organization. Industry boundaries are blurring, and data scientists are the change agents.