1. Take the Assessment
  2. Get Counselled
  3. Gain Knowledge
  4. Glimpse Into the Future

Key Skills


As exciting as this sounds, there are some things you’ll need to learn to do the job right:


This is one of the most important skills for a data scientist in today’s age. You can implement your insights better with programming. Your statistics can be analysed and predicted better with programming. You can create your own tools that will be customised to each data set that you have. And there is nothing more valuable than that. You don’t need to know as much as a usual software engineer- but a basic understanding will do. For example, previously the success or failure of entertainment programs like TV shows was done by statistically analysing the reactions of a few thousand people who watched it. These were called TRPs- Television Rating Points. Today, you have close to exact numbers for views of every show that is released on Netflix or Youtube or other such medium. You can also analyse the feedback they receive on various digital media.


This is a skill that requires some imagination. You will need to analyse complex data and come up with methods to experiment with them. The experimenting is constant and continuous in today’s age. But it also needs to add real time value. The most famous example of this is Uber’s surge pricing. They have created a model where supply and demand interact with each other. This produces profitable results for all parties concerned. For example on New Year's Eve, drivers have to be incentivised to go out and pick party goers. The surge price increases if fewer drivers are available and decreases if more drivers are available. This is also localized geographically, so the surge price in various parts of the city will be different.


Your work is mainly leveraged by how you communicate your hypothesis. You need to be a storyteller, a visualiser and a presenter. You will tell the stories that your data has helped you find about the consumer. You will visualize and present the data, insights and hypotheses in an engaging and satisfying manner. Finally you have to have a clear and concise while communicating to the various stakeholders including engineers, designers, sales heads, product managers and many more.


A data scientist cannot work in isolation. You will be iterating all your models on the basis of feedback from other teams. Its crucial that you share your knowledge and understand how it’s used. You need to work towards a common goal with all teams. You will be deeply embedded as a part of various teams so managing relationships with them is key.