- How his team used AI to develop a social distancing tool to be used during times of pandemic.
- His advices to beginners in data science who are starting out their careers.
How did you first get into data science?
Right from the university days, I had an inclination towards strategy making which was used to run my own student chapter. I always get fascinated by seeing how data reveals a lot of details and helps you build strategies around that. Those collective moments got me to explore the field of data science and learn new technologies around that. I started learning R and did some basic analytical projects and that’s how it all started.
How is data science used to create value in your current project(s)?
In my current projects, We deal mostly with neural networks and image processing. It always gives us insights and interesting predictions. For example, in this the current pandemic situation, We have developed an AI Social Distancing Tool called Flowity which can be utilized by governments and organization to monitor and control the crowd. It can detect if people are keeping a safe distance from each other by analyzing real-time video streams from the camera in a GDPR safe way!
What are the key skills that you use every day as a data scientist, and how did you develop them?
To become a good data scientist, you need to have a mix of three major skillsets : Business domain knowledge, Statistics, and Programming.
Domain knowledge increases with your day to day experiences.
Check out what books helped 20+ successful data scientists grow in their career.
For learning statistics, you need to read some standard books, pursue online courses, and read research papers.
And Programming, It’s all about the practice and getting myself updated with modern packages and tools.
What are the top challenges you currently face as a professional data scientist, and how do you go about tackling them?
I think the biggest challenge for any professional data scientist is to handle large unstructured datasets especially at the stages of data collection and pre-processing – cleaning, profiling, and quality. The tackling strategies depend on the problem statements as well as datasets. There is no fixed strategy around that. The better the data, the more we are likely to get better results.
Do you create data science content?
Yes, I am in process of launching my own data science online course this year and post that, will be also releasing my next book on that. It will be for business leaders and project managers.
3 words that best summarize how you learned ML and data science:
Explore, Practice & Experience!
People: who are some inspiring data scientists and people in AI that you follow?
I follow the works of many but my idols are Andrew Ng, Yann LeCun and Geoffrey Hinton.
Books: which books have helped you the most in your journey and why?
You need to read a lot of books. But one book I loved was – “Programming Collective Intelligence” by Toby Segaran
Courses: what courses/programs have you taken that have significantly contributed to advancing your career in data science?
I would like to recommend a few fundamental courses listed in Coursera by Andrew Ng, Regular participation in Data Science Challenges hosted at Kaggle and other forums can help anyone to advance their career in data science and be updated with new techniques.
What advice would you give to someone who wants to get into data science today?
Even though there are modern tools and platforms to make our work easy, never ever skip learning the fundamentals. This is the base on which we build data science projects.
Also, always work on your business domain knowledge, which you mostly will get outside the data science circles.
What inspires you about working in Data Science?
I really like the multi-disciplinary approach involved in data science, where we learn every day and find happiness in solving real-life problems and build strategies to mitigate risk and make this world a better place.