Shobhit Nigam leads the data science and engineering division of edtech platform KnowledgeHut. He spent 17+ years honing skills in machine learning, AI and embedded engineering. Apart from developing innovative data solutions, Shobhit trains young professionals in data science, machine learning, AI, and embedded engineering for corporate firms. He has over 1,500 days of training experience with MNCs like Amazon, Mercedes Benz, Samsung, Airbus, Nokia-Siemens, etc.
Analytics India Magazine got in touch with Shobhit to retrace his data science journey.
AIM: What drew you to data science?
Shobhit Nigam: In late 2010, I was researching technologies that would dominate the future. I loved talking to architects and research scientists and reading specialised reports (like Gartner, Forester) to understand futuristic technologies. By early 2012, I was convinced technologies like cloud computing, DevOps, data science, blockchain, and AR/VR held the key to the future.
I pursued data science because I knew it would become massive. However, I didn’t choose data science. It chose me. Back then, we had developed sensor-based technologies for mining minerals for an Australian client. The company wanted us to leverage data to drive efficiency, and we soon started providing predictive insights. That was my first tryst with data science. And I fell in love with data.
AIM: How important is it for data science aspirants to start early?
Shobhit Nigam: It’s like playing a sport. You can reach the peak faster if you start early. I do not believe it is mandatory, though. However, starting early has its advantages. Understanding the domain can take some time, and an early start means you are well prepared when you start your career.
AIM: What kind of projects did you take up in college?
Shobhit Nigam: Honestly, I was more interested in being a good electronics student in college. Most of my projects were around building attractive electronic-based mini products. However, when I embarked on my data science expedition, I did many projects–mostly related to making predictions for sports using publicly available data.
AIM: How important are certifications?
Shobhit Nigam: I want to break this into two categories based on experience:
A) 0-4 years
A professional certificate like a data scientist or ML engineer from a reputed institute carries a lot of weight. Certification in programming language expertise (preferably Python) also counts. Since most of the technologies used in data science are open source, the certifications don’t come from standard organisations. The learner should pick and choose legitimate certification courses. Examples include Data camp, WeCloudData, KnowledgeHut, Springboard, Imarticus etc.
B) 3-12 years
Again, a professional certificate like a data scientist or ML engineer from a reputed institute has a lot of weight. In addition, certain certifications proving your credentials (like cloud expertise, managerial skills etc.) go a long way. I belong to this category and chose to go for certifications and licences such as AWS associate, TOGAF, Agile Scrum Master and Lean Six Sigma green belt.
AIM: How did you land your first job at T-Sysmac? Tell us about your data science journey.
Shobhit Nigam: It’s almost 20 years back. I had started applying to companies all over the country. I got the interview through a third party at T-Sysmac. After four rounds of grilling, I landed a job as a software engineer.
My major career breakthrough as a data scientist was with an Australian mining company. I was heading the team and had to be completely involved in working with data from scratch. Later, I worked on projects like predicting player efficiency in cricket.
Currently, I am working closely with the data science team at KnowledgeHut to create a process for analysing learner data and increasing the efficiency of every learner by customising the knowledge path.
AIM: What made you get into freelance data science consultancy?
Shobhit Nigam: I was already a freelance consultant in 2010. I adapted data science into my training and consultation role. It proved to be quite beneficial as I got to work with clients worldwide.
I have worked on hundreds of training projects. However, I enjoyed working with clients like Amazon, Allstate, Mercedes Benz, Qualcomm, Airbus, etc. In addition, I was involved in data science training for Alstom in the run-up to the company establishing a data science centre in Bengaluru. I have also enjoyed building training modules with Certific Technologies designed to make fresh graduates job-ready professionals.
AIM: You have 1,500+ days of corporate data science training with MNCs. What’s your training method?
Shobhit Nigam: I have established a process over the years to address the training needs of organisations. The idea is to give maximum output in terms of educating the employees. I have been revising the process based on the requirement of training. It starts with spending one to two hours with the project team and training division to understand the objective. Then I work backwards and create a plan for the training.
AIM: How is the freelance scene for data science professionals? What’s your advice for young professionals?
Shobhit Nigam: The freelance opportunity is huge. Every domain now needs data science professionals. Most data scientists in Europe are freelancers. The supply-demand gap is very high. Whether banking, retail, hospitality, or medicine, almost every company needs a data science expert.
My suggestions to young professionals would be to gain sound knowledge; work on projects at a lower cost (or free) to understand and adopt the technology; read about the domain, speak to experts; and be confident.