The mentoring classes have been very properly deliberate

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aiml
aiml

Hello, I am Saurabh Ray. Currently, I am working with IBM as a Data Scientist and help in strategic solutions to bring up key Business Insights. We extensively create forecasting models along with image analytics seeking to innovate techniques that can exponentially grow the business. Prior to IBM, I worked with Accenture as a Data Scientist. However, my role was limited to specific ML use-cases and very limited exposure to Data. Through the AIML course, I diversified my knowledge especially building custom Deep learning models.

The biggest challenge was getting exposure. Although I was working as a Data Scientist for 3 years with one of the best organizations worldwide, my scope was limited to predictive modeling. This eventually brings a feeling of dissatisfaction that your knowledge is limited to a fraction of what an actual Data Scientist would do.

Before joining the program, I did try to address the shortcomings by enrolling in multiple online education portals. However, my major experience was the scope of knowledge was limited and was situational-driven programs. Yes, I could understand a CNN model, but to design an architecture with multiple layers, that confidence never boosted. These challenges were very well handled in the PGP program by Amit sir and Arjun sir from IIT Bombay. Not only did this give a clear picture of how to design a CNN architecture, but also how mathematics behind it.

The mentoring session also played a very crucial role in the journey. These sessions were not about a 2-hour lecture. It was rather an interactive round table session where we got to opportunity to discuss the behavior of data, analytics, and real-life situation openly. Also, we got the opportunity to interact with experienced Data Practitioners and get a practical overview of what lies next. The mentoring sessions were very well planned and were an extension of what we learned throughout the week in our lectures. Also, we got the opportunity to discuss a vast number of case studies and practical usage of AI in business. My mentor, Anudeep sir, was very helpful throughout my journey. There were situations where I used to post-mortem the data from all aspects and brought a plethora of queries into the plate with a research mindset. His guidance helped me immensely choose an efficient approach to derive a powerful solution.

Post completion, today I am highly confident in my current state. Certainly, to expect a role where we use all aspects of Data and AI is unlikely. However, I am confident to work on any DS domain.

My advice to data enthusiasts would be to take some time first and understand why they want to pursue a career in Data Science. This is a must that you take up this field out of passion and not by the influence, which sadly is the cause. Next, the process is very important. What I see outside is Data Science has become a business where courses pop up in every second advertisement. People claim to develop you into a full-stack DS in 6 months with placement assurance. Don’t get carried away by such marketing. Learning Data Analytics is a beautiful journey. You need to understand what your data is speaking to you, how to wrangle data, perform EDA, do Descriptive Analytics on it, think of an optimized solution, and innovate every day. This won’t happen overnight as this is not all about the technical specialty. Your thought process, too, is very crucial. So, follow a well-paced path, move one step at a time, and don’t get carried away in the rat race. Eventually, you will land where you deserve. Cheers!

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