Data Science is not Rocket Science — my takeaway from GreyAtom 5 day Bootcamp.
By: Swarnalata Patra
Tags: Data Science, 5daybootcamp, Experience, Machine Learning, Become Datascientist

What is the most important thing you would need to do to become a data scientist. The answer is “ Consistency ”. The question that should pop up in your mind now is — are there no prerequisites. Believe me there are none, with reference to your prior education. Though there is a basic very important requirement : you should be willing to code and get your hands dirty by experimenting with data and code. Aaaah!! So, did that bring a smile on your face. This is real. Now, let’s get to the point. How I got started and how that will help you get started too.

Awesome. I was at the same state a few years back. And I won’t lie to you that you can become anything you want. Although through this article I can tell you, how this 5 day bootcamp at GreyAtom gave me motivation and guide to become Data scientist.

Are you fascinated by the title Data Scientist ? Do you wonder who are these techie people ? Does the idea of becoming a Data scientist in a top class company give you goose bumps ?

I have done masters in Artificial Intelligence from K U Leuven university in Belgium. I have worked in Belgium post my studies. I have done projects on machine learning , computer vision , natural language processing. I am looking for a job as data scientist in India and this workshop helped me to identify my weak points and where I should focus more if I want to reach my goal.

I was already in the field of machine learning, so I had an idea of the language used and the platforms needed. But they did provide detailed video to get started with Jupyter notebook, GitHub, and Medium account. So, if you are not a coder, you can still dream to be a data scientist and its not too late. I got myself into writing my first article in Medium. Thanks to GreyAtom. And I will tell you later why blogging is important.

Day1 : More than the programming and mathematical aspects of machine learning, this workshop was about problem solving. You will get an idea of how to think and formulate your problem. Have you heard the saying, half of the answer lies in the question itself. This is so true in Data analysis.

Day2 : What are data scientists ? It should ring a bell in your head. Something to do with data ? That’s right. So we have to explore what the data is trying to tell us. In the workshop we learnt of several basics of data visualization and clean up techniques that help us to understand the data. Once you understand the data, you know where to go next.

Day3 : Here comes the science part. Oh no, don’t worry. Not rocket science. Here we learn to build models. If that sounds complex, in simple words, we make the program understand the data and learn some patterns in it, using various algorithms and predict the futuristic data. We learn to formulate a business problem into machine learning problem, such as classification or regression or other AI problems. In the workshop, we take an example banking domain problem, try to formulate it into a machine learning problem and create a model that will be used to classify the clients into particular categories.

Day4 : Once we had hands on with machine learning models and solving problems, we now discussed about how the solution is going to benefit a specific organization. Is it worth to invest in machine learning / data scientists team to increase revenue of a company. If so, how do we prove that we are the ones who will be able to increase their revenue through our data analysis. Because, if not I then who ? Ask yourself this, and you will know what to do.

Day5 : This was the last day of the workshop. Basically the last workshop of the year 2020. I can definitely say, this gave me a lot of motivation to start from where I am and take steps forward to reach my goal. Everyone’s questions were answered by the mentors: Hardik Gupta and Mayuresh Shilotri. Thank you very much for the amazing workshop.

We did work on a project end to end, except for the deployment part. We got tasks everyday to look at challenges from Kaggle and get our hands dirty with datasets from Kaggle, comment on linked in with our insights on the data. These comments were addressed in the workshop sessions which helped a lot. Access to very useful study materials and video lectures were given for lifetime which will help one to get on track.