Why do you wish to be a Data Scientist? | Self Assessment (2023)

Why do you wish to be a Data Scientist? | Self Assessment (1)

All of us know and Google Trends have validated the fact that anyone even remotely associated with ‘data’ wants to be a ‘Data Scientist’. Be it professionals or high school students, this is the latest craze in technology domain. There has been a huge surge in the number of people who have enrolled in one or the other eLearning portals like Udemy, Coursera, DataCamp, etc. Leaderships are ready to change their innovation strategy focusing more on churning of their unused/used data sources to get insights of every aspect of their present/future business model. Even small businesses like my neighborhood restaurant in downtown wants to strategically classify their guests and with all this buzz around, I literally needed to live on an isolated island to stay away from this. Allow me to begin with a thought-provoking analysis of aspiration agenda for this new generation aspirants by Eric Weber.

Why do you wish to be a Data Scientist? | Self Assessment (2)

Portraying myself as the protagonist in this aspiration scenario, I would like to start by saying that it all started couple of months back when I got fully convinced with the trending idea and decided to march to the same drum. Being a Sr. Data Analyst and also a DBA for sometime now, I realized that I have already been around petabytes of data till date, so the transition shouldn’t be that difficult with my expertise in Python, SQL, Excel and reporting tools like PowerBI, SAP BO, etc. Now first assignment in my head was to research what a Data Scientist actually does and skill sets they possess, where resources like LinkedIn Jobs, Analytics Vidhya, KDNuggets, etc. came in handy. I started jotting down all the intrinsic skill sets and was very soon overwhelmed with the abundance of knowledge required to become a Data Scientist.

Suddenly for next 2 days, I was swimming downstream before an article flashed in front of me — Data Scientist Skills & Salaries. Every coin has two sides, and we need to be extremely careful while choosing our’s because unfortunately I chose the wrong side, as the only thing I (unconsciously)focused on was the Data Scientist Salary, associated perks,recognition and a lavish life ahead. Tadaa! I was all set to begin my learning path (with gluttonous intentions). Evolving from a Finance background, I knew that it isn’t going to be easy for me to learn all the tools required (especially Statistics). Since I didn’t know anyone around me with related skills, I chose DataCamp and Udemy for learning more on Python (OOP, Decorators, etc.) to begin with, and then moved on to a detailed course on Data Science and Machine Learning to finish off (at least that is what I then thought of) what is required of me.

Meanwhile, there was one good thing that I did (or assumed so), i.e. started connecting with Data Science experts & visionaries on LinkedIn. It was almost over a month now and I was struggling big time to cover all those topics included in the course along with my regular job. It was getting worse everyday with my habit of digging every aspect of each topic because by now not just at home but also at work I always had a whiteboard next to me filled with mathematical equations. And there came a time when I quit studying for almost a week because I wasn’t able to recollect most of what I had learned. This was extremely depressing for me because instead of coming closer to my king-size lifestyle and fancy perks, I was getting even more distant from it. I desperately needed to restructure my plan so I took a week off from work and headed solo to my favorite getaway destination. For this entire week, I peacefully channelized all my energy into reassessing my aspiration adventure because even the thought of being a slacker literally scared me. It is then, that I realized that from the very start I chose wrong side of the coin.

The other side of the coin had a golden rule embedded in it — Interest leads to Passion and that feeds forward to knowledge, and then the rest automatically follows, if determined. Finally, I had a smile on my face and kind of knew what needs to be done. I don’t need to study these algorithms to get a job because I already have one and I’m quite content with it. With reshuffled strategies, now my primary task was to decide what exactly am I passionate about, and luckily the answer remained to be quite on path — Divine power of Brain. Next self-assigned task was to understand my own brain & it’s interest, and that made me pursue Applied Statistics in-depth to better comprehend to each algorithm of Machine Learning.

Abruptly(for good), things automatically started changing for me and this matrix wasn’t looking that difficult to decode. I finished an introductory course on Data Science with Python from Jose within next 3 weeks and I could remember everything from it. Additionally, I had been practicing heavily with data sets from online repositories. I started taking little pride in my efforts, and these days it is all that matters to me. Actually Data Science isn’t a topic for me to study anymore, it is a topic of interest that I have developed so this one course can’t be the end because now I was hungry for more knowledge like never before. Suddenly the abundance of knowledge in ML/AI started fascinating me instead of discouraging. Hence, I enrolled into another Machine Learning training from Kirill and Hadelin where they cover majority of ML algos though they don’t really get into core Mathematics (but do provide links to infamous research works by others). With all the knowledge imparted by these amazingly talented instructors, it just requires extra effort to understand the core mathematics and science behind every step. Jose was even kind enough to gift me his TensorFlow course free of cost. Gradually implementation wasn’t anymore about using Scikit-Learn, PyTorch, Keras, OpenCV or TF to get things done but to understand their functionality (big thanks to Open Source community) and hard-code those on my own.

On the other hand, LinkedIn also has been a great resource because experts like(to list a few) Daniel Tunkelang, Ben Taylor, Beau Walker, Carla Gentry, Brandon Rohrer, Eric Weber, Andy Kriebel, Andrew Trask and Matthew Mayo almost everyday share a lot of their knowledge, best approach and practical guidance which is immensely beneficent in segregating the best from the rest. There are many other resources freely available online for budding Machine Learning enthusiasts to learn from. Every individual (including experts) have a different perspective but going by whom I follow, I won’t consider myself ready for the big game unless as Ben says “Have you ever looked into Scikit-Learn behind-the-scene and tried to understand and then code it yourself….Have you made yourself uncomfortable?…Have you LOVED Data Science enough?”. Though when the going gets little tough with the overwhelming amount of knowledge, I prefer relaxing with what Brandon says “Being a Generalist is OK”.

Sole intention is to make our community of aspiring Data Scientists realize (with my own limited understanding) that we are on the right path but have we thought of ‘Why are we on this path?’. I realized it with my own mistakes that I cannot jump directly into the Data Science field because it has to be a natural progression. Rome wasn’t built in a day!

“Do we remember our high school days when we wanted everyone to treat us like Adults but were never treated so, until we literally were Adults.”

In Data Science domain as well, there is a step-by-step progression that would lay the foundation for us. There is nothing more amazing than starting with a Data mining/analyst skill set, followed by Big Data, and by then we would automatically know what our actual strength is and which way can we head to achieve our goal. Research is the key to any problem in life and history has proved it every now & then, so exploit Stack Overflow, Quora, etc. or even better if we get a book (even an eBook) or Research papers on a subject and invest time in it.

Time is Money so better we invest it wisely, instead of blindly following what others around us have been doing. Need to drill deep within ourselves and figure out our real passion, and it doesn’t necessarily has to be Data Science or Machine Learning. Knowing all of them is good, but it is much better if we make one aspect of this domain as our core strength, like NLP or Computer Vision, or possibly something else. There are thousands of them taking the same online courses that we have been taking, so how are we /85trying to create a difference? Suppose, I am 30 and have a sibling who is 12 (hypothetical, so don’t really have to focus on my parents’ family planning skills), where my brother doesn’t understand the implications of munching too many chocolates every day. Can I explain my repercussion inference model to him because in real world that is pretty much the case with stakeholders. So better we quickly assess which side of coin are we on and I wish good luck to everyone. If you could relate to my story, hope it does motivate to stay on right side of that coin. Good Luck!

Why do you wish to be a Data Scientist? | Self Assessment (4)
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