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Key Takeaways For Successful Transition To Data Science From My Journey
Latest   Machine Learning

Key Takeaways For Successful Transition To Data Science From My Journey

Last Updated on July 17, 2023 by Editorial Team

Author(s): Himanshu Joshi

Originally published on Towards AI.

Taking the leap of faith -Photo by Joshua Earle on Unsplash

I am a Lead Data Scientist at a Service based IT company with 11+ years of professional experience. I am a self-taught Data Scientist.

Even though there is a lot of content on how to transition to Data Science, while discussing with many acquaintances, it came across that they are having difficulty in transitioning to Data Science. This was the same for both fresh and experienced folks.

So I thought of writing about the things I did right (luckily) during my transition.

My Background :

As an Electronics and Telecommunication Engineer by education, I started my career as an Electrical Engineer at an Electrical Contracting firm before transitioning to Data Science.

So as you can see, I had “Zero” experience in coding. I hardly had any experience working with Excel (very basic level). The biggest mental obstacle I had was I didn't know to code and thought it was very difficult.

Still, somehow I started learning to code (I used DataCamp initially). There were lots of challenges, but I was persistent and learned every day.

Takeaway 1: Anyone can learn to code no matter what their background

Post this, I started with Statistics. I liked statistics but was out of touch, so I started learning it. It took me so many days to research and learn many things. In the end, I again started forgetting things as I had covered many concepts. Later a friend suggested learning basics and deep diving as and when it is required. This basic advice helped me a lot throughout my career.

The same happened with me while learning ML Algorithms, the maths behind the algorithms is quite complex, and there are many of jargon to master for a beginner or even an experienced person

Takeaway 2: You don’t have to learn everything in one go. Small steps taken in the right direction take you a long way

After learning many concepts mentioned above, I started to update my CV and started applying for jobs. This was when I realized I was not getting any interview calls even for several months.

I requested a friend working in a technology company to get me in touch with a Data Scientist working with him. Luckily he helped me, and the Data Scientist asked me to incorporate whatever I had learned into my experience and then apply it. I had just mentioned the Data Science keywords as jargon in my entire CV.

Takeaway 3: Everyone needs guidance, and there is no shame in reaching out to people. Most of the time, people are willing to help. You just need to take the first step.

Later I started getting calls, but I was not able to answer even basic questions that I had studied and knew the answers to.

I still remember during a face-to-face round, a technical guy asked, “Is Logistic regression used for Regression or classification ” I had no answer to such a silly question (I can have a good laugh about it today). I felt so embarrassed that I was almost on the verge of quitting Data Science.

Somehow I hung around.

Takeaway 4: Learn to make mistakes in your stride. What feels like a huge mistake today, you will laugh at it after some years. Always keep a smile on your face (Easy to preach, very tough to implement)

After about 8 to 10 rejections, I understood what the pattern of questions asked in the interviews was. This is when I started to narrate my projects efficiently and started to clear some rounds of interviews.

Takeaway 5: Never ever give up. Take things one at a time. You only fail when you give up. Till the time you haven't given up, there are only 2 outcomes either you pass or learn.

After many, many interviews, I was able to get 1 offer, and I was able to make the transition. ( During my last switch, I got 5 offers from the 8 odd interviews I had given.)

Takeaway 6: As a Data Scientist, you are expected to learn technical concepts, but most of the stakeholders we interact with our Non-technical and hence we are tested on if we are able to explain complex technical concepts in layman's language

Takeaway 7: “A Rocket requires maximum fuel during take-off”. Similarly, the start is the hardest, but if you somehow get through the initial phase, things start getting easier.

The whole idea behind sharing my story was to keep motivating people through my own journey.

I have been able to transition due to so many people's guidance.

As my way to give back to society, I will try to share many such posts along with Functional and Technical knowledge.

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Published via Towards AI

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