This is probably my first blog post on data science. So far, I have written blogs mostly about nutrition, lifestyle, and a little bit about my career on my blog. But being a data scientist is also a big part of my life because I work as a data scientist in a startup.
This is based on my personal experience; everyone will have a different journey. I hope you can gain some tips from this blog post.
How I got started in data science
I have been exploring the data science field since 2018 when I started my master’s. I joined the university to learn cybersecurity. However, I got super interested once I started taking the machine learning class. By the end of my course, I had a pretty good foundation in neural networks, machine learning, and natural language processing.
If you think you have to do a university degree to get into the field, you don’t have to. Even though I got my start through my master’s, it doesn’t have to be like that. If you think data science is something you enjoy, start learning and building things without wasting time. All you need is curiosity and the internet.
Importance of self-learning
When I started my machine learning course, it fascinated me. But I had considerable resistance to getting my hands dirty by building something. Since I was enrolled in a research-based course, I had to get out of my comfort zone and learn things independently.
I enrolled in Andrew Ng’s machine learning course on Coursera(Currently, they have an ML specialization), which made me quickly understand the fundamentals. Then I build some simple classifiers and regression models with data from Kaggle. From there, it was an iterative process of learning and improving.
To be a good self-learner, you must show up consistently and focus on your goals. Consistency and focus are skills that will help you in all walks of your future. On a micro level, this comes down to a lot of introspection, experimentation, and time management.
One of my favorite books I have read about focused work is Deep Work by Cal Newport. The book teaches you to do uninterrupted, undistracted work on a task that pushes your cognitive abilities to their limit. This book is such a good read especially if you are a knowledge worker like a data scientist.
Solid theoretical knowledge is crucial but there is no better way to learn data science than doing a project. My first project was a wine classification model. I got the data from Kaggle and built the model. Played with different algorithms and parameters. After building a couple of models with readily available data, I learned web scraping and collected my own data.
Importance of personal projects
So once I built some projects, I published them on my GitHub. Showing your work is so important when it comes to building a personal brand. You can start writing a blog, teach people what you are learning, and show your work on GitHub.
During this time, I got an opportunity to participate in the Smart India Hackathon, and I jumped on it. We traveled on a train for three days from Kochi to Chandigarh to participate. In the end, it paid off very well; I became solid in my foundations and confident in my skills. Grab the opportunities that present themselves to you. Temporary discomforts are normal on the journey to achieve your goals.
Two books helped me a lot when it comes to starting a personal project that doesn’t have a quick reward. Steal like an artist and Show your work by Austin Kleon.
Publish your projects on GitHub, Write blogs about new technologies you learn and share them on LinkedIn, and Make tutorials on YouTube… There are many ways. As Austin Kleon said in his very famous Book “Show Your Work”,
“If you want people to know about what you do and the things you care about, you have to share.”
Austin Kleon
How I got an internship
My path to a stable job in data science started with my internship. The problem with having a master’s is that you are overqualified for many entry-level positions, and we couldn’t even attend many placement drives in college. Internships are pretty crucial in situations like this.
I applied through the university, there was a test and interview. Finally, two of us got in, and it was unpaid. But I learned quite a few things and got some exposure to Natural Language Processing. Honestly, it was draining to travel back and forth, spending my own money.
For the internship interview, they tested me on my foundational knowledge. And I was mostly self-learning during the project. In the meantime, I luckily (emphasis on luck)got placed in the same company as an engineer.
Getting my first full-time job
I started my first job during the pandemic and got a different project with an obsolete technology stack. One mistake I made here was not applying to other AI companies before joining since I already had an offer.
Eventually, I started reaching out to other research teams in the company who work in AI, and I got an opportunity.
It was a very convoluted and painful process for me to join the team and get approvals for the shift (True story of big corporates).
I worked in my first company for almost two years before making my shift. Learned a lot during the first three years of my career. I dabbled in many areas and got an idea about what interests me and what doesn’t.
I switched because it was a research lab, I was not getting much exposure to real-world data problems, and I was underpaid based on industry standards. And I got burned out. It turns out that when your reward doesn’t match the work you put in, burnout happens.
I took a leap of faith and quit my job first. The three-month notice period in Indian companies prevents you from getting a good offer before leaving your job. Then I took a break, re-evaluated my priorities, and improved my skills. I was even thinking about becoming a designer😁.
I applied to almost 100 companies before getting around seven responses. But finally, I made a switch with more than a 100% hike.
I learned that even though you are working somewhere, leave time to upskill and learn things. Stay updated on the market trends and how the industry is evolving. Network with people. Join communities. To be honest, data science is not a promising career for people unwilling to update their knowledge constantly.
Is data science boring?
It depends on your personality and priorities. I love the investigative and storytelling part of data science. But the engineering part can be frustrating at times.
Another trick is to get a job in a domain you love. For me, it would be something like nutrition, behavioral science, etc. I’m not there yet. Currently, I’m working for a service-based startup.
Data science is a very valuable skill, and I consider it a tool to solve real-world problems that frustrate me(eventually😁).
A podcast that helped me a lot in terms of where to focus my energy and how to gain specific knowledge is How to Get Rich by Naval Ravikant. According to him code and media are the future. It’s so obvious when you look around us.
Get out of your comfort zone
The human brain hates uncertainty. We live in a world where it’s easier than ever to exist in a comfort zone. But growth happens at the edge of your comfort zone. Embrace new experiences, and meet more people. Block those cheap dopamine pathways.
Everything worth doing is hard.
When I got an opportunity to attend the Smart India Hackathon in 2019, I immediately took that. Traveling on a train for 2.5 days from Kerala to Punjab was UNCOMFORTABLE, but it gave me an edge during the upcoming interviews.
Conclusion
If you are looking to build a career in data science, know that it is a job that requires constant learning and focus. But it is very rewarding in terms of impact and pay(Once you are skilled enough). Anyway, that’s my story of how I became a data scientist. If you like this post, you can read my post about working from home and productivity and how I took a planned career break.
If you like content in video format, check out my YouTube channel and other blog posts.