Original article was published by Soner Yıldırım on Artificial Intelligence on Medium
The More I Learn About Data Science, The Less I Feel I Know
What is there to learn anyway?
Data science is a fancy field. The numerous applications and products of data science attract the attention of many companies and individuals. More and more businesses invest in this field to create value out of data. The “sexiness” of the field combined with the high demand drives lots of people to start a career in data science.
I was one of many who were attracted. After a short introduction, it was inevitable for me to pursue a career in this field. So, I did start learning more and more.
In the first couple of months, I felt like I was learning a lot which motivated me further. I was collecting certifications, completing online tutorials, reading papers, learning about the underlying principles of algorithms, and so on.
After some time, I realized that the more I learn, the more there is to learn. Although I had learned a lot, I felt like I was not doing well. I even thought my skills and learning techniques were inadequate.
If you feel like how I did in your data science journey, don’t worry. I will try to explain why you should not.
That feeling of insufficiency, some might call it “imposter syndrome”, made me stop and think for a while. In order to keep learning, I had to leave this feeling behind.
In this post, I explain, in my opinion, the reasons for this feeling and the solutions that helped me focus on my learning journey.
Accept there is too much to learn
Data science is an interdisciplinary field that consists of math, statistics, software, and algorithmic thinking. On top of those, depending on what we work on, domain knowledge can be a significant requirement. Thus, I accepted there is too much to learn and it’d be an unnecessary burden to even try.
Learn what you need to.
My motto became to learn what you need to. I tried to avoid overlearning. It is absolutely normal that my software skills cannot compete with the skills of a software developer. I needed math but not like a person majoring in math.