My first step into Data Science was a video on YouTube. A professor was talking about one of his master students who used machine learning to generate song lyrics.
After watching that video, it took me about two years to land my first job as a data scientist. I have a BS degree in electrical engineering and I was always good at math. Thus, it was relatively easier for me to learn math-related topics in data science.
However, I did not have any Programming experience or software knowledge. My statistics knowledge was limited to the measures of central tendency.
Long story short, it was quite a journey which required both hard work and dedication.
I have learned from many different resources such as YouTube videos, Medium articles, MOOC courses, personal blogs, GitHub, and books.
After I started to work as a data scientist, I realized that I missed the best resource for learning: a data scientist.
In this article, I will explain why I think a data scientist is the best resource. I will also explain what and how we can make the best out of this resource.
Why a data scientist?
Let me be clear first. I do not mean a data scientist should sit down and teach you everything. Instead, a data scientist should lead you and shape your learning path.
There is a tremendous amount of resources for learning data science. It is actually very good not to experience a lack of resources. However, having so many things to learn from might turn into a disadvantage if not used wisely.
Consider a giant pool full of numerous fish and you have one fishing rod. Some of the fish are not good for eating. If you know the good and delicious fish and catch a few of them, you will have a nice meal.
If you try to catch all of them, you will be exhausted after you are done. You may even feel too tired to eat. Moreover, you will waste your time catching the fish that are not good for eating.
Let’s draw an analogy here. The pool represents the entire scope of resources for learning data science. Each fish is a different resource. Catching a fish means learning.
Trying to catch all the fish means trying to learn from each and every resource. Just like there are some fish not good for eating, some of the resources are of little or no use.
What an actual data scientist can do is that she can tell you the good fish. She will also tell you how many fish are enough to get you full. As a result, you will focus on the good fish and not waste any time catching the bad ones.
During my two-year journey, I learned a lot of useful and important things. At the same time, I also spent time learning things that I have never used in my data science career.
If I knew what topics or tools to focus at first, I would probably get my first job sooner. Moreover, I would have more time to improve my skills on the important tools.
The most efficient way of solving these problems is to consult someone from the industry. The first candidate, of course, is a data scientist.
Conclusion
If you have decided to become a data scientist, I strongly suggest talking with a data scientist first. You will not only have a better idea about what data scientists do but also learn tricks to get there faster.
Please keep in mind that what data scientists do might change depending on the field they work in. For instance, if you want to work in finance, try to find someone in finance. Although the basic principles and concepts are the same, domain knowledge plays a critical role in some cases.
If you do not have a specific field in mind or cannot find a data scientist in your desired field, any data scientist will also be extremely helpful. Guidance about the fundamental tools and concepts is of great importance as well.
Thank you for reading. Please let me know if you have any feedback.
