I wanted to become a data scientist since I learned that such a job exists. I knew it was perfect for me. Cutting-edge technology and data analytics, what can be better?
I wondered how I can learn the necessary skills. The obvious choice was video tutorials, so I started watching online courses. I have purchased around six courses on Udemy and two or three Coursera courses. I watched dozens of YouTube videos.
I felt in love with data science, but I had no idea how to become a data scientist. I sort of knew what I needed to learn, but I did not know how to start learning it for real.
I wanted to understand the topics, not only learn that they exist, and copy-paste some code. I started doing Kaggle challenges. It was not the perfect choice, either.
Let’s face reality, the majority of Kaggle solutions are just copy-pasted code from top 20 Kaggle notebooks. I could train myself to copy some code, but I had no clue whether it is the best possible code or why does it even work. It was not enough for me. I could not accept that.
Quickly, I realized that I may learn something in such a way, but I won’t be able to show it to anyone or use it in practice.
I needed to practice solving real-world problems. I tried to “do data science” at work. We already had a data team, and they were trying really hard to “squash the competition.”
I needed to find a different way. A way that could not be controlled by the manager of the data team. I had to look for an opportunity outside of that company.
The problem was the fact that I had no real-world experience. I needed not only to learn but also show people that I should be hired because I can get the job done.
I started blogging. First, I was blogging about everything. I wrote articles about software craft, Scala libraries, book reviews, etc. I did write a few texts about data-related stuff, but only some easy ones. Obviously, that was not helping me reach my goal.
One day, I decided that enough is enough. I was wasting time at a job that did not allow me to grow my skills or even fully use the skills I already had. I had to change that.
I made a blogging schedule. I began blogging three times a week. I continued writing about the same topics, but I was doing it more often. It still wasn’t giving me the results I wanted, but I was learning to produce good content faster.
After four months, I limited the topics of my articles to data analytics and machine learning. Of course, I continued writing three articles every week. At that time, it wasn’t a huge effort anymore because of the time I invested in learning my writing skills.
Three months later, a strange thing had happened. I sent CVs to two companies looking for data scientists. I was invited to both interviews, and… they did not ask me any technical questions. They mentioned that they had read my blog, and we talked only about the culture of their organizations.
I was surprised because I had still remembered the interviews from the past, during which I had been grilled for six hours by multiple interviewed who had tried to prove that I had not known anything.
This time, it was different. I did not need to show my technical skills during the interview. I was hired by one of those companies. Finally, I was a data scientist.
I have finally reached the goal, and I was happy. I was coming to work every day and doing something challenging. I was training machine learning models, doing data analysis, and spending most of the day reading research papers.
Was data science good for me?
It wasn’t perfect for long. Soon, I realized that something was missing. It was difficult to admit, but I learned that being a data scientist is not the perfect career path.
I missed software engineering. I missed talking about software architecture and focusing on software craft.
I wanted to be a data scientist, but I also wanted to do sophisticated software engineering. I needed to make a change in my career once again.
I sent my CV to one other company. I went to an interview. One week after sending the CV, I was hired by a company that is often described as the best workplace in the city where I currently live (Poznan, Poland). It was awesome!
Now, I’m a data engineer. It’s perfect for me. I can train machine learning models, build ETL pipelines, write complex software, care about code quality, and plan the architecture of my software.
How did it happen?
I firmly believe that none of that would happen if I did not start blogging regularly. Blogging not only helped me to learn a lot but also allowed me to show to other people what I am capable of doing.
It also helped me stop wasting time learning things that may be useful in the future. Now, when I learn something like that, I write an article about it.
I know that if I learn it and don’t use it, I will forget everything, so I use the blog also as a way to store notes and easily recall the things I used to know.
I know that my story about blogging may look like something requiring a massive investment of time. It is not like that. If I started blogging regularly earlier, I wouldn’t need to write three times a week.
I believe that you can achieve the same results by blogging once a week, every week for a year. For sure, it will take some time. I think that six months is the minimal amount of time you need to build a successful blog that boosts your career.
I think that the biggest problem of aspiring data scientists is standing out of the crowd. Everyone does Kaggle challenges, everyone has a blog and writes articles. Clearly, you need to do something differently.
I think that what makes the most significant difference is demonstrating work ethics and commitment. It is easy to have a blog and post one text a year. If you can regularly produce good quality content, you are no longer part of the wannabes. Instead of that, you become one of the people who know what they are doing and are available for hire.
Free Blogging Course for Aspiring Data Scientists
I have created a free blogging course for aspiring data scientists. If you subscribe to the course, I will send you a lesson every week. During the first month, I am going to show you how to choose the topics of your articles, how to set up your blog quickly, what kind of articles you should write, and how to write them quickly.
Later, I will teach you also where you can effortlessly find ideas for new texts, how to promote your content in social media, how to become a better writer, how you can get more readers without buying ads, and how you can earn money while blogging.
You can subscribe to the course here: https://www.mikulskibartosz.name/how-i-become-data-scientist#blogging_course
Top comments (5)
Congrats on finding something that fits you well - even though it took a bit to get there!
I like that this post shows the power of being a bit more public (blogging) - I didn't embrace that until later in my career, and I realized how much it can help with things like career changes (for me, it was my move into freelancing/consulting).
The next step in the process is to package up a bunch of blog posts, and call it an ebook! A lot of people are scared of writing something so "big" - but if you think of an ebook as just 10 or so blog posts, then it becomes a lot more manageable :)
Have you thought of writing an ebook about data engineering? I'm always curious what people think about that suggestion when I give it :)
This is the very reason I started blogging last year. Now, most of the time, I visit my blog instead of searching for stuff on Google.
You have a typo under the "Learning" section
... I have purchased around six curses on Udemy ....
Thank you ;]
That's brilliant. I've recently started trying to blog more and share my knowledge too after years of meaning to but never having the time. It's great to know that it is a positive thing to do.