The Best Thing I Ever Did

My story about how I got into Data

In 2019, I started to work as an intern at a telecom company. It was a boring 9-5 job with some general Excel tasks. At that time, I was not an Excel expert. We had an Excel course at the university, so at least I knew VLOOKUP. But that was it. I started to learn the relevant keyboard shortcuts, and I got faster.

After the short internship, I got a full-time position in the team. I did the same boring shit, but for more money. Then Covid hit, and I became a zombie staring at Excel sheets for a few hours a day from home office. I hated it, and I wanted something more exciting.

“There must be a better way to do all this.” I thought, and luckily, the company organized a VBA training internally. “Ohh, this will be a better way.” I realized after I googled what the hell VBA is.

I enrolled in the course, and that was the best thing I ever did!

We had 4 sessions about the very basics. Variables, Loops, Functions. It was a programming 101 with VBA. It got me hooked. At the end of the course, we got a script that was able to send automated emails. It felt magical.

For the final project, I automated one of my tasks, saving me 1 hour every week. It was super simple, but I knew that this was something I needed to dig deeper into.

Programming was different back then. We didn’t have ChatGPT, so I learned VBA the OG way. Googling every single line and spending hours on Stack Overflow. It was painful, but I had a mission to automate all my tasks at least partially. And I did. The more I automated, the more I could spend on learning, and the more I learned, the more I automated.

In 6 months, I maxed out the options I had, so I started to look for something more challenging. A Data Analyst role opened internally. It required some BI skills and SQL proficiency. I had none of those, but I still applied.

In the interview, I presented my VBA projects. It showed how passionate I was and my ability to learn. They told me that if in the next few weeks I get decent with SQL, I will get the job.

Projects, 100/10, would recommend and must-haves. You may have a lot of badges on your CV from 1–2 hours Udemy courses, but can you speak about a project you worked on and solved real-world problems? I did not have the SQL badges from Udemy yet, but I solved problems before. No matter which career path you choose, work on some projects!

I bought a SQL course from Udemy. and spent 3–4 hours a day to learn it. I felt the spark with SQL again. I got the job. Maybe the little cute cat helped to stay on the course.

It was a beginner-friendly role, but I learned a lot of SQL and some basics of QLIK. That is a BI Tool that is not so popular for a reason. 2/10 would not recommend. But at least I could tell later that I had some experience with BI.

I knew that the next major step would be to learn Python. I watched some courses on Youtube and took notes. During my journey, I started to follow people on social media, and I heard that if you want to deeply understand something, teach it. I found some other people who did “learning in public” and I gave it a shot myself.

I studied Statistics, Pandas, Matplotlib, basics of ML, and a bunch of other topics. Sharing my journey into data also taught me communication, marketing, design, and business stuff. Blogging is another great thing I did for my career. 10/10 would recommend.

Data Analysis is not just about the data. We need to sell our work, and we need to sell ourselves on interviews, for example. In data, hard skills are overrated, and soft skills are underrated. We should spend more time on communication, sales, and storytelling skills early in our careers. Even if we are not directly working within those areas, the skills are general. You can be the most talented coder if you cannot present your results effectively.

In the meantime, I got accepted to a Swedish university, so I did my masters in Finance there. I wanted to be a Financial Data Analyst. I studied Finance at the Uni, and I studied Data online in front of more and more people.

You may ask why no data at a university? I felt that a Financial degree would be more beneficial later in my career with self-taught data knowledge. I did my niche knowledge at an institution and studied data on my own. Niche knowledge is also really important. If your niche is Biochemistry in data, then the Biochemistry part will be super hard to learn alone.

But if you want to be in Machine Learning, the path without an ML degree will be way harder.

That’s how my career and content game started, and now here we are. I am working in Risk Reporting, so I can use all the knowledge I collected throughout the last few years. I am still learning a lot of data, and I share it here.

To sum it up, here is my roadmap in order of learning the skill:

Excel
VBA
SQL
BI Basics
Python
Python packages for data
Visualization
Statistics

Next on my list:

DAX (since I mainly use Power BI now)
Data Modeling
Cloud

Do I recommend others to follow this roadmap? You probably know the answer. It is always: “It depends.”

I think it has a great progression towards Analytics Engineering, where I am heading.

If you want to be a Data Analyst, it is a great path. You would learn from the ground up. VBA is not a must, but Excel knowledge can be super beneficial. Learning how to work with tabular data, how pivots work, how functions work will be useful. Then, picking up SQL, BI, and Python will set you up for a junior role.

For Data Science and Machine Learning, Math, Statistics, Linear Algebra, and similar topics should be a big part of the roadmap. These are harder skills to master, so self-taught ML Engineers are rare animals. If you want to go down this route, you should consider a degree from a great college.

They say there are no Junior Data Engineers. If this is true, being a Data Analyst is a great starting point, and then you can double down on SQL, data modeling, pipelines, and Cloud.