I’m a data science self-learner. I’ve been challenging myself with the data world for a few months now and decided to share how/what I’m doing and a few tips that are helping me in this journey so far.

Switching to data science

I think data science is so fascinating because of its beauty of bringing together different backgrounds. Data has so much to tell that can be analyzed from many different angles.

However, this can also make the field very scary. When we start looking into data science, the biggest challenge we face is being overwhelmed by the number of things we have to learn — coding, statistics, machine learning, domain knowledge, communication, and business skills.

When I started studying a few months ago, I felt completely lost and didn’t know how to create my learning path. As a person who comes from scientific research in biological sciences, I’m familiar with analyzing data, testing hypotheses, basic statistics, and the scientific mindset. All this knowledge is readily transferable to data science. Yet, I had no experience with coding, advanced statistics, nor working with big data sets.

Nevertheless, learning programming has been the most challenging and interesting thing I’ve ever done in my short 25 years.

How to start?

Self-Learning is not easy but it can be a fun journey. Of course, it requires tremendous discipline, dedication, and motivation. Also, it’s very easy to get lost in the medium of endless online courses and certifications that are available. On the other hand, you can draw your own path, learn what you’re really interested in and in the way that suits you better.

If you google how to become a data scientist, you’ll rely on huge lists with MOOCs (Massive Open Online Courses), Bootcamps, books, and required skills — the amount of content out there can be scary!

Hence, this is what I’m doing to not get lost (or try not to) while keeping myself motivated.

#self-study #online-learning #towards-data-science #data-science #machine-learning