Start Your Data Science Engine
Getting into Data Science as a Career
In recent months, I was asked a few times, “How do I start the Data Science journey?”
The first step, learn mathematics and statistics. With good foundation comes good understanding. Remember that mathematics and statistics are the “processes” that turns raw data into insights. Having a good understanding of the maths and stats behind the scene is important. They are the foundations of machine learning models as well.
The second step, learn machine learning and pick up a language. Understand how machine learning works, the mathematics behind, the strengths and weaknesses for each one, the built-in assumptions, etc. Parallel processing, pick up a language. You can start with Python first but do not neglect R, for open source tools.
Third step, do a project. Where to get data? There are many open datasets you can get your hands on, you just need to look hard enough. You can check out my other posts listed at the end of the newsletter for ideas on where to look for data. And remember to document your project!
Fourth step, do not stop, keep building up your project. Turn it into a portfolio. The more (passion) project you have, you get to showcase more of your capabilities, knowledge, and skills.
Remember this, data science is not about complex algorithms or fancy tools. It is about how to solve business challenges with data. That is where all the value from data science comes from. So focus your learning and project portfolio management towards that.
Broadly here are the steps you can take. If you need more details, do check out my posts listed here.
And I have launched my podcast channel, "Symbolic Connection”. Check out the 3rd episode as it shares in more detail how you can start your data science career. :)
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