My introduction to the world of data science was through a university course called Introduction to Machine Learning I took during my bachelor’s. And it was, by far, my least favorite class of all time. Even though I was a generally high-scoring student, I struggled with it a lot.

At the time, I blamed the professor of the course for not doing a good job explaining things. Though, in retrospect, I think the main problem was my perspective.

Machine Learning was a new way of approaching problems and I didn’t take enough time to internalize the logic behind how it…

What do you need to do to become a data scientist? Based on the internet you need to:

• Learn Python (or R)
• Learn machine learning

Sounds easy enough to follow, right?

Well, not really. If you’ve already been through some of the learning, you might be aware that, things can get out of hand.

It’s pretty straightforward to learn Python. At least the basics of Python. There are many website and online resources. And it’s a programming language after all. After you learn the basics, the actual learning comes from doing.

Next is to learn machine learning. And that is…

Prefer to watch this? Check out the video version on YouTube.

Isn’t Pandas the best? It is such a great library with so much potential and so much flexibility. I remember the times when I just started using it. I immediately fell in love with it.

Don’t get me wrong, it does have a steep-ish learning curve. Not everything is immediately obvious from the start. There are a couple of tricky concepts in Pandas. And these will be the things to take you to 80% with just 20% effort.

Here are the main working principles of Pandas I have picked…

Prefer to watch this? Check out my Streamlit tutorial on YouTube.

Having a portfolio is crucial to land the data science job of your dreams. And as long as you get your hands dirty working with data working with interesting use cases, you will impress your future employer. But you can always go the next mile when it comes to presenting… and make an interactive web app out of the project you built.

Until very recently this required one to learn web development and start complex React or Angular projects but no more!

Streamlit is an amazing tool that makes…

LinkedIn is a great source of people who can help you with your professional life. This could be getting advice, acquiring information about a company, hearing about open positions and much more. Though, in order to get the most out of it, you need to follow a smart approach rather than a shotgun approach of sending connection requests and copy-paste messages to everyone. This involves who you should connect with, how you should approach them and what you should say in your messages. Let’s start with what kind of people you can get in touch with.

1. Make a list of the profiles you’re looking for

It’s very tempting to…

I would understand it if you were worried about data science dying. You’re getting interested in a new field, looking into how to study it or maybe you’re already studying it and then there is this talk of it dying. You see questions on Quora or on Reddit, tons of articles written as clickbait, seemingly naively asking “Is data science dying?”.

Let’s see why not by going back to when the term first started becoming a fact of everyday life.

Data science was born out of a need

‍ There are tons of unnecessary jobs out there. Created by unfunctional, inefficient corporate dynamics. People who manage people who…

Job hunting sometimes feels like looking for a candle in a dark room we’re unfamiliar with. We don’t have complete information, every company has different standards, and it’s unclear where this position might take you in the coming years. This is true, especially in data science because data science work is far from being standardised. There are many layers and levels of how different your work might be than another data scientist’s.

Previously I talked about what type of positions you can end up in (in-house/consultant data scientist/freelancing/etc.) and the difference between the seniority of positions. …

Debugging is a funny-sounding word. The word originates from an actual bug getting in a computer and impeding the computer’s function back in the first computers’ times. Since then it has taken a new meaning. Now, it means finding the source of a problem in your code and resolving it.

When you’re first starting out with coding, debugging your code or resolving errors can be one of the hardest things to do. After all, the courses that teach how to code do not provide you with the tools you need to find the source of a problem and fix it…