Blog posts

Khuyen Tran is a student and a data scientist who got her first job before even graduating. She tells me all about how she got her first job as a data scientist while still studying, how her writing on Medium brought a lot of opportunities to her foot, what resources she used to gain new skills and more. Do not miss this episode!

By now you have probably heard this explanation: ML algorithms learn like humans. You give it examples and it recognizes and remembers the patterns in them. You need to give it a lot of examples, though, so that it can learn accurately. Okay, that’s clear. But HOW does it learn?

Learning Python and Machine Learning are two of the most fundamental things you need to do to become a data scientist. Out of the two, learning Python is relatively easy. Learning machine learning though is when things get very overwhelming very fast. There are many concepts you need to be aware of and they have very intertwined relationships. That's why you need to have a smart strategy when learning them. I talk about my own approach in this article.

In this episode, I talk to Sadie St. Lawrence, the founder of Women in Data. Women in Data is an international organization that aims to increase diversity in data careers by providing awareness and education to its members. Listen to my chat with Sadie to find out how she herself became a data scientist, what advice she has for the aspiring data scientist, how you can also become part of Women in Data and much more!

Two weeks ago, a bunch of people stormed the Capitol building in the U.S. This whole situation reminded me of a saying we have in computer/data science: garbage in, garbage out. In this article, I explain why.

This week on the show I have Nicole Janeway Bills. She works for Atlas Research. After realizing her passion in data analytics, Nicole followed her heart and started the journey that brought her to where she is today. We talk about how she decided to become a data scientist, what qualifications she went through, how she landed her first job and much more.

This week on the show I have Mikiko Bazeley. She is currently busy co-founding Sidewalk.ai, an AI based solution for residential real-estate. We talk about sidewalk.ai, her story of becoming a data scientist, the projects she's involved with and the future of AI. Mikiko also shares her advice to aspiring data scientists on how they can turn their career around in the direction they want.

Pandas can be intimidating at first but it's such a great library with so much potential and so much flexibility. Here are the main working principles of Pandas I have picked up over the years that will boost your coding performance.

Have you ever thought about starting your own business? It is very accessible to anyone who has some time to invest. Because becoming a data scientist or starting a career in data doesn't have to mean that you will work at a company or have a boss. In this episode, I talk to Susan Walsh. She started her own business in an area she wasn't particularly trained on: data. And now her business is flourishing and she's having a lot of fun.