November 26, 2024
I was never the best at math, but I wasn’t bad either and I always liked it, one way or another. At the same time, I thought for a long time: if you are not good at math, you cannot go into computer science, data analysis, data science or computer science. After a few detours through psychology, I finally found myself in a role as a Salesforce consultant and I successfully completed my degree in business IT and AI, specializing in architecture and software design, with top marks.
But whether I’m programming with Python, building machine learning models, or analyzing data, I encounter math concepts all the time. When using data analysis, automation or Python programming, I don’t think it’s important to understand mathematical topics in every detail. But mathematical understanding comes up very often. A few months ago, I wanted to improve my knowledge of mathematics and logic and I came across the application ‘Bright‘ (no affiliate link). The app explains topics like probability and regression models in a very simple way. The cool thing is that the app is structured similarly to Duolingo and is almost addictive thanks to the gamification… Read until the end of the article to see how many series I have have already completed 💪
In this article, I will introduce you to some of the most important and essential mathematical topics in applied data analysis. If you have beginner or intermediate knowledge and want to deepen your understanding of the keys…
