Knowing about the ins and outs of the cloud is pretty useful when you are a data scientist. During the Microsoft Azure ertified Data & AI track of the Techonista Academy I will take 4 Microsoft exams and today I passed my first, the AZ-900 Azure Fundamentals, with flying colors. This exam covers topics ranging from different cloud concepts, core Azure services and management tools to security features, compliance and privacy.

People have asked me what coding environment I use for my projects. I started out with Anaconda which conveniently installed Python, Jupyter Notebook and all common libraries I needed for my projects. However, I found the process of installing new libraries quite complicated. I also ran into problems when I installed a version of Tensorflow that was not compatible with my version of Pandas. Even with the help of the Stackoverflow community this was a problem that I found hard to solve.

I started my first machine learning course on Udemy in May 2020. I quickly realized two things. First, for me simply following along with these courses is not the best way to learn because it doesn’t challenge me to think about a solution myself. And secondly, I was missing some basic knowledge on how to get started with a project. I mean, it is great to know how to perform a regression analysis, but preparing your data is a huge part of data science and this topic is not always covered in these courses.