Introduction
Data science heavily involves statistics. So, to become a good data scientist you need to have a good grasp of the fundamental statistical concepts. However, when it comes to statistics, it is a massive field, and people – including me – might not know where to start. Well, that’s what the article Basic Fundamentals of Statistics for Data Science by Soumallya Bishayee aims to help with(the article can be found here). It focuses on the fundamentals of statistics that are used in data science so you can first get a grip on the basics and then learn more on the concepts as you go.
Overview of Article
The article is a long one, covering a wide range of statistical topics. They include everything from probability to the central limit theorem, hypothesis testing to regression analysis permutations, and combinations to confidence intervals. While going through the concepts it lists the formulas, visuals of the concept(i.e. graph, table, etc.), an explanation of the concept, examples of the concept if they are relevant, other sub-concepts relating to that overarching concepts, and types of that concept if it is relevant. The article also provides extra links for further reading if the reader is interested(however this doesn’t happen for the majority of topics so don’t count on it).
My Take
Overall, I really liked this article, it gives you a lot of information on the concepts. I really like these types of articles, the type where they give you a basic overview of certain concepts all in one place because it really helps the reader understand the whole picture and identify what parts they are missing and how much to see where they have to learn more. One thing I really liked about this article is that this article gives you both the theoretical explanation of the concept and the real-world examples of the concept. This really helps the reader contextualize and internalize the concept. The way the article is written along with the formatting of the article also adds to the effect of helping the reader understand the information because with the formatting the author makes certain words stand out which enables the reader to always know what they are reading even if they don’t fully get the concept on the first readthrough. Another thing that I really found was useful the visuals, most of the time the visuals presented the formulas which was good because it showed the text of the formulas in the way that it would have been written on paper as opposed to mathematical writing(for example when showing an exponent the exponent was actually the size of a subscript but near the top of the base instead of writing 5^2 where it is harder to understand).
Conclusion
All in all, this article was a really good one, I thoroughly enjoyed reading it and learned a lot from it. Its combination of visuals and explanation of both theory and real-world application made it not only informative but also applicable. I highly recommend you read it when you get the chance(the link to the article can be found here).