6 Statistical Terms I wish I knew before starting my first Data Science Job
As a data scientist, understanding statistical concepts is crucial to making informed decisions about data analysis and modelling. Unfortunately, crucial terms and techniques are often not covered in traditional academic coursework or training programs. This can leave new data scientists feeling unprepared for the complex and varied tasks they are expected to tackle in their first jobs. In this article, we will explore some of the critical statistical terms that I wish I had known before starting my first Data Science job. By the end of this article, you will have a better understanding of some of the essential statistical terms that every data scientist should know, which will help you to optimise your approach and avoid common pitfalls that can lead to inaccurate or unreliable results. Whether you’re just starting out in data science or looking to brush up on your statistical knowledge, I hope you’ll find this article helpful!
1. Bootstrapping
Bootstrapping is a statistical technique that involves resampling your data to generate multiple datasets for analysis. Instead of collecting new data, you use your existing data to create many new samples. This is done by randomly selecting data points from your original dataset with replacements, meaning that the same data point can be…
