A breakdown of the the fields of statistics you should know for an entry-level data science role with useful resources
Let’s be honest, maths, especially statistics, can be quite scary.
In one of my previous posts, I discussed the mathematics you need to become a high-caliber data scientist. In a nutshell, you need to know three key areas: Linear Algebra, Calculus, and Statistics.
Now, statistics is the most useful and important to grasp fully. Statistics is the backbone of many data science principles, you will use it every single day and even machine learning came from statistical learning theory.
I want to dedicate a whole post with a detailed roadmap of the statistics knowledge you should have as a data scientist and resources to learn all these things.
Obviously, statistics is a massive field, and you can’t learn everything about it, especially with all the active research going on. However, if you have a solid working knowledge of the topics I will go over in this article, then you are in a very strong position.
If you want a full view of the field, this Wikipedia article summarises the whole statistics landscape.
Wikipedia defines a statistic as
“A statistic (singular) or sample statistic is any quantity computed from values in a sample which is considered for a statistical purpose.”
In other words, a statistic summarises information about some given data, sample or population. So, the first thing a buddying data scientist should know is the different summary statistics to describe the data.
Summary statistics generally measure four things: location, spread, shape, and dependence. Below is a list of the key ones you should know:
- Mean, Mode, and Median.
- Variance, Standard Deviation, and Coefficient of Variation.
- Skewness and Kurtosis.
- Percentiles, Quartiles and Interquartile Range.