8/8/2023 0 Comments Basic data science statisticsThere may be a relationship between them, but there could be other factors as to the cause of the relationship.įor categorical data, this is any data that isn’t a number, which can mean a string of text or date. What this means is that even though two variables may be correlated, doesn’t mean that one variable causes the second variable to react. It’s important to note here something you’ve probably heard before – correlation does not mean causation. Some examples include variables for days in the month, or number of bugs logged. Examples include variables that represent money or height.ĭiscrete numbers are the opposite they have a logical end to them. Numerical data is information that is measurable, and it is, of course, data represented as numbers and not words or text.Ĭontinuous numbers are numbers that don’t have a logical end to them. And categorical data can be broken down into nominal and ordinal values. Numerical data can be divided into continuous or discrete values. There are two types of variables you’ll find in your data – numerical and categorical. Doing this alone can give your business the upper hand by knowing that the data has statistical significance or not. Knowing some basic statistics is extremely helpful whether you are deep into machine learning algorithms or just staying up-to-date on the latest machine learning research.Įven if you don’t want to get that deep into machine learning, these basics will get you on the right path to exploring and extracting meaning from your data. The algorithms and models used in machine learning all come from what’s called statistical learning. Statistics is a form of math, and it involves formulas, but it doesn’t have to be that scary even if you’ve never encountered it before. Statistics is an excellent tool for unlocking such insights in data. One of the central concepts of data science is gaining insights from data. In this post, we’ll cover some basic concepts of data types in statistics and a few ways on how you can collect your own data.
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