Learn Stats for Python: Descriptive Statistics I

In today’s world, pervaded by data and AI-driven technologies and solutions, mastering their foundations is a guaranteed gateway to unlocking powerful insights from data and making effective and reliable data-driven decisions. One such family of foundational notions comes from nowhere other than statistics. Given its versatility and capabilities, as well as its popularity in data analysis and AI applications, learning stats with the aid of the Python programming language is an ideal approach to learning statistical concepts and putting them in practice: all at the same time!

Statology offers a wide range of Python-based stats tutorials that cover virtually every area and topic in statistics you can imagine—from descriptive statistics and data visualization to probability, statistical inference, predictive analysis, and more.

Not sure where to start, amid so many resources available? Don’t fret, here is the solution.

This comprehensive tutorial series, consisting of five parts, curates and links together these “learn stats for Python” tutorials, providing you with a strong foundational learning pathway in both programming and statistics. Each tutorial is designed to be short, straight to the point, and easy to digest.

About Part I: Descriptive Statistics

Part I of the series focuses on tutorials to get started with the most essential pillar of statistics: descriptive statistics. Descriptive statistics encompass tools and techniques to summarize and describe the main characteristics of a dataset, its distribution, variability, tendency, etc.

1. Data Preparation Essentials

The first group of tutorials we curated for you focuses on the essential preliminary steps needed before conducting any statistical analysis (even the most basic ones). These steps include cleaning, normalizing, and transforming your initial data to ensure consistent and accurate analysis results thereafter. Data preparation is essential as it sets the foundation for any subsequent statistical computations. These selected tutorials illustrate how to use Python to perform some of the most frequent data preparation steps:

2. Descriptive Statistics Fundamentals

Now it’s time to plunge into pure stats. The following group of tutorials covers the central notions of descriptive statistics, that is, summarizing and describing the main characteristics of your (previously prepared) data: mean, median, variability, skewness, percentiles, and more. It is important to understand these essentials to uncover the “appearance” of your data: the first step toward interpreting and communicating insightful patterns underlying them.

These two tutorials showcase the calculation of mean, median, and mode, using two different Python libraries:

Meanwhile, other basic statistical properties are covered in these tutorials:

3. Frequencies and Distributions

After learning to calculate the most common statistics used to describe the characteristics of your data, the next natural step is to learn mechanisms to analyze the distribution of the data, bet it across categories or through intervals. Accordingly, the next few tutorials will teach you how to build frequency tables, calculate relative frequencies upon absolute frequencies, and work with contingency tables to summarize relationships between categorical variables, among others.

4. Correlation and Covariance Metrics

To finalize part I of this tutorial series, let’s put together some tutorials aimed at exploring correlation and covariance measures. These are key statistical metrics to analyze and explore the relationship between variables in your data. The tutorials below illustrate how to calculate and interpret several types of correlations, perform correlation tests, and build correlation and covariance matrices for uncovering hidden connections between parts of your data. These tools are very relevant and constitute part of the foundations behind predictive modeling, AI, and machine learning systems: not without reason, making predictions and inferences intelligently entails discovering the hidden relationships in our data.

Coming Up Next

In the next post in this series, we will wrap up with additional and advanced descriptive statistics topics, and move on to statistical data visualization tools.