What are the most common statistical methods for data analysis in Python?

1 Descriptive statistics

Descriptive statistics are used to summarize and display the basic features of your data, such as the mean, median, mode, standard deviation, range, frequency, and distribution. You can use the pandas library to create data frames, which are tabular structures that store your data in rows and columns. Pandas also provides various methods and functions to calculate descriptive statistics, such as .describe(), .mean(), .median(), .mode(), .std(), .min(), .max(), .count(), and .value_counts(). You can also use the seaborn library to create visualizations, such as histograms, box plots, and scatter plots, to show the distribution and relationship of your data.

2 Inferential statistics

Inferential statistics are used to make generalizations and predictions about a population based on a sample of data, as well as to measure the uncertainty and reliability of those estimates. You can use the scipy library to perform various inferential statistics, such as hypothesis testing, confidence intervals, and correlation analysis. For example, you can use the scipy.stats module to conduct t-tests, ANOVA, chi-square tests, and Pearson's r tests, among others. You can also use the statsmodels library to perform regression analysis, which is a method of modeling the relationship between a dependent variable and one or more independent variables.

3 Multivariate statistics

Multivariate statistics are used to analyze data that involves more than two variables, and to explore the interactions and dependencies among them. You can use the sklearn library to perform various multivariate statistics, such as principal component analysis (PCA), factor analysis (FA), cluster analysis (CA), and discriminant analysis (DA). These methods can help you reduce the dimensionality of your data, identify latent factors or groups, and classify or separate your data into different categories or segments.

4 Bayesian statistics

Bayesian statistics are based on the idea of updating your beliefs about a parameter or a hypothesis based on new evidence or data, using a mathematical formula called Bayes' theorem. You can use the pymc3 library to perform Bayesian statistics, which allows you to define probabilistic models, specify prior and likelihood distributions, and sample from the posterior distribution using Markov chain Monte Carlo (MCMC) methods. You can also use the arviz library to visualize and analyze the results of your Bayesian models, such as trace plots, posterior plots, and forest plots.

5 Machine learning

Machine learning is a branch of artificial intelligence that involves creating algorithms or models that can learn from data and make predictions or decisions. You can use the sklearn library to perform various machine learning tasks, such as classification, regression, clustering, dimensionality reduction, and feature selection. You can also use the keras library to build and train neural networks, which are powerful models that can learn complex patterns and functions from data. You can also use the tensorflow library to perform deep learning, which is a subset of neural networks that can handle large and high-dimensional data, such as images, text, and speech.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?