What you'll learn

Learn how to understand data and hone your skills in inferential, descriptive, and hypothesis testing statistics.

Discover how to use descriptive statistical measures, such as mean, median, variance, and standard deviation, to summarize and understand data.

Python tools for cleaning, modifying, and analyzing real-world data include pandas, numpy, seaborn, matplotlib, scipy, and scikit-learn.

Establish a methodical procedure for data analysis that includes conversion, cleaning, and the use of statistical techniques to guarantee quality and accuracy.

Learn how to set up, run, and comprehend one-sample, independent sample, crosstabulation, association tests, and one-way ANOVA for hypothesis testing.

Gaining a rudimentary understanding of regression analysis will enable you to foresee and model variable relationships—a critical skill for making informed deci

Use python to show complex, interactive statistical visualizations including box plots, KDE plots, clustered bar charts, histograms, heatmaps, and bar plots.

Full explanation on each Python code that is used to solve statistical challenges. This will make the use of statistical analysis more clear.