This chapter focuses on using Python for statistical analysis in data science. It begins with statistics essentials, teaching how to calculate descriptive statistics like mean, median, variance, and standard deviation using NumPy. The chapter covers data visualization techniques using Matplotlib to create histograms, bar charts, and scatterplots for exploring data patterns. Key topics include importing data using Pandas DataFrames, performing correlation analysis to measure relationships between variables, and conducting statistical inference through hypothesis testing. Students learn to implement t-tests for comparing means between two groups and ANOVA for comparing multiple groups. The chapter emphasizes practical applications through hands-on examples, from analyzing family age data to comparing exam scores across different classes. These statistical techniques form the foundation for more advanced data science work, enabling students to extract meaningful insights from datasets and make data-driven decisions.

After a discussion of best programming practices and a brief summary of basic features of the Python programming language, chapter 1 discusses several modern idioms. These include the use of list comprehensions, dictionaries, the for-else idiom, as well as other ways to iterate Pythonically. Throughout, the focus is on programming in a way which feels natural, i.e., working with the language (as opposed to working against the language). The chapter also includes basic information on how to make figures using Matplotlib, as well as advice on how to effectively use the NumPy library, with an emphasis on slicing, vectorization, and broadcasting. The chapter is rounded out by a physics project, which studies the visualization of electric fields, and a problem set.