Pandas

Chapter 3: Probability and Statistics (Mathematical Foundations for Machine Learning)

Chapter 4: Python Data Science Stack (Python Ecosystem and Classical Machine Learning)

Chapter 28: The Data Science Lifecycle (Data Science: Process & Practice)

Chapter 29: Data Acquisition & Wrangling (Data Science: Process & Practice)

Chapter 30: Exploratory Data Analysis (Data Science: Process & Practice)

Chapter 31: Feature Engineering & Selection (Data Science: Process & Practice)

Chapter 32: Data Visualization & Storytelling (Data Science: Process & Practice)

Chapter 33: Descriptive Statistics & Distributions (Statistics & Statistical Inference)

Chapter 34: Probability Distributions in Depth (Statistics & Statistical Inference)

Chapter 35: Sampling & Estimation (Statistics & Statistical Inference)

Chapter 37: Resampling & the Bootstrap (Statistics & Statistical Inference)

Chapter 38: Bayesian Inference (Statistics & Statistical Inference)

Chapter 39: Regression & Generalized Linear Models (Statistics & Statistical Inference)

Chapter 40: Experimental Design & A/B Testing (Statistics & Statistical Inference)

Chapter 42: Intermediate SQL: Joins & Aggregation (Databases & SQL for Data Science)

Chapter 43: Advanced SQL: Window Functions & CTEs (Databases & SQL for Data Science)

Chapter 44: Analytical SQL & Data Warehousing (Databases & SQL for Data Science)

Chapter 45: NoSQL & Modern Data Stores (Databases & SQL for Data Science)

Chapter 46: Time Series Fundamentals (Time Series Analysis & Forecasting)

Chapter 47: Classical Forecasting: ARIMA & ETS (Time Series Analysis & Forecasting)

Chapter 48: Machine Learning for Time Series (Time Series Analysis & Forecasting)

Chapter 49: Python for Data Science in Depth (Programming for Data Science)

Chapter 51: Reproducible Research & Notebooks (Programming for Data Science)