What this book covers
Chapter 1, Introduction to Data Analysis, teaches you the fundamentals of data analysis, gives you a foundation in statistics, and guides you through getting your environment set up for working with data in Python and using Jupyter Notebooks.
Chapter 2, Working with Pandas DataFrames, introduces you to the pandas library and shows you the basics of working with DataFrames.
Chapter 3, Data Wrangling with Pandas, discusses the process of data manipulation, shows you how to explore an API to gather data, and guides you through data cleaning and reshaping with pandas.
Chapter 4, Aggregating Pandas DataFrames, teaches you how to query and merge DataFrames, how to perform complex operations on them, including rolling calculations and aggregations, and how to work effectively with time series data.
Chapter 5, Visualizing Data with Pandas and Matplotlib, shows you how to create your own data visualizations in Python, first using the matplotlib library, and then from pandas objects directly.
Chapter 6, Plotting with Seaborn and Customization Techniques, continues the discussion on data visualization by teaching you how to use the seaborn library to visualize your long-form data and giving you the tools you need to customize your visualizations, making them presentation-ready.
Chapter 7, Financial Analysis – Bitcoin and the Stock Market, walks you through the creation of a Python package for analyzing stocks, building upon everything learned from Chapter 1, Introduction to Data Analysis, through Chapter 6, Plotting with Seaborn and Customization Techniques, and applying it to a financial application.
Chapter 8, Rule-Based Anomaly Detection, covers simulating data and applying everything learned from Chapter 1, Introduction to Data Analysis, through Chapter 6, Plotting with Seaborn and Customization Techniques, to catch hackers attempting to authenticate to a website, using rule-based strategies for anomaly detection.
Chapter 9, Getting Started with Machine Learning in Python, introduces you to machine learning and building models using the scikit-learn library.
Chapter 10, Making Better Predictions – Optimizing Models, shows you strategies for tuning and improving the performance of your machine learning models.
Chapter 11, Machine Learning Anomaly Detection, revisits anomaly detection on login attempt data, using machine learning techniques, all while giving you a taste of how the workflow looks in practice.
Chapter 12, The Road Ahead, covers resources for taking your skills to the next level and further avenues for exploration.
