Murach’s Python for Data Science, 2/e

These days, data scientists and analysts are in high demand by organizations of all types. This book covers everything you need to hit the ground running with using Python for data science and analysis.

To start, it presents a crash course in using the Pandas and Seaborn libraries for data analysis and visualization. Then, it presents a thorough course in data analysis, including how to use the Scikit-learn library to create statistical models that make predictions. Finally, it presents four case studies that tie these skills together and show how they’re used in the real world.

Who this book is for

This book is for anyone who wants to become a data scientist or data analyst. The only prerequisite is some programming experience in Python or any other programming language. That’s because chapter 1 presents the Python skills that you need for this book.

What this book does

To present the essential Python skills for data science in a manageable progression and at the right pace, this book is divided into 4 sections.

Section 1: Get off to a fast start

This section gets you started fast. First, you’ll learn how to use JupyterLab and Notebooks to organize and work with Python code. Then, you’ll learn how to use the Pandas and Seaborn libraries for data analysis and visualization. By the end of this section, you’ll be able to start doing analyses of your own.

Section 2: The critical skills for success on the job

This section presents the descriptive analysis skills that are critical for success on the job. That includes how to:

• Get data from CSV files, Excel files, JSON files, Stata files, and databases
• Clean data by dropping unneeded rows and columns and fixing missing values, data types, and outliers
• Prepare data by adding columns, modifying the data in columns, and combining data frames
• Analyze data by grouping and aggregating the data, using pivot tables, and more
• Analyze time-series data by reindexing, downsampling, and working with rolling windows and running totals

Section 3: An introduction to predictive analysis

This section presents the predictive analysis skills that you need to create statistical models that make predictions. Although predictive analysis is a large topic that could be an entire book of its own, this section presents the concepts you need to get started with it. More specifically, it shows you how to use the Scikit-learn library to create linear regression models to predict numeric values.

Section 4: The case studies

This section presents four complete analyses that show how the skills in this book can be applied to real-world datasets:

• Polling data for the 2016 presidential election
• Wildfire data from the US Forest Service
• US social survey data
• Basketball shot data from the NBA (National Basketball Association)

These in-depth analyses make sure that you master the professional skills that are in demand today from organizations of all types.

Why you’ll learn faster and better with this book

This book has features that are designed to make it as easy as possible for you to learn new skills faster and retain them better. Here are a few of those features:

• All of the information is presented in paired pages, with the essential syntax, guidelines, and examples on the right page and clear explanations on the left page.
• The hundreds of short examples present usable code for tasks that you are likely to need for your own analyses.
• The exercises at the end of each chapter provide a way for you to gain valuable hands-on experience without any extra busywork.
• The four analyses presented in section 4 use real-world datasets.
• The paired-pages format is ideal for reference when you need to refresh your memory about how to do something.

What software you need for this book

The only software that’s needed for this book is the Anaconda distribution of Python. It includes JupyterLab, Pandas, Seaborn, Scikit-learn, and more.

Appendixes A and B show how to download and install this distribution on both Windows and macOS systems. Then, chapter 1 shows how to get started with JupyterLab.