About this Book

This book covers data acquisition, cleaning, storing, retrieval, transformation, visualization, elements of advanced data analysis (network analysis), statistics, and machine learning. It is not an introduction to data science or a general data science reference, although you’ll find a quick overview of how to do data science in Chapter 1, ​What Is Data Science?​. I assume that you have learned the methods of data science, including statistics, elsewhere. The subject index at the end of the book refers to the Python implementations of the key concepts, but in most cases you will already be familiar with the concepts.

You’ll find a summary of Python data structures; string, file, and web functions; regular expressions; and even list comprehension in Chapter 2, ​Core Python for Data Science​. This summary is provided to refresh your knowledge of these topics, not to teach them. There are a lot of excellent Python texts, and having a mastery of the language is absolutely important for a successful data scientist.

The first part of the book looks at working with different types of text data, including processing structured and unstructured text, processing numeric data with the NumPy and Pandas modules, and network analysis. Three more chapters address different analysis aspects: working with relational and non-relational databases, data visualization, and simple predictive analysis.

This book is partly a story and partly a reference. Depending on how you see it, you can either read it sequentially or jump right to the index, find the function or concept of concern, and look up relevant explanations and examples. In the former case, if you are an experienced Python programmer, you can safely skip Chapter 2, ​Core Python for Data Science​. If you do not plan to work with external databases (such as MySQL), you can ignore Chapter 4, ​Working with Databases​, as well. Lastly, Chapter 9, ​Probability and Statistics​, assumes that you have no idea about statistics. If you do, you have an excuse to bypass the first two units and find yourself at Unit 47, ​Doing Stats the Python Way​.

About the Audience

At this point, you may be asking yourself if you want to have this book on your bookshelf.

The book is intended for graduate and undergraduate students, data science instructors, entry-level data science professionals—especially those converting from R to Python—and developers who want a reference to help them remember all of the Python functions and options.

Is that you? If so, abandon all hesitation and enter.