What this book covers
Chapter 1, First Steps, introduces you to all the basic tools (command shell for interactive computing, libraries, and datasets) necessary to immediately start on data science using Python.
Chapter 2, Data Munging, explains how to upload the data to be analyzed by applying alternative techniques when the data is too big for the computer to handle. It introduces all the key data manipulation and transformation techniques.
Chapter 3, The Data Science Pipeline, offers advanced explorative and manipulative techniques, enabling sophisticated data operations to create and reduce predictive features, spot anomalous cases and apply validation techniques.
Chapter 4, Machine Learning, guides you through the most important learning algorithms that are available in the Scikit-learn library, which demonstrates the practical applications and points out the key values to be checked and the parameters to be tuned in order to get the best out of each machine learning technique.
Chapter 5, Social Network Analysis, elaborates the practical and effective skills that are required to handle data that represents social relations or interactions.
Chapter 6, Visualization, completes the data science overview with basic and intermediate graphical representations. They are indispensable if you want to visually represent complex data structures and machine learning processes and results.
Chapter 7, Strengthen Your Python Foundations, covers a few Python examples and tutorials focused on the key features of the language that it is indispensable to know in order to work on data science projects.
This chapter is not part of the book, but it has to be downloaded from Packt Publishing website at https://www.packtpub.com/sites/default/files/downloads/0429OS_Chapter-07.pdf.
