What this book covers

Chapter 1, First Steps, introduces Jupyter Notebook and demonstrates how you can have access to the data run in the tutorials.

Chapter 2, Data Munging, presents all the key data manipulation and transformation techniques, highlighting best practices for munging activities.

Chapter 3, The Data Pipeline, discusses all the operations that can potentially improve data science project results, rendering the reader capable of advanced data operations.

Chapter 4, Machine Learning, presents the most important learning algorithms available through the scikit-learn library. The reader will be shown practical applications and what is important to check and what parameters to tune for getting the best from each learning technique.

Chapter 5, Visualization, Insights, and Results, offers you basic and upper-intermediate graphical representations, indispensable for representing and visually understanding complex data structures and results obtained from machine learning.

Chapter 6, Social Network Analysis, provides the reader with practical and effective skills for handling data representing social relations and interactions.

Chapter 7, Deep Learning Beyond the Basics, demonstrates how to build a convolutional neural network from scratch, introduces all the tools of the trade to enhance your deep learning models, and explains how transfer learning works, as well as how to use recurrent neural networks for classifying text and predicting series.

Chapter 8, Spark for Big Data, introduces a new way to process data: scaling big data horizontally. This means running a cluster of machines, having installed the Hadoop and Spark frameworks.

Appendix, Strengthening Your Python Foundations, covers a few Python examples and tutorials that are focused on the key features of the language that are indispensable in order to work on data science projects.