What this book covers

Chapter 1, Preparing the Data, covers the process of reading and writing from and to various data formats and databases, as well as cleaning the data using OpenRefine and Python.

Chapter 2, Exploring the Data, describes various techniques that aid in understanding the data. We will see how to calculate distributions of variables and correlations between them and produce some informative charts.

Chapter 3, Classification Techniques, introduces several classification techniques, from simple Na?ve Bayes classifiers to more sophisticated Neural Networks and Random Tree Forests.

Chapter 4, Clustering Techniques, explains numerous clustering models; we start with the most common k-means method and finish with more advanced BIRCH and DBSCAN models.

Chapter 5, Reducing Dimensions, presents multiple dimensionality reduction techniques, starting with the most renowned PCA, through its kernel and randomized versions, to LDA.

Chapter 6, Regression Methods, covers many regression models, both linear and nonlinear. We also bring back random forests and SVMs (among others) as these can be used to solve either classification or regression problems.

Chapter 7, Time Series Techniques, explores the methods of handling and understanding time series data as well as building ARMA and ARIMA models.

Chapter 8, Graphs, introduces NetworkX and Gephi to handle, understand, visualize, and analyze data in the form of graphs.

Chapter 9, Natural Language Processing, describes various techniques related to the analytics of free-flow text: part-of-speech tagging, topic extraction, and classification of data in textual form.

Chapter 10, Discrete Choice Models, explains the choice modeling theory and some of the most popular models: the Multinomial, Nested, and Mixed Logit models.

Chapter 11, Simulations, covers the concepts of agent-based simulations; we simulate the functioning of a gas station, out-of-power occurrences for electric vehicles, and sheep-wolf predation scenarios.