What this book covers

Chapter 1, First Steps in Data Analysis, describes how to install the Wolfram Mathematica software and starts us off by giving a tour of the Mathematica language features and the basic components of the system: front end and kernel.

Chapter 2, Broad Capabilities for Data Import, examines the basic functions that are used to import data into Mathematica. You will also learn how to cast these data into a form that is convenient for analysis and check it for errors and completeness.

Chapter 3, Create an Interface for an External Program, focuses on the basic skills to transfer accumulated data-processing tools to Mathematica, as well as to use Mathematica's capabilities in computing expressions in other systems.

Chapter 4, Analyzing Data with the Help of Mathematica, covers Mathematica's functions that help to perform data classification and data clustering. You will know how to recognize faces, classify objects in a picture, and work with textual information by identifying the language of the text and recognizing it.

Chapter 5, Discovering the Advanced Capabilities of Time Series, profiles the various ways to process and generate time series. You will find out how time series processes are analyzed and become familiar with the main model type of these processes such as MA, AR, ARMA, and SARIMA. You will able to check observation data for stationary, autocorrelation, and invertibility.

Chapter 6, Statistical Hypothesis Testing in Two Clicks, deals with hypothesis testing on possible parameters. Several examples are provided, which will check the degree of dependence of data samples and test the hypothesis on true distribution of the samples.

Chapter 7, Predicting the Dataset Behavior, takes a moment to look at some useful functions that help in finding regularities and predict the behavior of numeric data. We'll take a look at the possibilities of intelligent processing of graphical information and even imitate an author's style expanding their work or restoring it. Using the methodology of probability automaton modeling, we will be able to build a model of a complex system in order to make predictions with the parameters of the system.

Chapter 8, Rock-Paper-Scissors – Intelligent Processing of the Datasets, tackles the creation of interactive forms to present research results. Also, Markov chains are considered with functions that help in finding the transition probability matrix. In the end, we will cover how to export results to a file for cross-platform presentations.