#### Overview of this book

There are many algorithms for data analysis and it’s not always possible to quickly choose the best one for each case. Implementation of the algorithms takes a lot of time. With the help of Mathematica, you can quickly get a result from the use of a particular method, because this system contains almost all the known algorithms for data analysis. If you are not a programmer but you need to analyze data, this book will show you the capabilities of Mathematica when just few strings of intelligible code help to solve huge tasks from statistical issues to pattern recognition. If you're a programmer, with the help of this book, you will learn how to use the library of algorithms implemented in Mathematica in your programs, as well as how to write algorithm testing procedure. With each chapter, you'll be more immersed in the special world of Mathematica. Along with intuitive queries for data processing, we will highlight the nuances and features of this system, allowing you to build effective analysis systems. With the help of this book, you will learn how to optimize the computations by combining your libraries with the Mathematica kernel.
Mathematica Data Analysis
Credits
www.PacktPub.com
Preface
Free Chapter
First Steps in Data Analysis
Broad Capabilities for Data Import
Creating an Interface for an External Program
Analyzing Data with the Help of Mathematica
Discovering the Advanced Capabilities of Time Series
Statistical Hypothesis Testing in Two Clicks
Predicting the Dataset Behavior
Rock-Paper-Scissors – Intelligent Processing of Datasets
Index

## Summary

In this chapter, we considered how to find regularities and predict the behavior of numeric data with the help of Mathematica. We got to know which parameters of the `Predict` function can improve the quality of a prediction. We also became familiar with the possibilities of intelligent processing of graphical information using the `Inpaint` function and learned to imitate an author's style by expanding their work or restoring it. Using the methodology of probability automaton modeling, we were able to build a model of a complex system to build a prediction with the parameters of a system.

In the next chapter, we will apply most of our knowledge to build a self-learning system with an example of the Rock-Paper-Scissors game.