#### 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.
Table of Contents (15 chapters)
Mathematica Data Analysis
Credits
About the Author
About the Reviewer
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

## Image processing

One of the unique features of predicting is the obtaining of similar data. For example, a comic impersonator after having learned the peculiarities of a famous person's voice begins to speak with their intonation. In this section, we will see how Mathematica, after having learned the features of an artist's style, can continue his painting. This opens up new possibilities in the field of data restoration.

For example, we'll take Claude Monet's painting Water Lilies:

Using the `ImagePad` function, we cut it off on all sides by 50 pixels. Then, we process the image to enable Mathematica to continue this:

With the help of the `ImageDimensions` function, we have got an array consisting of the length and width values of the image. Then, using the `ImageCrop` function, we have created a new image, which is two times bigger than the previous one. At the same time, we have created an image of exactly the same size that will be used as a mask for further calculations. Note that the black...