Book Image

Mathematica Data Analysis

By : Sergiy Suchok
Book Image

Mathematica Data Analysis

By: Sergiy Suchok

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
About the Author
About the Reviewer

Mathematica's information depository

We have considered an elementary example of time series with abstract values; however, in practice, we have to analyze large arrays in order to find patterns and dependencies and to make conclusions from these. In this section, we will review what data has been already collected by Mathematica for our use.

We can take demographic data and use any statistics of the country: GDP, unemployment rate, population, and so on:

Using the FinancialData function, you can access the values of various financial instruments such as share prices and exchange rates.

For example, you can determine the periods of the highest index volatility of Standards & Poor's 500 using the following functions:

In this case, we also got familiar with the MovingMap function that built the time series with the standard deviations of S&P500 index based on the previous 3-year daily data. As you can see, the greatest jump falls during the 2009 crisis.

Using the WeatherData function,...