Suchok S. Mathematica data analysis: learn and explore the fundamentals of data analysis with the power of Mathematica

New York: Packt Publishing, 2015. — 164 p.Use the power of Mathematica to analyze data in your applicationsDiscover the capabilities of data classification and pattern recognition offered by MathematicaUse hundreds of algorithms for time series analysis to predict the futureBook Description
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.
What you will learnImport data from different sources to MathematicaLink external libraries with programs written in MathematicaClassify data and partition them into clustersRecognize faces, objects, text, and barcodesUse Mathematica functions for time series analysisUse algorithms for statistical data processingPredict the result based on the observationsAbout the Author
Sergiy Suchok graduated in 2004 with honors from the Faculty of Cybernetics, Taras Shevchenko National University of Kyiv (Ukraine), and since then, he has a keen interest in information technology. He is currently working in the banking sector and has a PhD in Economics. Sergiy is the coauthor of more than 45 articles and has participated in more than 20 scientific and practical conferences devoted to economic and mathematical modeling.Copyright
Credits
About the Author
About the Reviewer
www.packtpub.comFirst Steps in Data Analysis
System installation
Setting up the system
The Mathematica front end and kernel
Main features for writing expressionsBroad Capabilities for Data Import
Permissible data format for import
Importing data in Mathematica
Additional cleaning functions and data conversion
Checkpoint 2.1 -- time for some practice!
Importing strings
Importing data from Mathematica's notebooks
Controlling data completeness
Summary. Process models of time seriesThe moving average model
The autoregressive process -- AR
The autoregression model -- moving average (ARMA)
The seasonal integrated autoregressive moving-average process -- SARIMA
Choosing the best time series process model
Tests on stationarity, invertibility, autocorrelation, and seasonality
Checking for stationarity
Invertibility check
Autocorrelation checkStatistical Hypothesis Testing in Two Clicks
Hypotheses about the mean
Hypotheses about the variance
Checking the degree of sample dependence. Hypotheses on true sample distributionSummaryPredicting the Dataset Behavior
Classical predicting
Image processing
Probability automaton modellingRock-Paper-Scissors --
Intelligent Processing of Datasets
Interface development in Mathematica
Markov chains
Creating a portable demonstration