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 applications.
Discover the capabilities of data classification and pattern recognition offered by MathematicaUse hundreds of algorithms for time series analysis to predict the future.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 a 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 procedures.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 clusters.
Recognize faces, objects, text, and barcodes.
Use Mathematica functions for time series analysis.
Use algorithms for statistical data processing.
Predict the result based on the observations.Sergiy Suchok graduated in 2004 with honors from the Faculty of Cybernetics, Taras Shevchenko National University of Kyiv (Ukraine), and since then, he has had a keen interest in information technology. He is currently working in the banking sector and has a Ph.D. 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.First Steps in Data Analysis.
System installation.
Setting up the system.
The Mathematica front end and kernel.
Main features for writing expressions.Broad 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 series.
The 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 check.Statistical Hypothesis Testing in Two Clicks.
Hypotheses about the mean.
Hypotheses about the variance.
Checking the degree of sample dependence. Hypotheses on true sample distribution.Predicting the Dataset Behavior.
Classical predicting.
Image processing.
Probability automaton modelingRock-Paper-Scissors.
Intelligent Processing of Datasets.
Interface development in Mathematica.
Markov chains.
Creating a portable demonstration.