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

164 Pages·2015·5.189 MB·English

by  Sergiy Suchok

Mathematica Data Analysis
Learn and explore the fundamentals of data analysis
with the power of Mathematica
Sergiy Suchok
BIRMINGHAM - MUMBAI
Mathematica Data Analysis
Copyright © 2015 Packt Publishing
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First published: December 2015
Production reference: 1151215
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ISBN 978-1-78588-493-1
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Credits
Author Project Coordinator
Sergiy Suchok Dinesh Rathe
Reviewer Proofreader
Shivranjan P Kolvankar Safis Editing
Commissioning Editor Indexer
Amarabha Banerjee Rekha Nair
Acquisition Editor Graphics
Manish Nainani Jason Monteiro
Content Development Editor Production Coordinator
Sumeet Sawant Aparna Bhagat
Technical Editor Cover Work
Vivek Arora Aparna Bhagat
Copy Editor
Kausambhi Majumdar
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About 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.
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About the Reviewer
Shivranjan P Kolvankar is a teacher and a passionate embedded system
developer.
He did his masters in instrumentation science from the University of Pune in 2014.
He has worked on statistical process control charts and data analysis for his masters'
thesis.
He has experience in working with Bluetooth low energy, embedded system
development, C#.NET, VB.NET, and Android application development.
Currently, he is working with Teach for India as a teacher with underprivileged
and low income kids. He believes that quality education that caters to the learning
ability of a child is their fundamental right.
He applies a head-heart-hand strategy to teach mathematics.
When he is free, he loves to play the flute and tinker with Arduino and Sensor
Interfacing.
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Table of Contents
Preface v
Chapter 1: First Steps in Data Analysis 1
System installation 1
Setting up the system 8
The Mathematica front end and kernel 10
Main features for writing expressions 11
Summary 14
Chapter 2: Broad Capabilities for Data Import 15
Permissible data format for import 15
Importing data in Mathematica 16
Additional cleaning functions and data conversion 20
Checkpoint 2.1 – time for some practice!!! 22
Importing strings 25
Importing data from Mathematica's notebooks 26
Controlling data completeness 28
Summary 33
Chapter 3: Creating an Interface for an External Program 35
Wolfram Symbolic Transfer Protocol 35
Interface implementation with a program in С/С++ 38
Calling Mathematica from C 40
Interacting with .NET programs 45
Interacting with Java 49
Interacting with R 52
Summary 54
[ i ]
Table of Contents
Chapter 4: Analyzing Data with the Help of Mathematica 55
Data clustering 55
Data classification 58
Image recognition 63
Recognizing faces 65
Recognizing text information 68
Recognizing barcodes 70
Summary 72
Chapter 5: Discovering the Advanced Capabilities
of Time Series 73
Time series in Mathematica 74
Mathematica's information depository 77
Process models of time series 80
The moving average model 80
The autoregressive process – AR 81
The autoregression model – moving average (ARMA) 82
The seasonal integrated autoregressive moving-average
process – SARIMA 83
Choosing the best time series process model 84
Tests on stationarity, invertibility, and autocorrelation 88
Checking for stationarity 88
Invertibility check 89
Autocorrelation check 89
Summary 91
Chapter 6: Statistical Hypothesis Testing in Two Clicks 93
Hypotheses about the mean 93
Hypotheses about the variance 100
Checking the degree of sample dependence 103
Hypotheses on true sample distribution 106
Summary 110
Chapter 7: Predicting the Dataset Behavior 111
Classical predicting 112
Image processing 117
Probability automaton modelling 122
Summary 124
[ ii ]
Table of Contents
Chapter 8: Rock-Paper-Scissors – Intelligent Processing
of Datasets 125
Interface development in Mathematica 125
Markov chains 133
Creating a portable demonstration 138
Summary 140
Index 141
[ iii ]