You are previewing Mathematica Data Analysis.
O'Reilly logo
Mathematica Data Analysis

Book Description

Learn and explore the fundamentals of data analysis with power of Mathematica

About This Book

  • Use the power of Mathematica to analyze data in your applications

  • Discover the capabilities of data classification and pattern recognition offered by Mathematica

  • Use hundreds of algorithms for time series analysis to predict the future

  • Who This Book Is For

    The book is for those who want to learn to use the power of Mathematica to analyze and process data. Perhaps you are already familiar with data analysis but have never used Mathematica, or you know Mathematica but you are new to data analysis. With the help of this book, you will be able to quickly catch up on the key points for a successful start.

    What You Will Learn

  • Import data from different sources to Mathematica

  • Link external libraries with programs written in Mathematica

  • Classify 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

  • In Detail

    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.

    Style and approach

    This book takes a step-by-step approach, accompanied by examples, so you get a better understanding of the logic of writing algorithms for data analysis in Mathematica. We provide a detailed explanation of all the nuances of the Mathematica language, no matter what your level of experience is.

    Downloading the example code for this book. You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If you purchased this book elsewhere, you can visit http://www.PacktPub.com/support and register to have the code file.

    Table of Contents

    1. Mathematica Data Analysis
      1. Table of Contents
      2. Mathematica Data Analysis
      3. Credits
      4. About the Author
      5. About the Reviewer
      6. www.PacktPub.com
        1. Support files, eBooks, discount offers, and more
          1. Why subscribe?
          2. Free access for Packt account holders
      7. Preface
        1. What this book covers
        2. What you need for this book
        3. Who this book is for
        4. Conventions
        5. Reader feedback
        6. Customer support
          1. Downloading the example code
          2. Errata
          3. Piracy
          4. Questions
      8. 1. First Steps in Data Analysis
        1. System installation
        2. Setting up the system
        3. The Mathematica front end and kernel
        4. Main features for writing expressions
        5. Summary
      9. 2. Broad Capabilities for Data Import
        1. Permissible data format for import
        2. Importing data in Mathematica
        3. Additional cleaning functions and data conversion
          1. Checkpoint 2.1 – time for some practice!!!
        4. Importing strings
        5. Importing data from Mathematica's notebooks
        6. Controlling data completeness
        7. Summary
      10. 3. Creating an Interface for an External Program
        1. Wolfram Symbolic Transfer Protocol
        2. Interface implementation with a program in С/С++
          1. Calling Mathematica from C
        3. Interacting with .NET programs
        4. Interacting with Java
        5. Interacting with R
        6. Summary
      11. 4. Analyzing Data with the Help of Mathematica
        1. Data clustering
        2. Data classification
        3. Image recognition
        4. Recognizing faces
        5. Recognizing text information
        6. Recognizing barcodes
        7. Summary
      12. 5. Discovering the Advanced Capabilities of Time Series
        1. Time series in Mathematica
        2. Mathematica's information depository
        3. Process models of time series
          1. The moving average model
          2. The autoregressive process – AR
          3. The autoregression model – moving average (ARMA)
          4. The seasonal integrated autoregressive moving-average process – SARIMA
        4. Choosing the best time series process model
        5. Tests on stationarity, invertibility, and autocorrelation
          1. Checking for stationarity
          2. Invertibility check
          3. Autocorrelation check
        6. Summary
      13. 6. Statistical Hypothesis Testing in Two Clicks
        1. Hypotheses about the mean
        2. Hypotheses about the variance
        3. Checking the degree of sample dependence
        4. Hypotheses on true sample distribution
        5. Summary
      14. 7. Predicting the Dataset Behavior
        1. Classical predicting
        2. Image processing
        3. Probability automaton modelling
        4. Summary
      15. 8. Rock-Paper-Scissors – Intelligent Processing of Datasets
        1. Interface development in Mathematica
        2. Markov chains
        3. Creating a portable demonstration
        4. Summary
      16. Index