Beginning Mathematica and Wolfram for Data Science : Applications in Data Analysis, Machine Learning, and Neural Networks

Enhance your data science programming and analysis with the Wolfram programming language and Mathematica, an applied mathematical tools suite. The book will introduce you to the Wolfram programming language and its syntax, as well as the structure of Mathematica and its advantages and disadvantages. You’ll see how to use the Wolfram language for data science from a theoretical and practical perspective. Learning this language makes your data science code better because it is very intuitive and comes with pre-existing functions that can provide a welcoming experience for those who use other programming languages. You’ll cover how to use Mathematica where data management and mathematical computations are needed. Along the way you’ll appreciate how Mathematica provides a complete integrated platform: it has a mixed syntax as a result of its symbolic and numerical calculations allowing it to carry out various processes without superfluous lines of code. You’ll learn to use its notebooks as a standard format, which also serves to create detailed reports of the processes carried out. What You Will Learn Use Mathematica to explore data and describe the concepts using Wolfram language commands Create datasets, work with data frames, and create tables Import, export, analyze, and visualize data Work with the Wolfram data repository Build reports on the analysis Use Mathematica for machine learning, with different algorithms, including linear, multiple, and logistic regression; decision trees; and data clustering Who This Book Is For Data scientists new to using Wolfram and Mathematica as a language/tool to program in. Programmers should have some prior programming experience, but can be new to the Wolfram language. Table of Contents About the Author About the Technical Reviewer Acknowledgments Introduction Chapter 1: Introduction to Mathematica Why Mathematica? The Wolfram Language Structure of Mathematica Design of Mathematica Notebooks Text Processing Palettes Expression in Mathematica Assigning Values Built-in Functions Dates and Time Basic Plotting Logical Operators and Infix Notation Algebraic Expressions Solving Algebraic Equations Using Wolfram Alpha Inside Mathematica Delayed Expressions Code Performance Strings How Mathematica Works How Computations are Made (Form of Input) Searching for Assistance Handling Errors Notebook Security Chapter 2: Data Manipulation Lists Types of Numbers Working with Digits A Few Mathematical Functions Numeric Function Lists of Objects List Representation Generating Lists Arrays of Data Nested Lists Vectors Matrices Matrix Operations Restructuring a Matrix Manipulating Lists Retrieving Data Assigning or Removing Values Structuring List Criteria Selection Chapter 3: Working with Data and Datasets Operations with Lists Arithmetic Operations to a List Addition and Subtraction Division and multiplication Exponentiation Joining a list Applying Functions to a List Defining Own Functions Pure Functions Indexed Tables Tables with the Wolfram Language Associations Dataset Format Constructing Datasets Accessing Data in a Dataset Adding Values Dropping Values Filtering Values Applying Functions Functions by Column or Row Customizing a Dataset Generalization of Hash Tables Chapter 4: Import and Export Importing Files CSV and TSV Files XLSX Files JSON Files Web Data Semantic Import Quantities Datasets with Quantities Costume Imports Export Other Formats XLS and XLSX Formats JSON Formats Content File Objects Searching Files with Wolfram Language Chapter 5: Data Visualization Basic Visualization 2D Plots Plotting Data Plotting Defined Functions Customizing Plots Adding Text to Charts Frame and Grids Filled Plots Combining Plots Multiple Plots Coloring Plot Grids Colors Palette 3D Plots Customizing 3D Plots Hue Color Function and List3D Contour Plots 3D Plots and 2D Projections Plot Themes Chapter 6: Statistical Data Analysis Random Numbers Random Sampling Systematic Sampling Common Statistical Measures Measures of Central Tendency Measures of Dispersion Statistical Charts BarCharts Histograms Pie Charts and Sector Charts Box Plots Distribution Chart Charts Palette Ordinary Least Square Method Pearson Coefficient Linear Fit Model Properties Chapter 7: Data Exploration Wolfram Data Repository Wolfram Data Repository Website Selecting a Category Extracting Data from the Wolfram Data Repository Accessing Data Inside Mathematica Data Observation Descriptive Statistics Table and Grid Formats Dataset Visualization Data Outside Dataset Format 2D and 3D Plots Chapter 8: Machine Learning with the Wolfram Language Gradient Descent Algorithm Getting the Data Algorithm Implementation Multiple Alphas Linear Regression Predict Function Boston Dataset Model Creation Model Measurements Model Assessment Retraining Model Hyperparameters Logistic Regression Titanic Dataset Data Exploration Classify Function Testing the Model Data Clustering Clusters Identification Choosing a Distance Function Identifying Classes K-Means Clustering Dimensionality Reduction Applying K-Means Chaining the Distance Function Different K’s Cluster Classify Chapter 9: Neural Networks with the Wolfram Language Layers Input Data Linear Layer Weights and Biases Initializing a Layer Retrieving Data Mean Squared Layer Activation Functions SoftmaxLayer Encoders and Decoders Encoders Pooling Layer Decoders Applying Encoders and Decoders NetChains and Graphs Containers Multiple Chains NetGraphs Combining Containers Network Properties Exporting and Importing a Model Chapter 10: Neural Network Framework Training a Neural Network Data Input Training Phase Model Implementation Batch Size and Rounds Training Method Measuring Performance Model Assessment Exporting a Neural Network Wolfram Neural Net Repository Selecting a Neural Net Model Accessing Inside Mathematica Retrieving Relevant Information LeNet Neural Network LeNet Model MNIST Dataset LeNet Architecture MXNet Framework Preparing LeNet LeNet Training LeNet Model Assessment Testing LeNet Final Remarks Appendix A Installing Mathematica Index Enhance your data science programming and analysis with the Wolfram programming language and Mathematica, an applied mathematical tools suite. The book will introduce you to the Wolfram programming language and its syntax, as well as the structure of Mathematica and its advantages and disadvantages. You'll see how to use the Wolfram language for data science from a theoretical and practical perspective. Learning this language makes your data science code better because it is very intuitive and comes with pre-existing functions that can provide a welcoming experience for those who use other programming languages. You'll cover how to use Mathematica where data management and mathematical computations are needed. Along the way you'll appreciate how Mathematica provides a complete integrated platform: it has a mixed syntax as a result of its symbolic and numerical calculations allowing it to carry out various processes without superfluous lines of code. You'll learn to use its notebooks as a standard format, which also serves to create detailed reports of the processes carried out. What You Will Learn : Use Mathematica to explore data and describe the concepts using Wolfram language commands ; Create datasets, work with data frames, and create tables ; Import, export, analyze, and visualize data ; Work with the Wolfram data repository ; Build reports on the analysis ; Use Mathematica for machine learning, with different algorithms, including linear, multiple, and logistic regression; decision trees; and data clustering ; The fundamentals of the Wolfram Neural Network framework and how to build your neural network for different tasks ; How to use pre-trained models from the Wolfram Neural Net Repository