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

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