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

ISBN-13: 9781484265932 / Angielski / Miękka / 2021 / 416 str.

Jalil Villalobos Alva

Spis treści

1. Introduction

a. What is Data science?

b. Data science and Statistics

c. Data scientist

2. Introduction to Mathematica

a. Why Mathematica?

b. Wolfram Language

c. Structure of Mathematica

d. Notebooks 

e. How Mathematica works

f. Input Form

3. Data Manipulation 

a. Lists

b. Lists of objects

c. Manipulating lists

d. Operations with lists

e. Indexed Tables

f. Working with data frames

g. Datasets

4. Data Analysis

a. Data Import and export

b. Wolfram data repository

c. Statistical Analysis

d. Visualizing data

e. Making reports

5. Machine learning with Wolfram Language

a. Linear Regression

b. Multiple Regression

c. Logistic Regression

d. Decision Tress

e. Data Clustering

6. Neural networks with Wolfram Language

a. Network Data and structure

b. Network Layers

c. Perceptron Model

d. Multi-layer Neural Network

e. Using preconstructed nets from Wolfram Neural net repository

f. LeNet Neural net for text recognition

O autorze

Jalil Villalobos Alva is a Wolfram language programmer and Mathematica user. He graduated with a degree in engineering physics from the Universidad Iberoamericana in Mexico City. His research background comprises quantum physics, bionformatics, proteomics, and protein design. His academic interests cover the topics of quantum technology, bioinformatics, machine learning, stochastic processes, and space engineering. During his idle hours he likes to play soccer, swim, and listen to music.

Blurb

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. 

You will:

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