An Introduction to Data Science With Python

June 2024 | 312 pages | SAGE Publications, Inc

An Introduction to Data Science with Python by Jeffrey S. Saltz and Jeffery M. Stanton provides readers who are new to Python and data science with a step-by-step walkthrough of the tools and techniques used to analyze data and generate predictive models. After introducing the basic concepts of data science, the book builds on these foundations to explain data science techniques using Python-based Jupyter Notebooks. The techniques include making tables and data frames, computing statistics, managing data, creating data visualizations, and building machine learning models. Each chapter breaks down the process into simple steps and components so students with no more than a high school algebra background will still find the concepts and code intelligible. Explanations are reinforced with linked practice questions throughout to check reader understanding. The book also covers advanced topics such as neural networks and deep learning, the basis of many recent and startling advances in machine learning and artificial intelligence. With their trademark humor and clear explanations, Saltz and Stanton provide a gentle introduction to this powerful data science tool.

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Introduction - Data Science, Many Skills

What is Data Science?

The Steps in Doing Data Science

The Skills Needed to Do Data Science

Identifying Data Problems Through Stories

Case: Overall Context and Desired Actionable Insight

Chapter 1 - Begin at the Beginning With Python

Getting Ready to Use Python

Using Python in a Jupyter Notebook

Creating and Using Lists

Slicing Lists

The Virtual Machine

Shared Python Code Libraries: The Package Index

Chapter 2 - Rows and Columns

Creating Pandas DataFrames

Exploring DataFrames

Accessing Columns in a DataFrame

Accessing Specific Rows and Columns in a DataFrame

Generating DataFrame Subsets With Conditional Evaluations

A Quick Review

Chapter 3 - Data Munging

Reading Data From a CSV Text File

Removing Rows and Columns

Renaming Rows and Columns

Cleaning Up the Elements

Sorting and Grouping DataFrames

Grouping Within DataFrames

Chapter 4 - What’s My Function?

Why Create and Use Functions?

Creating Functions in Python

Defensive Coding

Classes and Methods

Chapter 5 - Beer, Farms, Peas, and Statistics

Historical Perspective

Sampling a Population

Understanding Descriptive Statistics

Using Descriptive Statistics

Using Histograms to Understand a Distribution

Normal Distributions

Chapter 6 - Sample in a Jar

Sampling in Python

A Repetitious Sampling Adventure

Law of Large Numbers and the Central Limit Theorem

Making Decisions With a Sampling Distribution

Evaluating a New Sample With Thresholds

Chapter 7 - Storage Wars

Accessing Excel Data

Working With Data From External Databases

Accessing a Database

Accessing JSON Data

Chapter 8 - Pictures vs. Numbers

A Visualization Overview

Basic Plots in Python

Using Seaborn

Scatterplot Visualizations

Chapter 9 - Map Magic

Map Visualizations Basics

Creating Map Visualizations With Folium

Showing Points on a Map

Chapter 10 - Linear Models

What is a Model?

Supervised and Unsupervised Learning

Linear Modeling

An Example—Car Maintenance

Partitioning Into Training and Cross Validation Datasets

Using K-Fold Cross Validation

Chapter 11 - Classic Classifiers

More Supervised Learning

A Classification Example

Supervised Learning With Naïve Bayes

Naïve Bayes in Python

Supervised Learning Using Classification and Regression Trees

Chapter 12 - Left Unsupervised

Supervised Versus Unsupervised

Data Mining Processes

Association Rules Data

Association Rules Mining

How the Association Rules Algorithm Works

Visualizing and Screening Association Rules

Chapter 13 - Words of Wisdom: Doing Text Analysis

Unstructured Data

Reading in Text Files

Creating the Word Cloud

Sentiment Analysis

Topic Modeling

Other Uses of Text Mining

Chapter 14 - In the Shallows of Deep Learning

The Impact of Deep Learning

How Does Deep Learning Work?

Deep Learning in Python—a Basic Example

Deep Learning Using the MNIST Data

I was hoping to find some full and completed examples and possible scripts for commonly used tasks/projects. However, whilst there are examples there are insufficient details that would assist my students at present.

Ms Sherin Nassa

Computer Science & Business Computing, Wolverhampton University

January 2, 2025

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Key features

Hands-on examples, including a case study that runs through all the chapters, are relatable to most students.
Each chapter is accompanied by a standalone Jupyter Notebook file.
A chapter on deep learning, the basis of many recent and startling advances in machine learning and artificial intelligence, introduces the essential ideas behind deep learning and develops a simple predictive model to illustrate the training process.
Brief code blocks help readers break down Python code into digestible chunks.
Chapter Challenge questions at the end of each chapter offer further exercises so readers can expand their knowledge.
Throughout the text, gentle humor helps make Python less intimidating.
A unified bibliography provides many suggestions for follow-up readings.

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An Introduction to Data Science With Python

Sage College Publishing

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