An Introduction to Data Science With Python

by Jeffrey S. Saltz, Jeffrey Morgan Stanton

Overview

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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Product Details

ISBN-13:	9781071850664
Publisher:	SAGE Publications
Publication date:	05/29/2024
Sold by:	Barnes & Noble
Format:	eBook
Pages:	312
File size:	26 MB
Note:	This product may take a few minutes to download.

About the Author

Jeffrey S. Saltz is an Associate Professor at Syracuse University in the School of Information Studies and Director of the school′s Master′s of Science program in Applied Data Science. His research and teaching focus on helping organizations leverage information technology and data for competitive advantage. Specifically, his current research focuses on the socio-technical aspects of data science projects, such as how to coordinate and manage data science teams. In order to stay connected to the “real world”, Dr. Saltz consults with clients ranging from professional football teams to Fortune 500 organizations. Prior to becoming a professor, Dr. Saltz′s two decades of industry experience focused on leveraging emerging technologies and data analytics to deliver innovative business solutions. In his last corporate role, at JPMorgan Chase, he reported to the firm′s Chief Information Officer and drove technology innovation across the organization. Jeff also held several other key technology management positions at the company, including CTO and Chief Information Architect. He also served as Chief Technology Officer and Principal Investor at Goldman Sachs, where he helped incubate technology start-ups. He started his career as a programmer, project leader and consulting engineer with Digital Equipment Corp. Dr. Saltz holds a B.S. degree in computer science from Cornell University, an M.B.A. from The Wharton School at the University of Pennsylvania, and a PhD in Information Systems from the New Jersey Institute of Technology.

Jeffrey M. Stanton, Ph.D. is a Professor at Syracuse University in the School of Information Studies. Dr. Stanton’s research focuses on the impacts of machine learning on organizations and individuals. He is the author of Reasoning with Data (2017), an introductory statistics textbook. Stanton has also published many scholarly articles in peer-reviewed behavioral science journals, such as the Journal of Applied Psychology, Personnel Psychology, and Human Performance. His articles also appear in Journal of Computational Science Education, Computers and Security, Communications of the ACM, Computers in Human Behavior, the International Journal of Human-Computer Interaction, Information Technology and People, the Journal of Information Systems Education, the Journal of Digital Information, Surveillance and Society, and Behaviour&Information Technology. He also has published numerous book chapters on data science, privacy, research methods, and program evaluation.  Dr. Stanton′s research has been supported through 19 grants and supplements including the National Science Foundation’s CAREER award. Before getting his PhD, Stanton was a software developer who worked at startup companies in the publishing and professional audio industries. He holds a bachelor′s degree in Computer Science from Dartmouth College, and a master′s and Ph.D. in Psychology from the University of Connecticut.

Table of Contents

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

Editorial Reviews

"A book focused on providing an introduction to data science with a breadth of topics that might stir up interest in further exploration."

John Bono

"Easy to understand, useful, practical."

Yi Liu

"I have not come across another similar book on Python. The content, structure, and writing style of this book are all quite unique because it is about Python."

Minjuan Wang

"Useful, direct text for teaching data analysis using Python."

James N. Maples

"This book could expand our students' knowledge base and help them build new data analysis skills."

Miao Guo