An Introduction to Data Science With Python

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. Included with this title: LMS Cartridge: Import this title’s instructor resources into your school’s learning management system (LMS) and save time. Don′t use an LMS? You can still access all of the same online resources for this title via the password-protected Instructor Resource Site.

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

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.

Review :
"Easy to understand, useful, practical." "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." "A book focused on providing an introduction to data science with a breadth of topics that might stir up interest in further exploration." "Useful, direct text for teaching data analysis using Python." "This book could expand our students′ knowledge base and help them build new data analysis skills."