This section provides a sample of the types of materials you can find by browsing the subject links below.
Data Science Fundamentals with R, Python, and Open Data by 
A Python Data Analyst's Toolkit by 
Statistics using Stata: an integrative approach, 3rd ed. by 
Building Interactive Dashboards in Microsoft 365 Excel by 
C and Python Applications: embedding Python code in C programs, SQL methods, and Python sockets by 
Data Science Using Python and R by
This book covers the two most widespread open-source platforms for data science: Python and R, so that readers may learn step-by-step how to produce hands-on solutions to real-world business problems, using state-of-the-art techniques. Written for the general reader with no previous analytics or programming experience, there are chapters dedicated to learning the basics and step-by-step instructions and walkthroughs for solving data science problems. Those with analytics experience will appreciate having a one-stop shop for learning how to do data science using Python and R. Topics covered include data preparation, exploratory data analysis, preparing to model the data, decision trees, model evaluation, misclassification costs, naïve Bayes classification, neural networks, clustering, regression modeling, dimension reduction, and association rules mining.
Modern Data Science with R, 2nd ed. by
This book illustrates how statistical programming in the state-of-the-art R/RStudio computing environment can be leveraged to extract meaningful information from a variety of data in the service of addressing compelling questions. The second edition is updated to reflect the growing influence of the tidyverse set of packages. All code in the book has been revised and styled to be more readable and easier to understand. New functionality from packages like sf, purrr, tidymodels, and tidytext is now integrated into the text. All chapters have been revised, and several have been split, re-organized, or re-imagined to meet the shifting landscape of best practice.
JavaScript for Data Science by
JavaScript is the native language of the Internet. Originally created to make web pages more dynamic, it is now used for software projects of all kinds, including scientific visualization and data services. However, most data scientists have little or no experience with JavaScript, and most introductions to the language are written for people who want to build shopping carts rather than share maps of coral reefs. This book will introduce you to JavaScript's power and idiosyncrasies and guide you through the key features of the language and its tools and libraries.
A Tour of Data Science: learn Python & R in parallel by
This item covers the fundamentals of data science, including programming, statistics, optimization, and machine learning in a single short book. It teaches the key concepts and topics, while it covers two of the most popular programming languages. Cover statistics, programming, optimization and predictive modelling, and the popular data manipulation tools - data.table and pandas, and includes machine learning algorithms implemented from scratch, linear regression, lasso, ridge, logistic regression, gradient boosting trees, etc. Contents will appeal to data scientists, statisticians, quantitative analysts, and others who want to learn programming with R and Python from a data science perspective.
Data Analytics for the Social Sciences by
This book is an introductory, graduate-level treatment of data analytics for social science. It features applications in the R language, arguably the fastest growing and leading statistical tool for researchers. The book starts with an ethics chapter on the uses and potential abuses of data analytics. Rather than focusing on equations, derivations, and proofs, this book emphasizes hands-on obtaining of output for various social science models and how to interpret the output. It is suitable for all advanced level undergraduate and graduate students learning statistical data analysis.
Data Analytics and Visualization in Quality Analysis Using Tableau by
This book goes beyond the existing quality statistical analysis. It helps quality practitioners perform effective quality control and analysis using Tableau, a user-friendly data analytics and visualization software.
Innovation analytics: tools for competitive advantage by 
Quick Functional Programming by 
