O’Reilly for Higher Education includes more than 45K books from O'Reilly and 200+ publishers, covering programming languages, cloud computing, software architecture, security, artificial intelligence and machine learning, and other IT topics, as well as business, data science and management, mathematics, science and engineering, and professional development/soft skills. OHE also has more than 10,000 videos and audiobooks, plus courses and curated playlists prepared by experts in their fields, and you can create your own shareable playlists.
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This section provides a sample of the types of materials you can find by browsing the subject links on the right.
Computational Finance with R by 
Visualizing Surveys in R by 
Tidy Finance with R by 
Getting Great Results with Excel Pivot Tables, PowerQuery and PowerPivot by 
Using Excel for Business and Financial Modelling: a practical guide, 3rd ed. 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.
R Cookbook, 2nd ed. by
Perform data analysis with R quickly and efficiently with more than 275 practical recipes in this expanded second edition. These task-oriented recipes make you productive with R immediately. Solutions range from basic tasks to input and output, general statistics, graphics, and linear regression.
Python for Data Mining Quick Syntax Reference by
Learn how to use Python and its structures, how to install Python, and which tools are best suited for data analyst work. This handy reference and tutorial covers topics ranging from basic Python concepts through to data mining, manipulating and importing datasets, and data analysis, including illustrative examples. The first part covers core Python including objects, lists, functions, modules, and error handling. The second part covers Python's most important data mining packages: NumPy and SciPy for mathematical functions and random data generation, pandas for dataframe management and data import, Matplotlib for drawing charts, and scikitlearn for machine learning.
Learn Business Analytics in Six Steps Using SAS and R by
Apply analytics to business problems using two very popular software tools, SAS and R. No matter your industry, this book will provide you with the knowledge and insights you and your business partners need to make better decisions faster.
Excel-Based Business Analysis by 
Mastering R: a beginner's guide by 
Getting Started with Enterprise Blockchain by 
