• Title Free SAS® e-Books
• Author(s) SAS Institute
• Publisher: SAS Institute, Inc.
• Paperback: N/A
• eBook: PDF
• Language: English
• ISBN-10: N/A
• ISBN-13: N/A
• Share This:  

Gain insight on SAS solutions and analytics technology with a collection of free e-books, includes Add-In for Microsoft Office, Computer Vision, Natural Language Processing, Programming for R Users, Open-Source Model Management, Forecasting, Visual Analytics, Exploring Modern Regression Methods, Text Analytics, Data Management, Artificial Intelligence, Machine Learning, Visualizing Data, Fraud Analytics, Discovering Data Science, etc.

• SAS Institute provides a complete selection of hard-copy books and electronic products that help customers use SAS software to its fullest potential. Covering a wide spectrum of topics, all books and products are developed and reviewed by technical experts.

• Amazon
• Amazon (SAS Programming and Data Visualization Techniques: A Power User's Guide)

• Statistics, Mathematical Statistics, and SAS Programming
• Probability and Stochastic Process
• Mathematical and Computational Software, MATLAB, etc.
• Financial Mathematics, Mathematical Economics, and Financial Engineering
• Algebra, Abstract Algebra, and Linear Algebra

• Free SAS® e-Books (SAS Institute, Inc.)

• A First Course on Time Series Analysis with SAS (Michael Falk)

  A unique feature of this book is its integration with the statistical software package SAS® (Statistical Analysis System) computing environment. Basic applied statistics is assumed through multiple regression.

• Step-By-Step Programming With Base SAS® Software

  This book provides conceptual information about SAS® software along with step-by-step examples that illustrate the concepts. It answers every question that a new or intermediate SAS® user might have. Examples are thorough, readable, and clear.

• An Introduction to Statistical Learning: with Applications in R

  It provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years.

• Introduction to Probability, Statistics, and Random Processes

  This book introduces students to probability, statistics, and stochastic processes. It provides a clear and intuitive approach to these topics while maintaining mathematical accuracy.

• Statistics Done Wrong: The Woefully Complete Guide (Reinhart)

  Scientific progress depends on good research, and good research needs good statistics. But statistical analysis is tricky to get right, even for the best and brightest of us. You'd be surprised how many scientists are doing it wrong.

• Foundations of Machine Learning (Mehryar Mohri, et al)

  This book is a general introduction to machine learning. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms.

• Understanding Machine Learning: From Theory to Algorithms

  Explains the principles behind the automated learning approach and the considerations underlying its usage. Provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations.

• Probabilistic Machine Learning: An Introduction (Kevin Murphy)

  This book is a comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. It is written in an informal, accessible style, complete with pseudo-code for the most important algorithms.