Learn how to access analytics from SAS Cloud Analytic Services (CAS) using Python and the SAS® Viya™ platform. SAS® Viya™: The Python Perspective is an introduction to using the Python client on the SAS Viya platform. SAS Viya is a high-performance, fault-tolerant analytics architecture that can be deployed more » on both public and private cloud infrastructures. While SAS Viya can be used by various SAS applications, it also enables you to access analytic methods from SAS, Python, Lua, and Java, as well as through a REST interface using HTTP or HTTPS.
This book focuses on the perspective of SAS Viya from Python. SAS Viya is made up of multiple components. The central piece of this ecosystem is SAS Cloud Analytic Services (CAS). CAS is the cloud-based server that all clients communicate with to run analytical methods. The Python client is used to drive the CAS component directly using objects and constructs that are familiar to Python programmers.
Some knowledge of Python would be helpful before using this book; however, there is an appendix that covers the features of Python that are used in the CAS Python client. Knowledge of CAS is not required to use this book. However, you will need to have a CAS server set up and running to execute the examples in this book. With this book, you will learn how to:
* Install the required components for accessing CAS from Python
* Connect to CAS, load data, and run simple analyses
* Work with CAS using APIs familiar to Python users
* Learn about general CAS workflows and advanced features of the CAS Python client
SAS® Viya™: The Python Perspective covers topics that will be useful to beginners as well as experienced CAS users. It includes examples from creating connections to CAS all the way to simple statistics and machine learning, but it is also useful as a desktop reference. « less
With Applications in the Life Sciences
This textbook provides an introduction to the free software Python and its use for statistical data analysis. It covers common statistical tests for continuous, discrete and categorical data, as well as linear regression analysis and topics from survival analysis and Bayesian statistics. Working code more » and data for Python solutions for each test, together with easy-to-follow Python examples, can be reproduced by the reader and reinforce their immediate understanding of the topic. With recent advances in the Python ecosystem, Python has become a popular language for scientific computing, offering a powerful environment for statistical data analysis and an interesting alternative to R. The book is intended for master and PhD students, mainly from the life and medical sciences, with a basic knowledge of statistics. As it also provides some statistics background, the book can be used by anyone who wants to perform a statistical data analysis. « less
50 Essential Concepts
A key component of data science is statistics and machine learning, but only a small proportion of data scientists are actually trained as statisticians. This concise guide illustrates how to apply statistical concepts essential to data science, with advice on how to avoid their misuse.
Many courses more » and books teach basic statistics, but rarely from a data science perspective. And while many data science resources incorporate statistical methods, they typically lack a deep statistical perspective. This quick reference book bridges that gap in an accessible, readable format. « less
The Woefully Complete Guide
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.
Statistics Done Wrong is a pithy, essential guide to statistical more » blunders in modern science that will show you how to keep your research blunder-free. You'll examine embarrassing errors and omissions in recent research, learn about the misconceptions and scientific politics that allow these mistakes to happen, and begin your quest to reform the way you and your peers do statistics.
You'll find advice on:
* Asking the right question, designing the right experiment, choosing the right statistical analysis, and sticking to the plan
* How to think about p values, significance, insignificance, confidence intervals, and regression
* Choosing the right sample size and avoiding false positives
* Reporting your analysis and publishing your data and source code
* Procedures to follow, precautions to take, and analytical software that can help
Scientists: Read this concise, powerful guide to help you produce statistically sound research. Statisticians: Give this book to everyone you know.
The first step toward statistics done right is Statistics Done Wrong. « less
Study guide to accompany A First Course in Probability. Never Highlight a Book Again! Just the FACTS101 provides the textbook outlines, highlights, and practice quizzes.
Over 110 practical recipes to explore the vast array of statistics in Minitab 17
Minitab has been a statistical package of choice across all numerous sectors of industry including education and finance. Correctly using Minitab's statistical tools is an essential part of good decision making and allows you to achieve your targeted results, while displaying fantastic charts and a powerful more » analysis will also communicate your results more effectively.
Minitab Cookbook will take the mystery out of using Minitab and will simplify the steps to produce great results. This book will be hugely beneficial for anyone who knows what statistics or studies they want to run, but who is unsure about just what button to press or what option to select. « less
An Introduction with Statistical Applications
Thoroughly updated, Probability: An Introduction with Statistical Applications, Second Edition features a comprehensive exploration of statistical data analysis as an application of probability. The new edition provides an introduction to statistics with accessible coverage of reliability, acceptance more » sampling, confidence intervals, hypothesis testing, and simple linear regression. Encouraging readers to develop a deeper intuitive understanding of probability, the author presents illustrative geometrical presentations and arguments without the need for rigorous mathematical proofs. « less
An Introduction Using R
A revised and updated edition of this bestselling introductory textbook to statistical analysis using the leading free software package R.
This new edition of a bestselling title offers a concise introduction to a broad array of statistical methods, at a level that is elementary enough to appeal to more » a wide range of disciplines. Step-by-step instructions help the non-statistician to fully understand the methodology. The book covers the full range of statistical techniques likely to be needed to analyse the data from research projects, including elementary material like t-tests and chi-squared tests, intermediate methods like regression and analysis of variance, and more advanced techniques like generalized linear modelling. « less
This Bayesian modeling book provides a self-contained entry to computational Bayesian statistics. Focusing on the most standard statistical models and backed up by real datasets and an all-inclusive R (CRAN) package called bayess, the book provides an operational methodology for conducting Bayesian inference, more » rather than focusing on its theoretical and philosophical justifications. Readers are empowered to participate in the real-life data analysis situations depicted here from the beginning. The stakes are high and the reader determines the outcome. Special attention is paid to the derivation of prior distributions in each case and specific reference solutions are given for each of the models. Similarly, computational details are worked out to lead the reader towards an effective programming of the methods given in the book. In particular, all R codes are discussed with enough detail to make them readily understandable and expandable. This works in conjunction with the bayess package.
Bayesian Essentials with R can be used as a textbook at both undergraduate and graduate levels, as exemplified by courses given at Université Paris Dauphine (France), University of Canterbury (New Zealand), and University of British Columbia (Canada). It is particularly useful with students in professional degree programs and scientists to analyze data the Bayesian way. The text will also enhance introductory courses on Bayesian statistics. Prerequisites for the book are an undergraduate background in probability and statistics, if not in Bayesian statistics. A strength of the text is the noteworthy emphasis on the role of models in statistical analysis.
This is the new, fully-revised edition to the book Bayesian Core: A Practical Approach to Computational Bayesian Statistics.
Jean-Michel Marin is Professor of Statistics at Université Montpellier 2, France, and Head of the Mathematics and Modelling research unit. He has written over 40 papers on Bayesian methodology and computing, as well as worked closely with population geneticists over the past ten years.
Christian Robert is Professor of Statistics at Université Paris-Dauphine, France. He has written over 150 papers on Bayesian Statistics and computational methods and is the author or co-author of seven books on those topics, including The Bayesian Choice (Springer, 2001), winner of the ISBA DeGroot Prize in 2004. He is a Fellow of the Institute of Mathematical Statistics, the Royal Statistical Society and the American Statistical Society. He has been co-editor of the Journal of the Royal Statistical Society, Series B, and in the editorial boards of the Journal of the American Statistical Society, the Annals of Statistics, Statistical Science, and Bayesian Analysis. He is also a recipient of an Erskine Fellowship from the University of Canterbury (NZ) in 2006 and a senior member of the Institut Universitaire de France (2010-2015). « less
With References to R Packages
A new and refreshingly different approach to presenting the foundations of statistical algorithms,Foundations of Statistical Algorithms: With References to R Packages reviews the historical development of basic algorithms to illuminate the evolution of today’s more powerful statistical algorithms. It more » emphasizes recurring themes in all statistical algorithms, including computation, assessment and verification, iteration, intuition, randomness, repetition and parallelization, and scalability. Unique in scope, the book reviews the upcoming challenge of scaling many of the established techniques to very large data sets and delves into systematic verification by demonstrating how to derive general classes of worst case inputs and emphasizing the importance of testing over a large number of different inputs.
Broadly accessible, the book offers examples, exercises, and selected solutions in each chapter as well as access to a supplementary website. After working through the material covered in the book, readers should not only understand current algorithms but also gain a deeper understanding of how algorithms are constructed, how to evaluate new algorithms, which recurring principles are used to tackle some of the tough problems statistical programmers face, and how to take an idea for a new method and turn it into something practically useful. « less