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"Become data-savvy with the widely used data and AI software Data and analytics are essential for any business, giving insight into what's working, what can be improved, and what else needs to be done. SAS software helps you make sure you're doing data right, with a host of data management, reporting, and analysis tools. SAS For Dummies teaches you the essentials, helping you navigate this statistical software and turn information into value. In this...
2) The numerati
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Award-winning journalist Stephen Baker traces the rise of the "global math elite": computer scientists who invent ways to not only record our behavior, but also to predict and alter it. Nowadays, we don't need to be online to create a digital trail; we do it simply by driving through an automated tollbooth or shopping with a credit card. As massive amounts of information are collected, sifted, and analyzed, we all become targets of those who want...
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There's nothing random about it this is the book on statistical analysis with Excel Stunned by statistics? Exhausted by Excel? Relax! Statistical Analysis with Excel For Dummies, 4th Edition shows you how to use the world's most popular spreadsheet program to crunch numbers and interpret statistics even if you've never taken a statistics or advanced math course. In no time, you'll learn to use Excel to create and translate statistics in everyday life,...
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"The dynamic, student focused textbook provides step-by-step instruction in the use of R and of statistical language as a general research tool. It is ideal for anyone hoping to: Complete an introductory course in statistics Prepare for more advanced statistical courses Gain the transferable analytical skills needed to interpret research from across the social sciences Learn the technical skills needed to present data visually Acquire a basic competence...
12) The practice of reproducible research: case studies and lessons from the data-intensive sciences
Summary
"The Practice of Reproducible Research presents concrete examples of how researchers in the data-intensive sciences are working to improve the reproducibility of their research projects. Each of the thirty-one case studies in this volume describes the workflow that an author used to complete a real-world research project, highlighting how particular tools, ideas, and practices have been combined to support reproducibility. Authors emphasize the very...
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The 4th Edition of Google Analytics Demystified covers Google Analytics' current features, paying special attention to those that have been added or updated in the past year. All of these features are explained in easy-to-follow, non-jargon discussions with particular emphasis on interpretation and strategic application. The book helps you better understand how to use and apply Google Analytics data so that you can immediately improve the success...
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"Data Science: An Introduction focuses on using the R programming language in Jupyter notebooks to perform basic data manipulation and cleaning, create effective visualizations, and extract insights from data using supervised predictive models. Based on sound educational research and active learning principles, the book uses a modern approach to the R programming language and accompanying sheets for self-directed learning this book will leave students...
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Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this practical guide-now including examples in Python as well as R-explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not....
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Your library has collected massive amounts of data, and the decision-making process requires that you analyze it. Digital analytics focuses on the digital data: from library websites, electronic resources, online collections, and even social media. Farney presents technical advice that you can use to develop your own digital analytics projects, and includes contributed essays that tackle the challenges your library will face.