Got Data? Now What? - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Got Data? Now What? write by Laura Lipton. This book was released on 2012-02-29. Got Data? Now What? available in PDF, EPUB and Kindle. Explore three defining challenges that school teams face when gathering, interpreting, and utilizing school data. Complete with survey questions for efficient data collection, group work structures, strategies, and tools—along with essential definitions and descriptions of data types—this compelling guide will help you confront data obstacles and turn struggling committees into powerful communities of learners.
Big Data
Big Data - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Big Data write by Viktor Mayer-Schönberger. This book was released on 2013. Big Data available in PDF, EPUB and Kindle. A exploration of the latest trend in technology and the impact it will have on the economy, science, and society at large.
Visualize This
Visualize This - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Visualize This write by Nathan Yau. This book was released on 2011-06-13. Visualize This available in PDF, EPUB and Kindle. Practical data design tips from a data visualization expert of the modern age Data doesn't decrease; it is ever-increasing and can be overwhelming to organize in a way that makes sense to its intended audience. Wouldn't it be wonderful if we could actually visualize data in such a way that we could maximize its potential and tell a story in a clear, concise manner? Thanks to the creative genius of Nathan Yau, we can. With this full-color book, data visualization guru and author Nathan Yau uses step-by-step tutorials to show you how to visualize and tell stories with data. He explains how to gather, parse, and format data and then design high quality graphics that help you explore and present patterns, outliers, and relationships. Presents a unique approach to visualizing and telling stories with data, from a data visualization expert and the creator of flowingdata.com, Nathan Yau Offers step-by-step tutorials and practical design tips for creating statistical graphics, geographical maps, and information design to find meaning in the numbers Details tools that can be used to visualize data-native graphics for the Web, such as ActionScript, Flash libraries, PHP, and JavaScript and tools to design graphics for print, such as R and Illustrator Contains numerous examples and descriptions of patterns and outliers and explains how to show them Visualize This demonstrates how to explain data visually so that you can present your information in a way that is easy to understand and appealing.
Data Smart
Data Smart - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Data Smart write by John W. Foreman. This book was released on 2013-10-31. Data Smart available in PDF, EPUB and Kindle. Data Science gets thrown around in the press like it'smagic. Major retailers are predicting everything from when theircustomers are pregnant to when they want a new pair of ChuckTaylors. It's a brave new world where seemingly meaningless datacan be transformed into valuable insight to drive smart businessdecisions. But how does one exactly do data science? Do you have to hireone of these priests of the dark arts, the "data scientist," toextract this gold from your data? Nope. Data science is little more than using straight-forward steps toprocess raw data into actionable insight. And in DataSmart, author and data scientist John Foreman will show you howthat's done within the familiar environment of aspreadsheet. Why a spreadsheet? It's comfortable! You get to look at the dataevery step of the way, building confidence as you learn the tricksof the trade. Plus, spreadsheets are a vendor-neutral place tolearn data science without the hype. But don't let the Excel sheets fool you. This is a book forthose serious about learning the analytic techniques, the math andthe magic, behind big data. Each chapter will cover a different technique in aspreadsheet so you can follow along: Mathematical optimization, including non-linear programming andgenetic algorithms Clustering via k-means, spherical k-means, and graphmodularity Data mining in graphs, such as outlier detection Supervised AI through logistic regression, ensemble models, andbag-of-words models Forecasting, seasonal adjustments, and prediction intervalsthrough monte carlo simulation Moving from spreadsheets into the R programming language You get your hands dirty as you work alongside John through eachtechnique. But never fear, the topics are readily applicable andthe author laces humor throughout. You'll even learnwhat a dead squirrel has to do with optimization modeling, whichyou no doubt are dying to know.
Doing Data Science
Doing Data Science - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Doing Data Science write by Cathy O'Neil. This book was released on 2013-10-09. Doing Data Science available in PDF, EPUB and Kindle. Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know. In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science. Topics include: Statistical inference, exploratory data analysis, and the data science process Algorithms Spam filters, Naive Bayes, and data wrangling Logistic regression Financial modeling Recommendation engines and causality Data visualization Social networks and data journalism Data engineering, MapReduce, Pregel, and Hadoop Doing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.
