HTML: Structured Data [repost]

HTML: Structured Data
Size: 531MB | Duration: 2h 57m | Video: AVC (.mp4) 1280×720 15fps | Audio: AAC 48KHz 2ch
Genre: eLearning | Level: Intermediate | Language: English

Structured data helps search engines, web crawlers, and browsers extract and process data from a webpage and use it to provide a richer browsing experience for users. Join senior author James Williamson for this course, as he explains structured data, its benefits, and the various syntaxes you can choose for markup, including microformats, RDFa, microdata, and JSON-LD. The course also includes four practical projects on structuring different types of data: contact data, event data, and product data, as well as the company data featured in a Google Knowledge Graph. By the end of this course, you’ll be able to create more structured, meaningful webpages and know where to find additional resources for learning more.

SAS Programming with Medicare Administrative Data

SAS Programming with Medicare Administrative Data by Matthew Gillingham
English | 2014 | ISBN: 1612903223 | 168 pages | EPUB | 11 MB

SAS Programming with Medicare Administrative Data is the most comprehensive resource available for using Medicare data with SAS. This book teaches you how to access Medicare data and, more importantly, how to apply this data to your research.

Knowing how to use Medicare data to answer common research and business questions is a critical skill for many SAS users. Due to its complexity, Medicare data requires specific programming knowledge in order to be applied accurately. Programmers need to understand the Medicare program in order to interpret and utilize its data.

With this book, you’ll learn the entire process of programming with Medicare data—from obtaining access to data; to measuring cost, utilization, and quality; to overcoming common challenges. Each chapter includes exercises that challenge you to apply concepts to real-world programming tasks.

SAS Programming with Medicare Administrative Data offers beginners a programming project template to follow from beginning to end. It also includes more complex questions and discussions that are appropriate for advanced users. Matthew Gillingham has created a book that is both a foundation for programmers new to Medicare data and a comprehensive reference for experienced programmers.

This book is part of the SAS Press program.

Case Studies in Data Mining with R [repost]

Case Studies in Data Mining with R
MP4 | Video: 1280×720 | 61 kbps | 44 KHz | Duration: 21 Hours | 7.14 GB
Genre: eLearning | Language: English

Learn to use the “Data Mining with R” (DMwR) package and R software to build and evaluate predictive data mining models.

Case Studies in Data Mining was originally taught as three separate online data mining courses. We examine three case studies which together present a broad-based tour of the basic and extended tasks of data mining in three different domains: (1) predicting algae blooms; (2) detecting fraudulent sales transactions; and (3) predicting stock market returns. The cumulative “hands-on” 3-course fifteen sessions showcase the use of Luis Torgo’s amazingly useful “Data Mining with R” (DMwR) package and R software. Everything that you see on-screen is included with the course: all of the R scripts; all of the data files and R objects used and/or referenced; as well as all of the R packages’ documentation. You can be new to R software and/or to data mining and be successful in completing the course. The first case study, Predicting Algae Blooms, provides instruction regarding the many useful, unique data mining functions contained in the R software ‘DMwR’ package. For the algae blooms prediction case, we specifically look at the tasks of data pre-processing, exploratory data analysis, and predictive model construction. For individuals completely new to R, the first two sessions of the algae blooms case (almost 4 hours of video and materials) provide an accelerated introduction to the use of R and RStudio and to basic techniques for inputting and outputting data and text. Detecting Fraudulent Transactions is the second extended data mining case study that showcases the DMwR (Data Mining with R) package.

O"Reilly Learning Paths - Data Visualization Video Training [Repost]

O’Reilly Learning Paths – Data Visualization Video Training
.MP4, AVC, 1000 kbps, 1280×720 | English, AAC, 128 kbps, 2 Ch | 14.7 hours | 3.75 GB
Instructors: Scott Murray, Rafael Hernandez, Michael Freeman, Jeff Heer

Successful data visualizations allow you to impart meaning and emphasis to your data points. This Learning Path will teach you how to display trends, patterns, and outliers while you discover the power of letting your data to speak. Once you’ve finished, you’ll be able to efficiently communicate volumes of data with ease.

An Introduction to d3.js: From Scattered to Scatterplot

This segment of your Learning Path will have you transforming data into visual images in no time, starting from scratch and building an interactive scatterplot by the end of the course. Learn to use d3.js, the web’s most powerful library for data visualization, to load data and translate values into SVG elements.

Learning to Visualize Data with D3.js

Add to your D3 skills as you build data visualizations with the D3 JavaScript library. You’ll start by learning how to bind data from JavaScript arrays to elements, scale data, and style simple data visualizations with CSS. From there, you’ll learn how to add interactivity to your data visualization to make it even more powerful.

Using Storytelling to Effectively Communicate Data

Learn how to use stories to introduce complex graphics in ways that entice and engage your audience. You’ll discover how storytelling can ensure clarity, humanize content, and allow you to present complex information using data visualization.

Effective Data Visualization

In this course, you’ll acquire best practices for designing interactive visualizations, performing exploratory data analysis, and examining multidimensional data. You’ll start with an exploration of design principles drawn from graphic design, visual art, perceptual psychology, and cognitive science. You’ll also learn techniques for scaling visualizations to extremely large data sets.

Intermediate D3.js

Expand your D3.js expertise as you learn how to work with charts, data layouts, and maps through several code examples. You’ll see how easily you can create beautiful, interactive, browser-based data visualizations with the D3 JavaScript library. Discover the power of D3.js for interactive maps, charts, and more.

See also:

O’Reilly Learning Paths – Data Science with R Video Training
O’Reilly Learning Paths – Beginning Java Video Training
O’Reilly Learning Paths – Beginning JavaScript Video Training
O’Reilly Learning Paths – CSS Fundamentals Video Training
O’Reilly Learning Paths – Beginning UX Design Video Training
O’Reilly Learning Paths – C# Video Training

Data Mining Techniques for the Life Sciences

Data Mining Techniques for the Life Sciences, Second Edition
Humana Press | Molecular Biology | May 28, 2016 | ISBN-10: 1493935704 | 552 pages | pdf | 18.25 mb

Editors: Carugo, Oliviero, Eisenhaber, Frank (Eds.)

Includes cutting-edge methods and protocols
Provides step-by-step detail essential for reproducible results
Contains key notes and implementation advice from the experts

This volume details several important databases and data mining tools. Data Mining Techniques for the Life Sciences, Second Edition guides readers through archives of macromolecular three-dimensional structures, databases of protein-protein interactions, thermodynamics information on protein and mutant stability, “Kbdock” protein domain structure database, PDB_REDO databank, erroneous sequences, substitution matrices, tools to align RNA sequences, interesting procedures for kinase family/subfamily classifications, new tools to predict protein crystallizability, metabolomics data, drug-target interaction predictions, and a recipe for protein-sequence-based function prediction and its implementation in the latest version of the ANNOTATOR software suite. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls.

Authoritative and cutting-edge, Data Mining Techniques for the Life Sciences, Second Edition aims to ensure successful results in the further study of this vital field.

Number of Illustrations and Tables

13 b/w illustrations, 84 illustrations in colour

Topics

Bioinformatics

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Adobe Illustrator Variable Data

Adobe Illustrator Variable Data
MP4 | Video: AVC 1280×720 | Audio: AAC 48KHz 2ch | Duration: 2 Hours | 529 MB
Genre: eLearning | Language: English

Have you ever changed data manually to make multiple copies of the same design? If so, you know it can be tedious and time consuming. Learn how to dynamically populate new information into a design and quickly generate multiple versions with Adobe Illustrator Variable Data.

Designer John Garrett appreciates the many different types of variables and their practical uses, including generating business cards and direct mailers. In this course, he explains how to use variable data including managing linked images, graphics, tables of data, and graphs. He covers the entire workflow, from setup to exporting dynamic batches.

Outside-In Marketing: Using Big Data to Guide your Content Marketing (IBM Press)

Outside-In Marketing: Using Big Data to Guide your Content Marketing (IBM Press) by James Mathewson
English | Apr. 23, 2016 | ISBN: 0133375560 | 208 Pages | AZW3/MOBI/EPUB/PDF (conv) | 6.1 MB

Supercharge ROI by Rebuilding Content Marketing Around Your Customer!

Marketing has always been about my brand, my product, my company. That’s “inside-out” marketing. Today, customers hate it—and ignore it. What does work? Customized messages they already care about. Marketing that respects their time and gives them immediate value in exchange for their attention. Marketing that’s “outside-in.”

Now, two renowned digital marketing thought leaders show how to integrate content marketing with Big Data to create high-ROI, outside-in marketing. James Mathewson and Mike Moran share new practices, techniques, guidelines, and metrics for engaging on your customers’ terms, using their words, reflecting their motivations. Whether you’re a content marketer, marketing executive, or analyst, you’ll learn how to:

• Ease your customers’ pain—solve what keeps them up at night—with compelling content experiences

• Build content that’s essential to clients and prospects in each step of their buyer journeys

• Integrate search and social data into all facets of content development to continually improve its effectiveness

• Build evergreen content that is continuously improved to better meet the needs of your clients and prospects

• Apply advanced machine learning, text analytics, and sentiment analysis to craft more discoverable, shareable content

• Shape your messages to intercept your clients’ and prospects’ information discovery in Google

• Transform culture and systems to excel at outside-in marketing

Transcribing Talk and Interaction: Issues in the representation of communication data (repost)

Transcribing Talk and Interaction: Issues in the representation of communication data by Christopher Joseph Jenks
English | ISBN: 9027211841 | 2011 | PDF | 131 pages | 1,5 MB

Interest in transcript-based research has grown significantly in recent years. Alongside this growth has been an increase in awareness of the empirical utility of naturalistic research on language use in interaction. However, a quick scan of the literature reveals that very few transcription books have been published in the past three decades. This is an astonishing fact given that there are perhaps hundreds of books published on spoken discourse analysis. This book aims to narrow this gap by providing an introduction to the theories and practices related to transcribing communication data. The book is intended for students with little to no knowledge of transcription work and/or instructors responsible for teaching introductory courses on transcript-based research. Readers who are learning or teaching discourse/conversation analysis or similar analytic methods of investigation will find this book particularly helpful.