Between the record-setting Michael Phelps swimming off into retirement, match-fixing in badminton, and Serena Williams’ post-match crip walk, the 2012 games have given us plenty of Olympic moments. However, unlike games past, this year’s Olympics haven’t just been watched on television — they’ve been streamed live on computers, tablets, and smartphones. [...]

Learn how to integrate full-stack open source big data architecture and to choose the correct technology-Scala/Spark, Mesos, Akka, Cassandra, and Kafka-in every layer.  Big data architecture is becoming a requirement for many different enterprises. So far, however, the focus has largely been on collecting, aggregating, and crunching large data sets in a…

Enterprises have learnt to harvest Big Data to earn higher profits, offer better services and gain a deeper understanding of their target clientèle.

Establish a strong foundation in accounting fundamentals that will help you succeed in your career and business with Warren/Jones/Tayler?s FINANCIAL AND MANAGERIAL ACCOUNTING, 16E. The latest business illustrations and current examples provide a meaningful context that demonstrates how each chapter?s content fits into the "big picture." The authors clearly connect fundamental accounting concepts to real businesses today. Each chapter begins with a real company opener that is referenced throughout the chapter. New examples and cases in each chapter highlight how to use and apply data analytics. These cases use data sets related to the chapter content while showing you how to analyze and develop reports using Excel and Tableau. In addition, updated Certified Management Accountant (CMA) exam questions prepare you for professional success. Pathways Challenges also help you hone critical-thinking skills. CNOWv2 online resources are also available to reinforce understanding.

Querybook’s core focus is to make composing queries, creating analyses, and collaborating with others as simple as possible.

Master Introduction to NoSQL Databases - Dive deep with our expert instructors and comprehensive curriculum.

Stay updated with the latest machine learning projects in 2024. Our website showcases the top 25 projects, providing insights into the future of AI.

This, the 24th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains extended and revised versions of seven papers presented at the 25th International Conference on Database and Expert Systems Applications, DEXA 2014, held in Munich, Germany, in September 2014. Following the conference, and two further rounds of reviewing and selection, six extended papers and one invited keynote paper were chosen for inclusion in this special issue. Topics covered include systems modeling, similarity search, bioinformatics, data pricing, k-nearest neighbor querying, database replication, and data anonymization.

Here are 5 useful things to know about Data Science, including its relationship to BI, Data Mining, Predictive Analytics, and Machine Learning; Data Scientist job prospects; where to learn Data Science; and which algorithms/methods are used by Data Scientists

Editor’s Note: Check out our 2017 State of Data Science Jobs Report to compare stats, sentiments, and POVs. *available in Spanish As many of you probably know, being a data scientist requires a large skill set . . . Credit: Swami Chandrasekaran To master all of that at a high...

Dans cet article, vous comprendrez mieux ce qu'est un doodle art, ses avantages pour notre bien être, mais aussi, vous serez surpris d'apprendre que le gribouillage

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About Machine Learning for Data Streams A hands-on approach to tasks and techniques in data stream mining and real-time analytics, with examples in MOA, a popular freely available open-source software framework. Today many information sources—including sensor networks, financial markets, social networks, and healthcare monitoring—are so-called data streams, arriving sequentially and at high speed. Analysis must take place in real time, with partial data and without the capacity to store the entire data set. This book presents algorithms and techniques used in data stream mining and real-time analytics. Taking a hands-on approach, the book demonstrates the techniques using MOA (Massive Online Analysis), a popular, freely available open-source software framework, allowing readers to try out the techniques after reading the explanations. The book first offers a brief introduction to the topic, covering big data mining, basic methodologies for mining data streams, and a simple example of MOA. More detailed discussions follow, with chapters on sketching techniques, change, classification, ensemble methods, regression, clustering, and frequent pattern mining. Most of these chapters include exercises, an MOA-based lab session, or both. Finally, the book discusses the MOA software, covering the MOA graphical user interface, the command line, use of its API, and the development of new methods within MOA. The book will be an essential reference for readers who want to use data stream mining as a tool, researchers in innovation or data stream mining, and programmers who want to create new algorithms for MOA.

Our leaders may be determined to make their daily dealings less transparent, but they probably didn't reckon on bored Steve Ballmer. The former Microsoft CEO...

Mimi Onuoha is an artist who works mostly with algorithms, data sets, and digital systems, but her best known work may be a file cabinet. White, metal, and unassuming, it’s the kind that used to line the carpeted halls of office buildings before the advent of Google Drive and iCloud. Sliding open On

Because electricity is not priced optimally, we all pay for excessive Bitcoin mining. (This post is coauthored with Catherine Wolfram) You may be reading this blog post in your dorm room, while yo…

Analyzing large volumes of data is only part of what makes big data analytics different from traditional data analytics

Data Science using Python Training - H2K Infosys with Certification from real time professionals with live examples and exercises.

Introduction to Apache Hadoop, an open source software framework for storage and large scale processing of data-sets on clusters of commodity hardware.

Geo-Distances are of great importance: Researchers from various disciplines refer to geographic distances – health researchers refer to geographic data when analyzing the spread of diseases, economists when evaluating the impact of transaction costs on human behavior, or sociologists when evaluating interpersonal distances (based on external factors) in human interaction. However, each query sent to The post Google Geo Data – Data Access Without Restrictions appeared first on ThinkToStart.

This product includes: 1. Editable Vector .AI file Compatibility: Adobe Illustrator CCIncludes Editable Text Font SuezOne (Under Free Open Font License) 2. Editable Vector .EPS-10 file Compatibility: Most Vector Editing Software 3. High-resolution JPG image 4000 x 4000 px License terms in short: Use for everything except reselling item itself. Read a full license here

Twitter and IBM announced a significant partnership today that will involve Twitter sharing its data with IBM for integration into IBM's enterprise

Today's social and behavioral researchers increasingly need to know: "What do I do with all this data?" This book provides the skills needed to analyze and report large, complex data sets using machine learning tools, and to understand published machine learning articles. Techniques are demonstrated using actual data (Big Five Inventory, early childhood learning, and more), with a focus on the interplay of statistical algorithm, data, and theory. The identification of heterogeneity, measurement error, regularization, and decision trees are also emphasized. The book covers basic principles as well as a range of methods for analyzing univariate and multivariate data (factor analysis, structural equation models, and mixed-effects models). Analysis of text and social network data is also addressed. End-of-chapter "Computational Time and Resources" sections include discussions of key R packages; the companion website provides R programming scripts and data for the book's examples.

Machine learning with Big Data is, in many ways, different than "regular" machine learning. This informative image is helpful in identifying the steps in machine learning with Big Data, and how they fit together into a process of their own.

Hadoop / Big Data Training Institute in Delhi. Enroll for Hadoop Training Courses and Big Data Certification Training Classes in Delhi NCR.

Norway's newest white-space data center has opened in a former mine. Lefdal Mine Datacenter could become the world's largest once of three of its five levels are filled.

This article on a complete tutorial to learn Data Science with Pyhon from scratch, was posted by Kunal Jain. Kunal is a post graduate from IIT Bombay in Aerospace Engineering. He has spent more than 8 years in field of Data Science. He learned basics of Python within a week. And, since then, he has not only… Read More »A Complete Tutorial to Learn Data Science with Python from Scratch

Here are 33 free to use public data sources anyone can use for their big data and AI projects.

It's not just the NSA anymore. Here's how local law enforcement collects your call data, even if unrelated to crime

This is an overview of structuring, cleaning, and enriching raw data.

Designed for social scientists working with big data sets, this book maps out the cycle of research, from epistemology and ethical questions to data collection and analysis. It introduces a unique mixed methods approach by integrating qualitative and quantitative methods. This book is also available as Open Access on Cambridge Core.

In one fell swoop, every utility's claim of "smart meters do not spy on you" is now dissolved. Their myth is now shattered.

Handwriting signatures captured electronic signing pads are getting wider popularity. The unauthorized use of a signature, such as copying it into an unauthorized payment, is becoming a big concern. Therefore, this project presents data hiding technique as an alternative to the cryptographic authentication approach. In this project, proposed signature in signature hiding model is presented. Hides secret signature data inside binary cover signature image, depend on bits manipulation. The performance of this proposed model has been successfully tested by computer simulation and the results are presented both quantitavely and qualitatively.

Use Java to create a diverse range of Data Science applications and bring Data Science into production About This Book * An overview of modern Data Science and Machine Learning libraries available in Java * Coverage of a broad set of topics, going from the basics of Machine Learning to Deep Learning and Big Data frameworks. * Easy-to-follow illustrations and the running example of building a search engine. Who This Book Is For This book is intended for software engineers who are comfortable with developing Java applications and are familiar with the basic concepts of data science. Additionally, it will also be useful for data scientists who do not yet know Java but want or need to learn it. If you are willing to build efficient data science applications and bring them in the enterprise environment without changing the existing stack, this book is for you! What You Will Learn * Get a solid understanding of the data processing toolbox available in Java * Explore the data science ecosystem available in Java * Find out how to approach different machine learning problems with Java * Process unstructured information such as natural language text or images * Create your own search engine * Get state-of-the-art performance with XGBoost * Learn how to build deep neural networks with DeepLearning4j * Build applications that scale and process large amounts of data * Deploy data science models to production and evaluate their performance In Detail Java is the most popular programming language, according to the TIOBE index, and it is a typical choice for running production systems in many companies, both in the startup world and among large enterprises. Not surprisingly, it is also a common choice for creating data science applications: it is fast and has a great set of data processing tools, both built-in and external. What is more, choosing Java for data science allows you to easily integrate solutions with existing software, and bring data science into production with less effort. This book will teach you how to create data science applications with Java. First, we will revise the most important things when starting a data science application, and then brush up the basics of Java and machine learning before diving into more advanced topics. We start by going over the existing libraries for data processing and libraries with machine learning algorithms. After that, we cover topics such as classification and regression, dimensionality reduction and clustering, information retrieval and natural language processing, and deep learning and big data. Finally, we finish the book by talking about the ways to deploy the model and evaluate it in production settings. Style and approach This is a practical guide where all the important concepts such as classification, regression, and dimensionality reduction are explained with the help of examples.

Pentaho Data Integration (PDI) is known to be a portion of the Pentaho Open Source Occupational intelligence set. It comprises of software for each and every one features that support the final…