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Что такое Big Data и почему это страшно интересно - Андрей Себрант (01.02.2014)
 
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[Slides] (http://www.slideshare.net/yandex/big-data-30799013?ref=http://habrahabr.ru/company/yandex/blog/214217/) [Ha Habre](http://habrahabr.ru/company/yandex/blog/214217/)
Views: 44230 Arthur Vard
Apache Spark for Big Data Processing
 
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Recorded at SpringOne2GX 2015 Presenters: Ludwine Probst & Ilayaperumal Gopinathan Big Data Track Slides: http://www.slideshare.net/SpringCentral/apache-spark-for-big-data-processing Today, we live in the world of Big Data. Hadoop and MapReduce are highly dominant in the domain of large scale data processing. However, the MapReduce model shows its limits for various types of treatment, especially for highly iterative algorithms frequently encountered in the field of Machine Learning. Spark is an in-memory data processing framework that, unlike Hadoop, provides interactive and real-time analysis on large datasets. Furthermore, Spark has a more flexible programming model and gives better performance than Hadoop. In this talk, we aim at giving a portrait of Spark and at browsing its ecosystem, in particular Spark Streaming and MLlib with a concrete example. We will also show how you can use Spark with Spring XD, allowing you to take advantage of the strengths in each platform.
Views: 44362 SpringDeveloper
Big Data and Cyber Security, David Stubley (7Elements)
 
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The slides are here: http://www.slideshare.net/WilliamBuchanan1/big-data-and-cyber-security
Views: 300 The Cyber Academy
Turning big data into big insight: New algorithms for big data analytics
 
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Data scientists and professional analysts spend much of their time focusing on how to use the massive amounts of data at their fingertips to enhance decision making. The latest release of IBM SPSS Modeler includes algorithms specifically designed to handle more types and sources of data than ever before, helping users uncover insights quickly while using them to differentiate and grow their business. Learn how to do the following: • Identify the big data algorithms included in SPSS Modeler. • Build predictive models using SPSS Modeler’s big data algorithms. • Deploy predictive models to decision makers. • Explore predictive extensions as part of the SPSS predictive analytics community. Discover how SPSS Modeler can help your organization solve its big data challenges. Learn more about IBM SPSS: http://ibm.co/spsstrial Subscribe to the IBM Analytics Channel: https://www.youtube.com/subscription_center?add_user=ibmbigdata The world is becoming smarter every day, join the conversation on the IBM Big Data & Analytics Hub: http://www.ibmbigdatahub.com https://www.youtube.com/user/ibmbigdata https://www.facebook.com/IBManalytics https://www.twitter.com/IBMbigdata https://www.linkedin.com/company/ibm-big-data-&-analytics https://www.slideshare.net/IBMBDA
Views: 4615 IBM Analytics
Introduction to Big Data and Hadoop in Hindi
 
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Introduction to Big Data and hadoop in Hindi Introduction to Big Data And Hadoop Introduction to big data BIg Data Sources Use Cases of Big Data Introduction to Hadoop Hadoop Components Hadoop Daemons Hadoop scale out storage Hadoop Cluster Link To English Video coming soon Link to PPT https://www.slideshare.net/SandeepPatil194/introduction-to-big-data-and-hadoop-81450933 FB page :- https://www.facebook.com/bitwsandeep/
Tom Kraljevic - Big Data Environments
 
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Tom Kraljevic discusses big data environments with H2O on Hadoop, AWS, Apache Spark, and more. Don’t just consume, contribute your code and join the movement: https://github.com/h2oai User conference slides on open source machine learning software from H2O.ai at: http://www.slideshare.net/0xdata
Views: 816 H2O.ai
Data Science - Part XI - Text Analytics
 
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For downloadable versions of these lectures, please go to the following link: http://www.slideshare.net/DerekKane/presentations https://github.com/DerekKane/YouTube-Tutorials This is an introduction to text analytics for advanced business users and IT professionals with limited programming expertise. The presentation will go through different areas of text analytics as well as provide some real work examples that help to make the subject matter a little more relatable. We will cover topics like search engine building, categorization (supervised and unsupervised), clustering, NLP, and social media analysis.
Views: 16739 Derek Kane
AR and Big Data: Interoperable Data Repositories for Collaborative Work Environments (CWEs)
 
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SlideShare: http://www.slideshare.net/AugmentedWorldExpo/ar-and-big-data-interoperable-data-repositories-for-collaborative-work-environments-cwes Jim Novack (Talent Swarm) Anand Gupta (Bigdatastrategy) Collaborative Work Environments (CWE) combined with Telepresence and Mixed Reality technologies offer new ways to improve the outcomes of engineering and building large construction, petrochemical, industrial, aeronautical and defense industry projects. This presentation will describe how in the near future, design, implementation and control processes in these projects will be performed more safely and accurately at lower cost by proposing a framework of existing, open and already adopted standards. Augmented World Expo (AWE) is back for its seventh year in our largest conference and expo featuring technologies giving us superpowers: augmented reality (AR), virtual reality (VR) and wearable tech. Join over 4,000 attendees from all over the world including a mix of CEOs, CTOs, designers, developers, creative agencies, futurists, analysts, investors, and top press in a fantastic opportunity to learn, inspire, partner, and experience first hand the most exciting industry of our times. See more at http://AugmentedWorldExpo.com
Educate 2017: Mining for Gold: Using advanced analytics to get more value from your data
 
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Slide deck available at: https://www.slideshare.net/learnosity/educate-2017-mining-for-gold-using-advanced-analytics-to-get-more-value-from-your-data Data mining is a multi-industry trend that looks certain to grow in both scope and application. It offers a hugely powerful means of identifying aggregates and trends at all levels, which is why we’ve worked extensively on deepening our analytics APIs over the last year. This session with Michael Sharman and Denis Hoctor will walk you through the latest advancements to our analytics and show you how we can make your data more valuable and how we handle the heavy lifting to make it easier for you to create complex reports at individual, class, school, or district level.
Views: 95 Learnosity
"Blockchain & Big Data", Trent MConaghy, Founder & CTO at ascribe GmbH
 
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"Blockchain & Big Data", Trent MConaghy, Founder & CTO at ascribe GmbH Watch more from Data Natives 2015 here: http://bit.ly/1OVkK2J Visit the conference website to learn more: www.datanatives.io Follow Data Natives: https://www.facebook.com/DataNatives https://twitter.com/DataNativesConf Stay Connected to Data Natives by Email: Subscribe to our newsletter to get the news first about Data Natives 2016: http://bit.ly/1WMJAqS Presentation Slides: https://www.slideshare.net/secret/8gK4MtBHS8AJyU About the author: Trent McConaghy is co-founder & CTO of ascribe, which uses blockchain technology and internet-scale machine learning to secure digital creations. Before that, he co-founded Solido Design Automation, which uses large-scale machine learning to help drive Moore's Law. Solido is now widely used in developing next-gen computer chips. Before that, he co-founded ADA, which used machine learning for analog synthesis. ADA was acquired in 2004. Trent has written two critically-acclaimed books on machine learning, creativity and circuit design, in addition to 50 papers+patents. He has given keynotes & invited talks at MIT, Columbia, Berkeley, JPL, Samsung, Qualcomm, Nvidia, Data Science Day, PyData, and more.
Views: 2124 Data Natives
JOSA TechTalks - Real Time and Big Data
 
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Although Hadoop might be the first thing to come in your mind when you think of processing large data sets, it is not always the best solution for your Big Data problems. Hadoop might be the right choice for batch-processing big data, but when it comes to real-time data processing there are other architectures and tools to consider. This TechTalk shows the need behind solving real time data problems and explains the Lambda architecture, covering Druid as an example, and the simpler and less expensive "relay model". By Mahmoud Jalajel - Head of Data Science Lab at Blue Kangaroo Presentation on: http://www.slideshare.net/jordanopensource/jalajel-tech-talk
Big Data and predictive analysis: use case in the hotel industry
 
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In order to improve its offer in a business strongy challenged by new players who offer new hosting modal. A hotel company intends to implement a Big Data solution that can predict hotel occupancy so that rates can be optimized according to demand. Discover how this hotel company has implemented a predictive analysis tool with no previous experience in Big Data thanks to Public Cloud and Orange Business Services experts. More about Orange Business Services: Official website: http://www.orange-business.com/en Facebook: https://www.facebook.com/orangebusiness/ Twitter: https://twitter.com/orangebusiness Linkedin: https://www.linkedin.com/company/oran... Slideshare: http://www.slideshare.net/orangebusiness Pinterest: https://fr.pinterest.com/orangebusiness
BigData and Old Data: Embedding Predictive Analytics in Real Applications
 
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Usama Fayyad, Chief Data Officer at Barclays Bank presents at RapidMiner World 2014 on the challenges of making the benefits of advanced analytics fit with the business or target area of application. Topics discussed include embedding data mining insights and models into production processes and live deployments, real-time data streaming and in situ data mining, BigData, unstructured data, and Hadoop. Access Usama's slides here: http://www.slideshare.net/RapidMiner/big-data-vs-classic-data-usama-fayyad
Views: 1439 RapidMiner, Inc.
Big Data Modeling at Semantic Tech / NoSQL Now 2014
 
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Slides available here: http://www.slideshare.net/zenhack/vital-ai-big-data-modeling Technologies such as Hadoop have addressed the "Volume" problem of Big Data, and technologies such as Spark have recently addressed the "Velocity" problem – but the "Variety" problem is largely unaddressed – there is a lot of manual "data wrangling" to mange data models. These manual processes do not scale well. Not only is the variety of data increasing, also the rate of change in the data definitions is increasing. We can’t keep up. NoSQL data repositories can handle storage, but we need effective models of the data to fully utilize it. This talk will present tools and a methodology to manage Big Data Models in a rapidly changing world. This talk covers: Creating Semantic Metadata Models of Big Data Resources Graphical UI Tools for Big Data Models Tools to synchronize Big Data Models and Application Code Using NoSQL Databases, such as Amazon DynamoDB, with Big Data Models Using Big Data Models with Hadoop, Storm, Spark, Giraph, and Inference Using Big Data Models with Machine Learning to generate Predictive Models Developer Collaborative/Coordination processes using Big Data Models and Git Managing change – Big Data Models with rapidly changing Data Resources
Views: 1017 vital-ai
The Big 6 Steps Of Big Data Explained [Audio]
 
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The world population as on October 2017 was 7.6 Billion people. Which directly points to the fact that this is Big Data! All the insights you receive from running digital campaigns is your big data! What is Big Data? It is voluminous information or relevant statistics acquired by companies, firms and large organizations. Often this big data is difficult to compute manually. Read our blog to gain more insights on Big Data https://goo.gl/FZpXpB The Big 6 Steps 1. Data Mining 2. Data Collection 3. Data Storing 4. Data Cleaning 5. Data Analysis 6. Data Consumption With all your marketing efforts for your business in place, it's a good idea to have an all - in- one payment solution in place as well. SignUp on PayUmoney now to enjoy the best payment gateway experience and grow your business effortlessly. Know all the features and benefits of PayUmoney by watching this video https://goo.gl/i2wjT4 For all the latest updates, reach out to us at: Blog - https://goo.gl/57f9ea Facebook - https://www.facebook.com/PayUmoney/ Twitter - https://twitter.com/PayUmoney Slideshare - https://www.slideshare.net/PayUmoney_India
Views: 20 PayUmoney
Data Science - Part III -  EDA & Model Selection
 
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For downloadable versions of these lectures, please go to the following link: http://www.slideshare.net/DerekKane/presentations https://github.com/DerekKane/YouTube-Tutorials This lecture introduces the concept of EDA, understanding, and working with data for machine learning and predictive analysis. The lecture is designed for anyone who wants to understand how to work with data and does not get into the mathematics. We will discuss how to utilize summary statistics, diagnostic plots, data transformations, variable selection techniques including principal component analysis, and finally get into the concept of model selection.
Views: 35589 Derek Kane
Big Data in Cyber Security, Simon Arnell (HPE)
 
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Slides: http://www.slideshare.net/WilliamBuchanan1/big-data-in-cyber-security
Views: 203 The Cyber Academy
A Tool For Big Data Analysis using Apache Spark
 
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A Tool For Big Data Analysis using Apache Spark. Presented at Bangalore Apache Spark Meetup by Ganesha Yadiyala on 10/01/2016. http://www.meetup.com/Bangalore-Apache-Spark-Meetup/events/227472649/ For slides of this talk, refer http://www.slideshare.net/datamantra/a-tool-for-big-data-analysis-using-apache-spark Connect with Ganesha Yadiyala at http://www.datamantra.io https://www.linkedin.com/in/ganeshayadiyala https://twitter.com/ganeshayadiyala
Views: 509 datamantra
A Look Under Progressive's Big Data Hood - Pawan Divakarla & Brian Durkin
 
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Pawan Divakarla, Data and Analytics Business Leader at Progressive Casualty Insurance Company Brian Durkin, Innovation Enablement Services at Progressive H2O World 2015, Day 3 Contribute to H2O open source machine learning software https://github.com/h2oai Check out more slides on open source machine learning software at: http://www.slideshare.net/0xdata
Views: 1668 H2O.ai
Big Data Governance with Neo4j — Nicolas Rouyer, Orange
 
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Slides for this talk: https://www.slideshare.net/neo4j/graphconnect-europe-2017-big-data-governance-with-neo4j-orange In this talk, Nicolas will show how to pilot big data governance with Neo4j by covering how to: manage fine-grained rights on data, define complex authorization workflows, establish links between data sources, schedule data transfers and computes, apply certified algorithms over crossed data sources, and track and audit the whole data flow. Nicolas Rouyer, Big Data Architect, Orange
Views: 384 Neo4j
Практическое применение data mining технологий / Александр Гринчук /  ИБМТ БГУ
 
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Практическое применение data mining технологий / Александр Гринчук / ИБМТ БГУ Презентация: http://www.slideshare.net/WG_Talks/data-mining-40811073 Александр рассказал о рынке бизнес-аналитики в Беларуси и на примере реальных бизнес-задач показал проблемы, с которыми сталкиваются специалисты при внедрении Data Mining. DataTalks - неформальные встречи бизнес-аналитиков и специалистов в области анализа данных. Присоединяйтесь к нашей группе на LinkedIn: https://www.linkedin.com/groups?gid=6788018
Views: 2340 Wargaming CIS
Big Data algorithms and data structures for largescale graphs
 
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Зиновьев Алексей Тамтэк www.dump-it.ru Исходная презентация: http://www.slideshare.net/it-people/big-data-algorithms-and-data-structures-for-large-scale-graphs-32925091
Become a data-driven Organization with Machine Learning
 
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Recorded at SpringOne2GX 2014. Speaker: Peter Harrington Big Data Track Slides: http://www.slideshare.net/SpringCentral/spring-one2gx-2014peterharrington Does your organization collect data? Lots of data? Does your organization make use of all that data they have collected? In this session you will learn what you do with machine learning, and what are the building blocks for an application that uses machine learning. This session will show you how to go from data you have collected to creating predictions for customers. You will learn how valuable insights into your data can be gleaned while building the code to make predictions.
Views: 869 SpringDeveloper
From data to AI with the Machine Learning Canvas by Louis Dorard
 
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https://www.bigdataspain.org Abstract: https://www.bigdataspain.org/program/fri-from-data-to-ai-with-the-machine-learning-canvas.html Slides: https://www.slideshare.net/secret/ETf7l0mccVWV8y Session presented at Big Data Spain 2016 Conference 17th Nov 2016 Kinépolis Madrid Event promoted by: http://www.paradigmadigital.com
Views: 513 Big Data Spain
Java in production for Data Mining Research projects (JET'15, Minsk)
 
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Alexey Zinoviev presented this paper on the JET conference Slides: http://www.slideshare.net/zaleslaw/javadaykiev15-java-in-production-for-data-mining-research-projects This paper covers next topics: Data Mining, Machine Learning, Hadoop, Spark, MLlib
Views: 168 Alexey Zinoviev
Building Scalable, Flexible Data Pipelines for Big Data, Vivek Ganesan 20140224
 
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Speaker: Vivek Ganesan Data Science meeting (Formerly Data Mining) This presentation will be an overview of ETL (Extract, Transform, and Load) tasks and tools in Hadoop and will cover the pros/cons of different approaches. Speaker Bio Vivek has worked on big data and cloud deployments at large companies such as Intuit and Paypal, and also in startups. Currently he provides expert consulting services to Fortune 500 clients on Big Data projects. Slides: http://www.slideshare.net/vivekganesan/big-data-pipelines http://www.meetup.com/SF-Bay-ACM/events/160985942/
Views: 3496 San Francisco Bay ACM
Java in production for Data Mining Research projects (JavaDayKiev'15)
 
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Alexey Zinoviev presented this paper on the JavaDayKiev'15 conference Slides: http://www.slideshare.net/zaleslaw/javadaykiev15-java-in-production-for-data-mining-research-projects This paper covers next topics: Data Mining, Machine Learning, Hadoop, Spark, MLlib
Views: 313 Alexey Zinoviev
[Webinar] Data Flow: Place digital analytics at the heart of your big data projects
 
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In this webinar, discover Data Flow, a new tool for extracting millions of events in just minutes, making it possible to exploit your digital analytics data for data mining and machine learning activities. Want to know more about AT Internet? Visit http://www.atinternet.com/en Be sure to check out our blog: http://blog.atinternet.com/en Follow us to stay updated on the latest trends in digital analytics: Facebook : https://www.facebook.com/atinternet.analytics/ Twitter : https://twitter.com/AT_Internet LinkedIn : https://www.linkedin.com/company/at-internet SlideShare : http://fr.slideshare.net/AT-Internet Xing : https://www.xing.com/companies/atinternetgmbh Google + : https://plus.google.com/+ATinternet
Views: 111 AT Internet
KNIME Italy Meetup - Going Big Data on Apache Spark
 
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Il talk che ho tenuto al KNIME Meetup di Milano ("KNIME Italy Meetup goes Big Data on Apache Spark"). Potete trovare le slide qui: http://www.slideshare.net/AndreBessi/knime-italy-meetup-going-big-data-on-apache-spark Apache Spark è un engine per l'elaborazione di dati su larga scala. Esso consente di costruire e testare modelli predittivi in ​​poco tempo, e contiene moduli per: SQL, Streaming, Machine Learning, Graph Processing. KNIME (Konstanz Information Miner) è una piattaforma open source di analisi dati e reporting che integra vari componenti per il machine learning e data mining. La sua interfaccia grafica consente la pre-elaborazione dei dati, la modellazione, l'analisi dei dati e la visualizzazione. KNIME Spark Executor è un insieme di nodi utilizzati per creare ed eseguire applicazioni su Apache Spark con la familiare piattaforma KNIME Analytics. In questo talk, approfondiremo l'architettura del KNIME Spark Executor, capiremo come KNIME interagisce con Spark, e vedremo i nuovi nodi sviluppati da Databiz.
Views: 336 Andrea Bessi
#BDAM: Kite SDK: Helping Hadoop projects work together - 06/23 Big Data Application Meetup, Talk #2
 
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Speaker: Ryan Blue from Cloudera Big Data Applications Meetup, 06/23/2015 Palo Alto, CA More info here: http://www.meetup.com/BigDataApps/ Link to slides: http://www.slideshare.net/_blue/big-data-applications-meetup-cask About this talk: Big data applications on Hadoop commonly require several projects from the ecosystem working together on the same data. Interoperability between those projects remains a big challenge for developers because those projects all interact with datasets differently. When Spark writes files with an OutputFormat, how does Impala know how to read it? In this talk, Ryan Blue from Cloudera willl introduce Kite, a data-focused API for Hadoop, and talk about how we are using it to address the interoperability problem.
Views: 605 Cask
Bart Baddeley - Measuring Similarity & Clustering Data
 
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http://www.slideshare.net/PyData/measuring-similarity-and-clustering-data-bart-baddeley Clustering data is a fundamental technique in data mining and machine learning. The basic problem can be specified as follows: "Given a set of data, partition the data into a set of groups so that each member of a given group is as similar as possible to the other members of that group and as dissimilar as possible to members of other groups". In this talk I will try to unpack some of the complexities inherent in this seemingly straightforward description. Specifically, I will discuss some of the issues involved in measuring similarity and try to provide some intuitions into the decisions that need to be made when using such metrics to cluster data.
Views: 955 PyData
Stream Processing as Game Changer for Big Data and IoT by Kai Wähner
 
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https://www.bigdataspain.org Abstract: https://www.bigdataspain.org/program/thu-stream-processing-game-changer-big-data-internet-things.html Slides: https://www.slideshare.net/secret/pZmmNP72lLoXgC Session presented at Big Data Spain 2016 Conference 17th Nov 2016 Kinépolis Madrid Event promoted by: http://www.paradigmadigital.com
Views: 317 Big Data Spain
Data Mining, Лекция №1
 
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Техносфера Mail.ru Group, МГУ им. М.В. Ломоносова. Курс "Алгоритмы интеллектуальной обработки больших объемов данных", Лекция №1 - "Задачи Data Mining" Лектор - Николай Анохин Обзор задач Data Mining. Стандартизация подхода к решению задач Data Mining. Процесс CRISP-DM. Виды данных. Кластеризация, классификация, регрессия. Понятие модели и алгоритма обучения. Слайды лекции: http://www.slideshare.net/Technosphere1/lecture-1-47107550 Другие лекции курса Data Mining | https://www.youtube.com/playlist?list=PLrCZzMib1e9pyyrqknouMZbIPf4l3CwUP Официальный сайт Технопарка | https://tech-mail.ru/ Официальный сайт Техносферы | https://sfera-mail.ru/ Технопарк в ВКонтакте | http://vk.com/tpmailru Техносфера в ВКонтакте | https://vk.com/tsmailru Блог на Хабре | http://habrahabr.ru/company/mailru/ #ТЕХНОПАРК #ТЕХНОСФЕРА x
Mike Pittaro - High Performance Hardware for Data Analysis
 
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View slides for presentation here: http://www.slideshare.net/PyData/mike-pittaro-high-performance-hardware-for-data-analysis PyData NYC 2014 Choosing hardware for big data analysis is difficult because of the many options and variables involved. The problem is more complicated when you need a full cluster for big data analytics. This session will cover the basic guidelines and architectural choices involved in choosing analytics hardware for Spark and Hadoop. I will cover processor core and memory ratios, disk subsystems, and network architecture. This is a practical advice oriented session, and will focus on performance and cost tradeoffs for many different options.
Views: 355 PyData
Data mining на практике. Подводные камни анализа данных / Ксения Петрова / COO dmlabs.org
 
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Ксения, с точки зрения своего опыта, рассказала про главные грабли, на которые может наступить молодой аналитик. Data mining на практике. Подводные камни анализа данных / Ксения Петрова / COO dmlabs.org Презентация: http://www.slideshare.net/WG_Talks/data-mining-dmlabsorg DataTalks - неформальные встречи бизнес-аналитиков и специалистов в области анализа данных. Присоединяйтесь к нашей группе на LinkedIn: https://www.linkedin.com/groups?gid=6788018
Views: 7572 Wargaming CIS
#HOTR TARGET, ADAPT, SELL, FORECAST, IT'S TIME TO MAKE BIG DATA TALK - HENRI VIERDIER
 
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Slides: http://www.slideshare.net/secret/A6VmvuUMYs90LL HENRI VERDIER - Chief Data Officer of the French Government & Director @ Etalab Henri Verdier is a French entrepreneur, and currently the Head of Etalab, the French Agency for Public Open data. With Etalab, he launched the first government open data portal open to Citizen's contributions. Henri Verdier was CEO of MFG Labs, an internet startup involved in social data mining, and Chairman of the Board of Cap Digital, the French European Cluster for Digital Content and Services located in Paris Region. Entreprendre n’est pas inné et 80% des erreurs peuvent être évitées. Ne perds pas de temps, offre-toi Koudetat : http://bit.ly/koudetat-youtube
Views: 328 Startupfood
Data Preparation vs. Data Wrangling Comparison in Machine Learning / Deep Learning
 
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Data Preparation: Comparison of Programming Languages, Frameworks and Tools for Data Preprocessing and (Inline) Data Wrangling in Machine Learning / Deep Learning Projects. A key task to create appropriate analytic models in machine learning or deep learning is the integration and preparation of data sets from various sources like files, databases, big data storages, sensors or social networks. This step can take up to 80% of the whole project. This session compares different alternative techniques to prepare data, including extract-transform-load (ETL) batch processing (like Talend, Pentaho), streaming analytics ingestion (like Apache Storm, Flink, Apex, TIBCO StreamBase, IBM Streams, Software AG Apama), and data wrangling (DataWrangler, Trifacta) within visual analytics. Various options and their trade-offs are shown in live demos using different advanced analytics technologies and open source frameworks such as R, Python, Apache Hadoop, Spark, KNIME or RapidMiner. The session also discusses how this is related to visual analytics tools (like TIBCO Spotfire), and best practices for how the data scientist and business user should work together to build good analytic models. Key takeaways for the audience: - Learn various options for preparing data sets to build analytic models - Understand the pros and cons and the targeted persona for each option - See different technologies and open source frameworks for data preparation - Understand the relation to visual analytics and streaming analytics, and how these concepts are actually leveraged to build the analytic model after data preparation Slide Deck: http://www.slideshare.net/KaiWaehner/data-preparation-vs-inline-data-wrangling-in-data-science-and-machine-learning
Views: 2369 Kai Wähner
Data Mining, Лекция №5
 
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Техносфера Mail.ru Group, МГУ им. М.В. Ломоносова. Курс "Алгоритмы интеллектуальной обработки больших объемов данных", Лекция №5 "Обработка текстов, Naive Bayes" Лектор - Николай Анохин Условная вероятность и теорема Байеса. Нормальное распределение. Naive Bayes: multinomial, binomial, gaussian. Сглаживание. Генеративная модель NB и байесовский вывод. Графические модели. Слайды лекции http://www.slideshare.net/Technosphere1/lecture-5-47107556 Другие лекции курса Data Mining | https://www.youtube.com/playlist?list=PLrCZzMib1e9pyyrqknouMZbIPf4l3CwUP Наш видеоканал | http://www.youtube.com/user/TPMGTU?sub_confirmation=1 Официальный сайт Технопарка | https://tech-mail.ru/ Официальный сайт Техносферы | https://sfera-mail.ru/ Технопарк в ВКонтакте | http://vk.com/tpmailru Техносфера в ВКонтакте | https://vk.com/tsmailru Блог на Хабре | http://habrahabr.ru/company/mailru/ #ТЕХНОПАРК #ТЕХНОСФЕРА x
Big Data Presentation SER322 (High Res)
 
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Proper High resolution version. References Marr, B. (2014a). Big data: The 5 vs everyone must know. Linkedin. https://www.linkedin.com/pulse/20140306073407-64875646-big-data-the-5-vs-everyone-must-know Marr, B. (2014b). What is big data? Linkedin. http://www.slideshare.net/BernardMarr/140228-big-data-slide-share Learning Tree International. (2014). What is big data and Hadoop? YouTube. https://www.youtube.com/watch?v=FHVuRxJpiwI ExplainingComputers, (2012). Explaining big data. YouTube. https://www.youtube.com/watch?v=7D1CQ_LOizA Global internet traffic to surpass one zettabyte in 2016. (2016). University of Florida. https://news.it.ufl.edu/general-news/global-internet-traffic-to-surpass-one-zettabyte-in-2016/ Rouse, M. (2000-2016). Definition exabyte (EB). TechTarget. http://searchstorage.techtarget.com/definition/exabyte Watson (computer). (2016). Wikimedia Foundation, Inc. https://en.wikipedia.org/wiki/Watson_(computer) Engadget. (2011). IBM's Watson supercomputer destroys humans in Jeopardy | Engadget. YouTube. https://www.youtube.com/watch?v=WFR3lOm_xhE knowlengr. (2013-2015). Another v: Making the case for big data veracity. Krypton Brothers LLC. http://kryptonbrothers.com/news/big-data-veracity/ Hill, K. (2012). How Target figured out a teen girl was pregnant before her father did. Forbes.com LLC. http://www.forbes.com/sites/kashmirhill/2012/02/16/how-target-figured-out-a-teen-girl-was-pregnant-before-her-father-did/2/#64ccb45171cc Smith, C. (2016). By the number; 200+ amazing Facebook statistics (January 2016). DMR. http://expandedramblings.com/index.php/by-the-numbers-17-amazing-facebook-stats/ Jesse Anderson. (2013). Learn MapReduce with playing cards. YouTube. https://www.youtube.com/watch?v=bcjSe0xCHbE
Views: 31 Tyler Cole
Data Science - Part VIII -  Artifical Neural Network
 
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For downloadable versions of these lectures, please go to the following link: http://www.slideshare.net/DerekKane/presentations https://github.com/DerekKane/YouTube-Tutorials This lecture provides an overview of biological based learning in the brain and how to simulate this approach through the use of feed-forward artificial neural networks with back propagation. We will go through some methods of calibration and diagnostics and then apply the technique on three different data mining tasks: binary prediction, classification, and time series prediction.
Views: 12381 Derek Kane
[LIVE] Kamanja: A New Open Source Real-Time System for Scoring Data Mining Models, Greg Makowski,
 
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[Streamed version. Front & back trimmed. Slide issue in beginning.] An edited version is available: https://www.youtube.com/watch?v=ANqB72b0r38 Slides: http://www.slideshare.net/gregmakowski/kamanja-driving-business-value-through-realtime-decisioning-solutions Greg Makowski, Director of Data Science, LigaDATA This talk will start with a number of complex data real-time use cases, such as a) complex event processing, b) supporting the modeling of a data mining department and c) developing enterprise applications on Apache big-data systems. While Hadoop and big data has been around for a while, banks and healthcare companies tend not to be early IT adopters. What are some of the security or roadblocks in Apache big data systems for such industries with high requirements? Data mining models can be trained in dozens of packages, but what can simplify the deployment of models regardless of where they were trained or with what algorithm? Predictive Modeling Markup Language (PMML), is a type of XML with specific support for 15 families of data mining algorithms. Data mining software such as R, KNIME, Knowledge Studio, SAS Enterprise Miner are PMML producers. The new open-source product, Kamanja, is the first open-source, real-time PMML consumer (scoring system). One advantage of PMML systems is that it can reduce time to deploy production models from 1-2 months to 1-2 days - a pain point that may be less obvious if your data mining exposure is competitions or MOOCs. Kamanja is free on Github, supports Kafka, MQ, Spark, HBase and Cassandra among other things. Being a new open-source product, initially, Kamanja supports rules, trees and regression. I will cover an architecture of a sample application using multiple real-time open source data, such as social network campaigns and tracking sentiment for the bank client and its competitors. Other real-time architectures cover credit card fraud detection. A brief demo will be given of the social network analysis application, with text mining. An overview of products in the space will include popular Apache big data systems, real-time systems and PMML systems. For more details: http://kamanja.org/ http://www.meetup.com/SF-Bay-ACM/events/223615901/ http://www.sfbayacm.org/event/kamanja-new-open-source-real-time-system-scoring-data-mining-models Venue sponsored by eBay, Food and live streaming sponsored by LigaDATA, San Jose, CA, July 27, 2015 Chapter Chair Bill Bruns Data Science SIG Program Chair Greg Makowski Vice Chair Ashish Antal Volunteer Coordinator Liana Ye Volunteers Joan Hoenow, Stephen McInerney, Derek Hao, Vinay Muttineni Camera Tom Moran Production Alex Sokolsky Copyright © 2015 ACM San Francisco Bay Area Professional Chapter
Views: 1158 San Francisco Bay ACM
IDC's Perspective on Big Data Outside of HPC
 
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In this video from the IDC Breakfast Briefing at ISC'13, Steve Conway presents: IDC's Perspective on Big Data Outside of HPC. View the slides: http://www.slideshare.net/insideHPC/idc-perspectives-on-big-data-outside-of-hpc Check out more talks from the show at our ISC'13 Video Gallery: http://insidehpc.com/isc13-video-gallery/
Views: 216 RichReport
HR Analytics: Using Machine Learning to Predict Employee Turnover - Matt Dancho, Business Science
 
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This presentation was recorded at #H2OWorld 2017 in Mountain View, CA. Enjoy the slides: https://www.slideshare.net/0xdata/hr-analytics-using-machine-learning-to-predict-employee-turnover. Learn more about H2O.ai: https://www.h2o.ai/. Follow @h2oai: https://twitter.com/h2oai. - - - In this talk, we discuss how we implemented H2O and LIME to predict and explain employee turnover on the IBM Watson HR Employee Attrition dataset. We use H2O’s new automated machine learning algorithm to improve on the accuracy of IBM Watson. We use LIME to produce feature importance and ultimately explain the black-box model produced by H2O. Matt Dancho is the founder of Business Science (www.business-science.io), a consulting firm that assists organizations in applying data science to business applications. He is the creator of R packages tidyquant and timetk and has been working with data science for business and financial analysis since 2011. Matt holds master’s degrees in business and engineering, and has extensive experience in business intelligence, data mining, time series analysis, statistics and machine learning. Connect with Matt on twitter (https://twitter.com/mdancho84) and LinkedIn (https://www.linkedin.com/in/mattdancho/).
Views: 3584 H2O.ai
Dive into IBM SPSS Text Analytics for Surveys
 
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Check out this demonstration of IBM SPSS Text Analytics for Surveys to help you get up and running quickly with your free trial. Learn more about IBM SPSS http://ibm.co/spsstrial Subscribe to the IBM Analytics Channel: https://www.youtube.com/subscription_center?add_user=ibmbigdata The world is becoming smarter every day, join the conversation on the IBM Big Data & Analytics Hub: http://www.ibmbigdatahub.com https://www.youtube.com/user/ibmbigdata https://www.facebook.com/IBManalytics https://www.twitter.com/IBMAnalytics https://www.linkedin.com/company/ibm-big-data-&-analytics https://www.slideshare.net/IBMBDA
Views: 9582 IBM Analytics
Data Mining, Лекция №13
 
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Техносфера Mail.ru Group, МГУ им. М.В. Ломоносова. Курс "Алгоритмы интеллектуальной обработки больших объемов данных", Лекция №13 "Глубокие нейронные сети" Лектор - Павел Нестеров Трудности обучения многослойного персептрона. Предобучение используя РБМ. Глубокий автоэнкодер, глубокая многослойная нейросеть. Deep belief network и deep Boltzmann machine. Устройство человеческого глаза и зрительной коры головного мозга. Сверточные сети. Слайды лекции http://www.slideshare.net/Technosphere1/lecture-12-47107587 Другие лекции курса Data Mining | https://www.youtube.com/playlist?list=PLrCZzMib1e9pyyrqknouMZbIPf4l3CwUP Наш видеоканал | http://www.youtube.com/user/TPMGTU?sub_confirmation=1 Официальный сайт Технопарка | https://tech-mail.ru/ Официальный сайт Техносферы | https://sfera-mail.ru/ Технопарк в ВКонтакте | http://vk.com/tpmailru Техносфера в ВКонтакте | https://vk.com/tsmailru Блог на Хабре | http://habrahabr.ru/company/mailru/ #ТЕХНОПАРК #ТЕХНОСФЕРА x
Hadoop. Введение в Big Data и MapReduce
 
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Техносфера Mail.ru Group, МГУ им. М.В. Ломоносова. Курс "Методы распределенной обработки больших объемов данных в Hadoop" Лекция №1 "Введение в Big Data и MapReduce" Лектор - Алексей Романенко. Что такое «большие данные». История возникновения этого явления­. Необходимые знания и навыки для работы с большими данными. Что такое Hadoop, и где он применяется. Что такое «облачные вычисления», история возникновения и развития технологии. Web 2.0. Вычисление как услуга (utility computing). Виртуализация. Инфраструктура как сервис (IaaS). Вопросы параллелизма. Управление множеством воркеров. Дата-центры и масштабируемость. Типичные задачи Big Data. MapReduce: что это такое, примеры. Распределённая файловая система. Google File System. HDFS как клон GFS, его архитектура. Слайды лекции http://www.slideshare.net/Technopark/lecture-01-48215730 Другие лекции курса | https://www.youtube.com/playlist?list=PLrCZzMib1e9rPxMIgPri9YnOpvyDAL9HD Наш видеоканал | http://www.youtube.com/user/TPMGTU?sub_confirmation=1 Официальный сайт Технопарка | https://tech-mail.ru/ Официальный сайт Техносферы | https://sfera-mail.ru/ Технопарк в ВКонтакте | http://vk.com/tpmailru Техносфера в ВКонтакте | https://vk.com/tsmailru Блог на Хабре | http://habrahabr.ru/company/mailru/ #ТЕХНОПАРК #ТЕХНОСФЕРА x
Extracting twitter data using flume
 
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PPT for the video can be found at :- http://www.slideshare.net/bharat3khanna/extracting-twitter-data-using-apache-flume
Views: 1516 BHARAT KHANNA
Advanced Munging in H2O with Matt Dowle
 
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In this video H2O.ai Hacker Matt Dowle, the main author of R's data.table package, talks about how H2O's data munging capabilities compare against best in class solutions including Sparkl SQL, Impala, data.table and more. Slides here: http://www.slideshare.net/0xdata/h2o-big-join-slides. Event page here: http://open.h2o.ai/nyc.html. Contribute to H2O open source machine learning software https://github.com/h2oai Check out more slides on open source machine learning software at: http://www.slideshare.net/0xdata
Views: 1324 H2O.ai
WHOOPS, THE NUMBERS ARE WRONG! SCALING DATA QUALITY @ NETFLIX
 
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Netflix is a famously data-driven company. Data is used to make informed decisions on everything from content acquisition to content delivery, and everything in-between. As with any data-driven company, it’s critical that data used by the business is accurate. Or, at worst, that the business has visibility into potential quality issues as soon as they arise. But even in the most mature data warehouses, data quality can be hard. How can we ensure high quality in a cloud-based, internet-scale, modern big data warehouse employing a variety of data engineering technologies? Link to slides: https://www.slideshare.net/Hadoop_Summit/whoops-the-numbers-are-wrong-scaling-data-quality-netflix In this talk, Michelle Ufford will share how the Data Engineering & Analytics team at Netflix is doing exactly that. We’ll kick things off with a quick overview of Netflix’s analytics environment, then dig into details of our data quality solution. We’ll cover what worked, what didn’t work so well, and what we plan to work on next. We’ll conclude with some tips and lessons learned for ensuring data quality on big data. Speaker: Michelle Ufford Staff Engineer, Data Engineering & Analytics, Netflix Link to slides: https://www.slideshare.net/Hadoop_Summit/whoops-the-numbers-are-wrong-scaling-data-quality-netflix Link to event page: https://dataworkssummit.com/san-jose-2017/sessions/whoops-the-numbers-are-wrong-scaling-data-quality-netflix/
Views: 1698 DataWorks Summit
Knowledge Graphs as a Data Platform - Data Architecture Summit 2017
 
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Slides available here: https://www.slideshare.net/BenjaminNussbaum/knowledge-graphs-as-a-data-platform Big data has given rise to massive volumes of highly interconnected and increasingly complex information, coming from many sources. This introduces a host of implementation challenges that require knowledge in building intelligent systems. While many novel solutions exist to model and manage complex data – across the NoSQL and especially the Graph Database space – there are crucial limitations to these solutions. We discuss how to get the most out of complex, multi-sourced, heterogeneous data by showing how to model it expressively, migrate it efficiently, and query it intuitively; using knowledge graphs as a data platform for knowledge management. Learn how knowledge graphs can eliminate many of the challenges of working with complex data: traversing complex relationships, drawing crucial insight, and effectively analyzing data to fully harness its value. Help in managing your complex data through a reference architecture and connected data platform is available at www.graphgrid.com
Views: 2455 GraphGrid

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