Mon Métier en Vrai - Episode 1 : Data analyst
Notre websérie “Mon Métier en Vrai” veut vous faire découvrir chaque semaine les coulisses de neuf métiers d’aujourd’hui. Deux minutes d’humour pour en finir avec les clichés et les idées reçues. Pour le premier épisode, découvrez la vraie vie d’un data analyst.
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UChicago Data Analytics for Business Professionals Certificate
The online Data Analytics for Business Professionals certificate is designed to improve evidence-based decision making skills of business professionals. Watch for a closer look at this online offering.
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DATA & ANALYTICS - IoT - from small data to big data: Building solutions with connected devices
Recorded on Mar 23 2016 at GCP NEXT 2016 in San Francisco.
Businesses can improve visibility into the health of their products and services using data from connected devices, known as the Internet of Things (IoT). Learn how to use the big data and analytics capabilities of GCP to build end-to-end solutions that collect, transform, and analyze this torrent of IoT data. From connecting devices to generating business and customer insights from specific use-cases, learn how to to make IoT work for you.
Speakers: Preston Holmes, Google & Gayathri Rajan, Google
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Cat® MineStar™ for Underground — Data Analytics
Cat MineStar for Underground offers a number of data analytics and reporting tools that can help your operation increase safety, boost productivity and efficiency, and reduce machine failure and downtime.
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Webinar: Snowflake on Azure: Modern Data Analytics
The need to store, transform, analyze and share data is growing exponentially, with demand for Azure-based data analytics and data warehouse solutions also on the rise.
Join Snowflake and Microsoft for Modern Data Analytics and see how we can help you:
• Empower your executives, line-of-business users and data scientists with Snowflake’s multi-cluster, shared data architecture, built-for-the-cloud data warehouse.
• Create a fully integrated data platform, connecting all of the applications you need, including Spark with HDInsight and many others.
• Securely share governed data, in real time, across and between enterprises.
You will also have a chance to see a live demo, which will show you how to provision a new Snowflake account on Azure and start loading your data within minutes.
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Creating and Using Information through Predictive Analytics in Finance
Slides available ► https://goo.gl/7Q9N8s
Full Event ► https://goo.gl/LvnmwY
Graham Giller - Office of Chief Data Science Officer - J.P. Morgan
A review of key issues in performing predictive analytics on data in a financial context.
Predictive analytics is defined in terms of extracting information about future conditional distributions of data, and the importance of a leverageable information advantage is discussed including the measurement of value with respect to benchmarks and the importance of properly modelling causality.
Differences between financial market contexts and retail contexts in terms of the nature of the analysis and the nature of the data is examined, including non-normality, heteroskedasticity, the large degree of correlation found within the data and the existence of power-laws and genuine outliers.
Graham ends by reviewing the actual differences in what we can and should do with data that are the consequences of commodity storage and commodity computation in the big data era.
Session recorded June 16, 2016 at the RavenPack 4th Annual Research Conference, titled "Reshaping Finance with Alternative Data".
Watch all sessions:
► https://goo.gl/3ij1Ev
Visit us at ►https://www.ravenpack.com/
Follow RavenPack on Twitter ► https://twitter.com/RavenPack
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Predictive Analytics within the Service Supply Chain
An overview of the research of Dr. Nagi Gebraeel, Georgia Power Associate Professor in the Stewart School of Industrial & Systems Engineering at Georgia Tech and co-founder of DecisionIQ, a breakthrough analytics platform developed at Georgia Tech that integrates revolutionary new generation of failure predictions with supply chain methodologies to optimize repair operations. He received his MS and PhD from Purdue University in 1998 and 2003, respectively.
Dr. Gebraeel's research interests lie at the intersection of sensor-based Predictive Analytics, Machine Learning, and Asset Management. His key focus is on developing next-generation machine learning tools specifically tailored for real-time equipment diagnostics and prognostics that enable subsequent operational and logistical decision-making. A significant component of his research deals with the scalability of these algorithms to Big Data settings that involve massive amounts of sensor data streams being generated from large fleets of equipment. From the standpoint of application domains, Dr. Gebraeel has a general interest in the energy industry with a focus on power generation, and the manufacturing industry with a focus on discrete and continuous manufacturing.
To learn more about Nagi and his research, please visit https://paro.scl.gatech.edu.
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Data Analysis vs Data Analytics(Data Science)
Data Science is the combination of statistics, mathematics, programming, problem solving, capturing data in ingenious ways, the ability to look at things differently, and the activity of cleansing, preparing, and aligning the data.
Complete Video English - https://goo.gl/WJfPeq
Complete Video Tamil - https://goo.gl/kaWumR
YouTube channel link
www.youtube.com/atozknowledgevideos
Website
http://atozknowledge.com/
Technology in Tamil & English
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Warum nutzen Unternehmen Predictive Analytics?
Studium, Weiterbildung und mehr- du willst mit uns tiefer in die Finanz- und Managementwelt eintauchen? Dann besuch uns doch:
http://www.frankfurt-school.de/en/home/programmes.html?utm_source=youtube&utm_medium=ACQUISITION
Man stelle sich vor, ein Modehändler weiß im Voraus, welche Farben, Schnitte oder Größen seine Kunden in der nächsten Saison nachfragen werden. Man stelle sich vor, Arbeiter in der Fertigung wissen wann und warum eine Maschine in der Zukunft ausfallen wird – bevor dies passiert und bevor die Produktion deswegen angehalten werden muss. Man stelle sich vor, ein Versicherungsunternehmen kann präzise die Wahrscheinlichkeit vorhersagen, mit der ein neuer Kunde einen Verkehrsunfall verursachen wird – und was dieser die Versicherung kostet. All dieses Wissen über die Zukunft würde es den betreffenden Unternehmen erlauben, bereits heute bessere Entscheidungen zu treffen und erfolgreicher zu sein. Aber wie lässt sich die Zukunft vorhersagen?
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Eric Siegel, How Predictive Analytics Delivers on the Promise of Big Data
Author of bestseller Predictive Analytics: The Power to Predict who will Click, Buy, Lie, or Die, this data heavyweight will be presenting on how predictive analytics delivers on the promise of big data.
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