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Oslo 3 dager 21 000 kr
02 Dec
Big Data Fundamentals, Big Data Roadmap and Machine & amp; Deep Learning: Delivering Insights from Big Data [+]
  Big data gets much attention for the changes that it brings to the field of data analytics. We must also realize that it changes the game for data management practitioners. Data architects, data engineers, and data analysts—nearly anyone who works with data—need to learn new skills to succeed in the age of big data. Big data is an important topic for modern analytics, yet it is continuously evolving. Achieving good return on your big data investment requires strategy that focuses on purpose, people, and process before exploring data and technologies. Strategy drives planning and architecture to ensure that big data complements and does not disrupt the existing BI and analytics environment. There are many technologies to leverage the power of big data, but data and technologies alone don’t create insight and value. The real question is how to engage in the big data journey and develop a viable big data strategy. This requires a roadmap to plan, build, and execute. With strategy in place the right technologies become the path to value. Data management and business insights both depend on technology. Machine learning and deep learning technology is at the leading edge of big data analytics. Hadoop is the widely accepted open source technology that establishes the baseline for big data management. The Must-Have Skills for Big Data Practitioners workshop will cover essential techniques and best practices for leveraging the power of big data over three days of in-depth, interactive training.   Price, 25.000,- For registrations before 31th of October 21.000,-   Your Team Will Learn Key characteristics of big data and why “big” is not among the top five The hidden structures of unstructured data and the common types of big data sources Value opportunities and common applications for big data How to define, refine, and apply a big data roadmap The opportunities, technologies, and techniques of machine learning and deep learning Key considerations for implementing machine learning Hadoop components and architecture Hadoop configuration, administration, and management How to use common Hadoop tools  Part 1 Big Data Fundamentals: Creating Value from Non-Traditional Data Sets    Big Data Basics What Is Big Data? Definitions Characteristics (3 V’s plus 2) Types of Big Data Why Big Data Analytics – Extending Advanced Analytics Capabilities Big Data Use Cases o Customer Understanding and Targeting Business Process Optimization o Healthcare Advances Law Enforcement and Public Safety o Sports Performance Improvement Public Transportation and Infrastructure Advances Why Big Data Now? – The Driving Forces Kinds of Big Data – Data Variety Sources of Big Data Web and Social Media o Machine to Machine Other Sources (Big Transaction Data, Biometrics, Human Generated Data, Publicly Available Data, Legacy Documents) Working with Big Data – The Big Picture Big Data Processes Business Case Business Needs and Opportunities Areas of Insight o Expected Outcomes Business Value Projection Technical Case – Big Data Rationale Data Sourcing – Getting Big Data Data Preparation and Storage Data Selection o Data Cleansing Data Integration o Data Reduction Big Data Analytics o Problem Framing Analytic Purpose o Analytic Modeling Data Visualization Consumption and Application Big Data Architecture The Role of Architecture What and why Data Architecture Data Storage Data Access Data Analysis Data Consumption Process Architecture Data Governance Processes Data Integration and Quality Analytics Architecture Machine Learning Predictive Analytics Prescriptive Analytics Descriptive Analytics Reporting Technology Architecture Search and Visualization Data Management and Data Access o Hadoop and NoSQL Big Data ... Big Architecture – Summary Big Data Technology The Technology Landscape – Overview Infrastructure Databases Development and Deployment Environment Analytics – Data Analysis Data Sources – Big Data Providers The Core Technologies o MapReduce o Hadoop Getting Started with Big Data Readiness Assessment – Check Your Position Planning and Preparation – Charting the Course Execution – Navigating the Course Post-Project Activities – At the Destination Best Practices – Lessons Learned Mistakes to Avoid – More Lessons Learned Summary of Key Points – A Quick Review References and Resources – To Learn More Part 2 Big Data Roadmap Big Data and Business Case What is Big Data? – Creating a definition What will it solve? – Potential solutions for an organization (this will be specific for onsite courses) Business Users and Big Data Understanding Roles and Skiils – What does the business user bring to the table? Why should IT look to getting the business users own and drive the initiative? Business User Ownership – What does this entail? Challenges – Issues and Risks Building the Business Case Components of Big Data Business Case How to build the Appropriate Business Case Next Generation of Business Intelligence Analytics and Metrics – What do we derive new? Visualization Requirements – What are the changes and associated challenges? Mashups – Understanding multi-dimensional data management. Metadata is critical and why? Semantics and Ontologies Introduction to Semantic Frameworks – Future of Visualization and Analytics Understanding Semantic integration for Big Data – Where and How? Business Benefits. Using Ontologies for Metadata Management – Case Study Managing Business Rules for Processing - Case Study Big Data and the Data Warehouse The New Landscape What Can We Solve How to Assess and Manage Data For Today and Future Technology overview Hadoop, NoSQL, Cassandra, Big Query, Drill, Redshift, AWS (S3, EC2) Programming with MapReduce Understanding analytical requirements Self-Service Discovery Platforms Challenges of Data Management and Processing MDM, Metadata and More – Have we moved over this? Workloads Data Management Infrastructure Limitations Must-Have Skills for Big Data Practitioners Course Outline Next-Generation Data Warehouse Solution architectures The three s’s: scalability, sustainability, and stability People skills Critical success factors  Big Data Road map Building A Road map Risks and Mitigations Business Driven Objectives Solving A Million Dollar Puzzle Readying The Organization Part 3 Machine & Deep Learning: Delivering Insights from Big Data Chaos Theory Definition Characteristics of Systems Chaos Theory Applications Game Theory Definition Players Strategies Payoffs Equilibrium Concepts Nash Equilibrium An Illustrative Advertising Game Dominant Strategies and Nash Equilibria Hotelling’s Beach Television Scheduling Machine Talk Techniques kNN algorithm Winnow algorithm Naïve Bayes classifier Decision trees Reinforcement learning (Rocchio algorithm) Genetic algorithm Neural Networks Input layer, hidden layer, output layer o Forward pass o Back Propagation Classification Connections Learning Hopfield Network Self-Organization Self-Organizing Networks How Is CNN/ConvNets different? LeNet-5 Architecture AlexNet Architecture - ImageNet 2012 Case Study: GoogLeNet Search Algorithm Types of Search Algorithm PageRank Algorithm Penguin Algorithm Panda Algorithm Hummingbird Algorithm Differences between old and new search engine methods Knowledge Graph Applications of Search Algorithm Enterprise Search Target Marketing Performance Optimization List Optimization o Indexing Google TensorFlow Definition Data Flow Graph Google TensorFlow Basic Elements Variable Operation Session Placeholder TensorBoard TensorBoard: Visual Learning MNIST Dataset TensorBoard Applications Natural Language Processing Image Processing Geo-Coding Processing Gaming Simulators Real-World Game Data Processing Intermittent Recurrent Data Processing Machine Learning Implementations R IBM Watson Microsoft Oracle Advanced Analytics DB Option Enterprise Solutions Machine Learning with Hadoop Tools for Data Preparation/Feature Engineering  Apache Mahout  More Machine Learning Interfaces for Hadoop Visualization Key Points Mashups Lat-Long Processing Semantic Processing Machine Learning Algorithms Iterative Processing of Data Polymaps Data-Driven Documents Apache Tajo Architecture Query Federation Storage and Data Format Support Presto Presto History Archite Connectors Extensibility – plug-ins   KRISH KRISHNAN-INSTRUCTOR Mr. Krishnan is a recognized expert worldwide in the strategy, architecture and implementation of high-performance big data analytics, data warehousing, analytics and business intelligence solutions. He is a visionary data warehouse thought leader, ranked as one of the top 20 data warehouse consultants in the world. Krishnan is an independent analyst, writing and speaking at industry leading conferences, user groups and trade publications. He has authored three books, four eBooks, over 430 plus whitepapers, articles, viewpoints and case studies in Big Data, Analytics, Business Intelligence, Data Warehousing and Data Warehouse Appliances and Architectures. Krishnan is an internationally recognized authority on Unstructured Data, Social Analytics and Big Data, Text mining and Text analytics. An innovator and solution expert, he is recognized for his work in high performance data warehouse architectures and is an acknowledged expert in performance tuning of complex database and data warehouse platforms.  Krishnan’s recent year has evolved in two areas where he is an avid practitioner – Blockchain and OPIOID Crisis. He is working on models and platforms for both these areas. In his 27 plus years of professional experience he has been solving complex solution architecture problems spanning all aspects of data warehousing and business intelligence for Fortune 500 clients. He has designed, architected and tuned some of the world’s largest big data platforms, data warehouses, analytics and business intelligence platforms. Clients include McDonalds, CNA, AON, Edward Jones, USAA, American Express, Sears, Ford, GM, BMW, CMS, Freddie Mac, DHA, Commonwealth Bank of Australia, HBO, Bank of America, Target, Tesco, RBS, JP Morgan Chase, Allstate, Walgreens, Time Warner Cable, Boeing, Macys, GAP, Williams Sonoma, Allianz and more. Krishnan has also implemented cybersecurity, information security and encryption algorithms for Fortune 500 customers, banks, government agencies and stock exchanges across the globe. Krishnan is an Independent Analyst in Big Data, Data Warehouse and Business Intelligence areas covering the entire stack. He is a faculty with TDWI and presents and speaks at DAMA, IRM UK, MIT Symposium and other industry conferences. Krishnan serves as a technology advisor to many startup companies and advises Venture Capital firms on their technology portfolio of investments. He is sought after to assess startup companies in the data management and its associated emerging technology areas. Price, 25.000,- For registrations before 31th of October 21.000,-   OM CGI CGI Group Inc. ble grunnlagt i 1976 og er verdens femte største uavhengige leverandør av tjenester innen IT- og forretningsprosesser. Med 74 000 medarbeidere over hele verden, leverer CGI en portefølje av ende-til-ende løsninger innen avanserte IT- og forretningskonsulenttjenester, systemintegrasjon og utsetting av IT- og forretningsprosesser. CGIs modell med kundenærhet, et globalt leveransenettverk og egne løsninger bidrar til at kundene hurtigere oppnår resultater og bidrar til deres digitale transformasjon. CGI har en samlet årlig omsetning på over 60 milliarder NOK. CGI-aksjene er notert på TSX (GIB.A) og NYSE (GIB). Hjemmeside: www.cginorge.no     [-]
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