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.
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?
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 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
Consumption and Application
Big Data Architecture
The Role of Architecture
What and why
Data Governance Processes
Data Integration and Quality
Search and Visualization
Data Management and Data Access o Hadoop and NoSQL
Big Data ... Big Architecture – Summary Big Data Technology
The Technology Landscape – Overview
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
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
Infrastructure Limitations Must-Have Skills for Big Data Practitioners Course Outline Next-Generation Data Warehouse
The three s’s: scalability, sustainability, and stability
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
Characteristics of Systems
Chaos Theory Applications Game Theory
An Illustrative Advertising Game
Dominant Strategies and Nash Equilibria
Naïve Bayes classifier
Reinforcement learning (Rocchio algorithm)
Input layer, hidden layer, output layer o Forward pass o Back Propagation
How Is CNN/ConvNets different?
AlexNet Architecture - ImageNet 2012
Case Study: GoogLeNet Search Algorithm Types of Search Algorithm
Differences between old and new search engine methods
Applications of Search Algorithm
List Optimization o Indexing
Google TensorFlow Definition
Data Flow Graph
Google TensorFlow Basic Elements
TensorBoard: Visual Learning
Natural Language Processing
Real-World Game Data Processing
Intermittent Recurrent Data Processing
Machine Learning Implementations R
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
Iterative Processing of Data
Storage and Data Format Support Presto
Extensibility – plug-ins
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.
25.000,- For registrations before 31th of October 21.000,-
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