TDWI Analytics Fundamentals Training

  • Learn via: Classroom / Virtual Classroom / Online
  • Duration: 1 Day
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  • We can host this training at your preferred location. Contact us!

Analytics is a hot topic, but also a complex topic. This continuously growing field now includes descriptive, diagnostic, predictive, and prescriptive analytics. Applied analytics including optimization, simulation, and automation expand the scope. Data growth also fuels the complexity – unstructured data, big data, social data, data streams, and more. Advanced analytics continues to expand with complex event processing, machine learning, cognitive computing, etc.

In the growing and evolving world of analytics we’re also experiencing a shift of roles and responsibilities. The “data things” that were once seen as IT responsibilities have become critical business skills. Analytics spans a continuum that encompasses IT departments, data scientists, data analysts, business analysts, business managers, and business leadership. It seems that everyone has a stake in analytics. Coordination, cross-functional analysis, data sharing, and governance all become important skills.

There are no prerequisites for this course.

  • Business leaders and managers seeking to understand business dynamics through analytics
  • IT leaders and managers responsible to deliver and to support analytics initiatives
  • BI and analytics architects guiding the design, development, and deployment of analytics
  • BI and analytics designers and developers
  • Business analysts, data analysts, data scientists and those who aspire to these roles

  • The concepts and practices of analytic modeling
  • An analytics topology to make sense of the variety of analytic types and techniques
  • The data side of analytics including data sourcing, data discovery, data cleansing, and data preparation
  • Analytic techniques for exploration, experimentation, and discovery
  • The human side of analytics: communication, conversation, and collaboration
  • The organizational side of analytics: self-service, central services, governance, etc.
  • A bit about emerging techniques and technologies shaping the future of analytics

Module One: Concepts of Analytics

  • Analytics Defined
  • Data Analytics and Business Analytics
    • Variations of Purpose
    • Variations of Skills
  • Why Analytics
    • Cause and Effect
    • Strategy and Analytics
    • Tactics and Analytics
    • Operations and Analytics
    • Systemic Analytics
  • Analytics Processes
    • Problem Framing
    • Problem Modeling
    • Solution Modeling
    • Visualization and Presentation
    • Understanding and Action
  • Analytics Foundations
  • Data
    • Scope of Data
    • Finding Data
    • Observations and Populations
    • Raw Data vs. Summary Data
    • Data Preparation
  • Statistics
    • Histograms
    • Distribution and Deviation
    • Correlation
    • Regression
  • Visualization
    • Visual Design
    • Choosing Charts and Graphs
  • Business Impact
    • Simulation
    • Optimization
    • Automation

Module Two: The Analytics Environment

  • Analytics Stakeholders
  • The Participants
    • Business Stakeholders
    • Analytic Modelers and Data Scientists
    • IT and Data Organizations
  • Analytic Culture
  • Values, Beliefs, and Competencies
    • Numeracy
    • Collaboration
    • Conversation
    • Decision Styles
  • Analytics Organizations
  • Organization Models
    • Self Service
    • Shared Services
    • Central Services
    • Hybrid Organizations
  • Analytics and Governance
  • Analytics Capabilities
  • Business Capabilities
    • Planning
    • Executing
    • Adapting
    • Innovating
  • Analysis Capabilities
    • Evaluating
    • Detecting
    • Predicting
    • Classifying
    • Recommending
    • Monitoring
  • Data Capabilities
    • Measuring
    • Searching and Acquiring
    • Blending and Integrating
    • Securing
    • Provisioning
  • A Capability-Based Framework
    • Discovery Analytics
    • Descriptive Analytics
    • Diagnostic Analytics
    • Predictive Analytics
    • Prescriptive Analytics

Module Three: Analytics Architecture

  • The Big Picture
  • Data Architecture
    • Data Sources and Types
    • Data Acquisition
    • Data Ingestion
    • Persistence
    • Data Management Topology
    • Data Quality and Utility
    • Data Usage / Information Delivery
  • Process Architecture
  • Next Generation BI
    • Extending BI
  • Basic Data Analysis
    • Statistical Analysis
    • Time-Series Analysis
  • Discovery and Prediction
    • Data Mining
    • Predictive Modeling
    • Ensemble Modeling
  • Text and Language Analysis
    • Natural Language Processing
    • Text Mining o People and Behaviors
    • Sentiment Analysis
    • Behavioral Analytics
  • People and Social Media
    • Social Network Analysis
    • Social Media Analytics
  • Events and Data Streams
    • Stream Processing
    • Complex Event Processing
  • Smart Machines
    • Machine Learning
    • Cognitive Computing
  • Technology Architecture
  • Connectivity
    • SQL
    • Messaging
    • Services
    • Replication
    • Virtualization
  • Data Stores
    • RDBMS
    • Columnar
    • MPP
    • MDDB
    • NoSQL
    • In Memory
  • Data Analysis
    • Data Mining
    • Analytic Modeling
    • Big Data Analytics
    • Streaming Analytics
    • Event Processing
    • Machine Learning etc.
  • Data Flow
    • Batch
    • Real-time
    • Streams
  • Management
    • Workflow
    • Service Levels
  • Platforms
    • Servers
    • Appliances
    • Cloud

Module Four: Analytic Modeling

  • The Roles of Models
    • Understanding the Problem Space
    • Understanding the Data
    • Understanding the Language
    • Understanding the Business Dynamics
  • Kinds of Models
    • Framing Models
    • Questioning
    • Kernel Seeking
  • Cause-Effect Models
    • Influence Models
    • Causal Loop Models
  • Data Models
    • Physical Models
    • Logical Models
    • Conceptual Models
  • Language Models
    • Ontology
    • Taxonomy
    • Lexicon
    • Semantics
  • Solution Models
    • Formula Based
    • Algorithm Based
  • Problem Modeling
    • Framing the Problem
    • Influence Diagramming
    • Causal Modeling
  • Solution Modeling
  • Formula Based Modeling
    • Structuring
    • Defining
    • Documenting
    • Developing
    • Parameterizing
    • Visualizing
  • Algorithm Based Modeling
    • Business Understanding
    • Data Understanding
    • Data Preparation
    • Model Building
    • Evaluation
    • Deployment

Module Five: Applied Analytics

  • Discovery Analytics
  • Description
  • Techniques
    • Experimental Design
    • Rule Discovery
    • Data Mining
    • Regression Models
  • Enabled Business Capabilities
  • Examples
  • Descriptive Analytics
    • Description
    • Techniques
    • Statistical Models
    • Probability Distribution Models
    • Monte Carlo Models
  • Enabled Business Capabilities
  • Examples
  • Diagnostic Analytics
  • Description
  • Techniques
    • Control Charts
    • Classification Models
    • Abnormal Condition Models
  • Enabled Business Capabilities
  • Examples
  • Predictive Analytics
  • Description
  • Techniques
    • Regression Models
    • Neural Network Models
    • Time Series Forecasting Models
    • Bayes Theorem Models
  • Enabled Business Capabilities
  • Examples
  • Prescriptive Analytics
  • Description
  • Techniques
    • Discrete Event Models
    • Continuous Simulation Models
    • Optimization Models
    • Linear Programming Models
  • Enabled Business Capabilities
  • Examples

Module Six: Summary and Conclusion

  • Summary of Key Points
  • References and Resources


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