TDWI Analytics Fundamentals. Course Outline. Module One: Concepts of Analytics

Transcription

1 TDWI Analytics Fundamentals Module One: Concepts of Analytics Analytics Defined Data Analytics and Business Analytics o Variations of Purpose o Variations of Skills Why Analytics o Cause and Effect o Strategy and Analytics o Tactics and Analytics o Operations and Analytics o Systemic Analytics Analytics Processes o Problem Framing o Problem Modeling o Solution Modeling o Visualization and Presentation o Understanding and Action Analytics Foundations o Data Scope of Data Finding Data Observations and Populations Raw Data vs. Summary Data Data Preparation o Statistics Histograms Distribution and Deviation Correlation Regression o Visualization Visual Design Choosing Charts and Graphs o Business Impact Simulation Optimization Automation Module Two: The Analytics Environment Analytics Stakeholders o The Participants Business Stakeholders Analytic Modelers and Data Scientists IT and Data Organizations Analytic Culture o Values, Beliefs, and Competencies TDWI page 1 of 6

2 Numeracy Collaboration Conversation Decision Styles Analytics Organizations o Organization Models Self Service Shared Services Central Services Hybrid Organizations o Analytics and Governance Analytics Capabilities o Business Capabilities Planning Executing Adapting Innovating o Analysis Capabilities Evaluating Detecting Predicting Classifying Recommending Monitoring o Data Capabilities Measuring Searching and Acquiring Blending and Integrating Securing Provisioning o A Capability-Based Framework Discovery Analytics Descriptive Analytics Diagnostic Analytics Predictive Analytics Prescriptive Analytics Module Three: Analytics Architecture The Big Picture Data Architecture o Data Sources and Types o Data Acquisition o Data Ingestion o Persistence o Data Management Topology o Data Quality and Utility o Data Usage / Information Delivery Process Architecture o Next Generation BI TDWI page 2 of 6

3 Extending BI o Basic Data Analysis Statistical Analysis Time-Series Analysis o Discovery and Prediction Data Mining Predictive Modeling Ensemble Modeling o Text and Language Analysis Natural Language Processing Text Mining o People and Behaviors Sentiment Analysis Behavioral Analytics o People and Social Media Social Network Analysis Social Media Analytics o Events and Data Streams Stream Processing Complex Event Processing o Smart Machines Machine Learning Cognitive Computing Technology Architecture o Connectivity SQL Messaging Services Replication Virtualization o Data Stores RDBMS Columnar MPP MDDB NoSQL In Memory o Data Analysis Data Mining Analytic Modeling Big Data Analytics Streaming Analytics Event Processing Machine Learning etc. o Data Flow Batch Real-time Streams o Management Workflow TDWI page 3 of 6

4 o Service Levels Platforms Servers Appliances Cloud Module Four: Analytic Modeling The Roles of Models o Understanding the Problem Space o Understanding the Data o Understanding the Language o Understanding the Business Dynamics Kinds of Models o Framing Models Questioning Kernel Seeking o Cause-Effect Models Influence Models Causal Loop Models o Data Models Physical Models Logical Models Conceptual Models o Language Models Ontology Taxonomy Lexicon Semantics o Solution Models Formula Based Algorithm Based Problem Modeling o Framing the Problem o Influence Diagramming o Causal Modeling Solution Modeling o Formula Based Modeling Structuring Defining Documenting Developing Parameterizing Visualizing o Algorithm Based Modeling Business Understanding Data Understanding Data Preparation Model Building Evaluation TDWI page 4 of 6

5 Deployment Module Five: Applied Analytics Discovery Analytics Experimental Design Rule Discovery Data Mining Regression Models Descriptive Analytics Statistical Models Probability Distribution Models Monte Carlo Models Diagnostic Analytics Control Charts Classification Models Abnormal Condition Models Predictive Analytics Regression Models Neural Network Models Time Series Forecasting Models Bayes Theorem Models Prescriptive Analytics Discrete Event Models Continuous Simulation Models Optimization Models Linear Programming Models Module Six: Summary and Conclusion TDWI page 5 of 6

6 Summary of Key Points References and Resources TDWI page 6 of 6