TDWI Analytics Fundamentals, Hong Kong

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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

Why have you chosen us?

I have attended a training from Bilginc IT Academy before and I was satisfied.

I have attended a training from a different provider and it was not helpful.

Other

How many employees do you have in your IT department?

0 – 50

50 – 250

250 – 1000

1000+

TDWI Analytics Fundamentals Training in Hong Kong

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TDWI Analytics Fundamentals Training in Hong Kong

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