TDWI Predictive Analytics Fundamentals (NEW) Training in Nigeria

There are no prerequisites for this course.

• BI and analytics executives, program managers, architects, and project managers
• Data-driven business professionals who want to learn how to implement the “power to predict”
• Technology professionals who want to develop their understanding of predictive analytics
• Business analysts who want to use predictive techniques in their analytics studies
• Business managers who want to develop a proactive and predictive decision-making style in their operations
• Anyone interested in learning the basics of predictive analytics and how it can drive business improvement

• Definitions, concepts, and terminology of predictive analytics
• What data science is and how it relates to predictive analytics and BI programs
• Purpose, structure, and categories of models
• Methods adapted from statistics, data mining, and machine learning
• Functionality of predictive models and related development approaches
• Common applications and use cases for predictive analytics
• How successful predictive capabilities are enabled by human and organizational factors
• Essential team composition, skills development, and organization models including roles, responsibilities, and accountabilities
• Why business, technical, and management skills are essential for success
• Practical guidance for getting started with predictive analytics

Module 1 - Predictive Analytics Concepts

• What and Why of Predictive Analytics
  • Predictive Analytics Defined
  • Business Value of Predictive Analytics
• The Foundation for Predictive Analytics
  • Statistical Foundation
  • Data Mining Foundation
  • Machine Learning Foundation
  • Data Science Foundation
  • Describing Data Science
  • The Changing Landscape of Data Sources
• Predictive Analytics in BI Programs
  • Predictive Analytics in the BI Stack
  • Predictive Analytics in the BI Roadmap
  • Business, Technical, and Data Dependencies
• Becoming Analytics Driven
  • Business Driven
  • Grass Roots Driven
• Common Applications for Predictive Analytics
  • What Business Needs to Predict
• The Language of Predictive Analytics
  • Making Sense of the Terminology

Module 2 - Understanding Models

• Overview and Context
  • What Are Models?
  • How Models Are Used
  • Categories of Models
  • How Are They Built?
• Enabling Techniques
  • Contributing Communities
• Descriptive Statistics
  • Frequencies and Summaries
  • Variables
  • Relationships
  • Dependent and Independent Variables
• Understanding Probability
  • Statistics Revisited
  • Probability
  • Probability Examples
  • Probability Estimation
  • Odds
  • Logit Transformation
  • Logit Transformation Example
  • Probability Distributions
  • Symmetrical Continuous Distribution
  • Skewed Continuous Distributions
  • Discrete Distributions
  • Distribution Examples

Module 3 - Regression Model Examples

• Regression Models
  • Description
• Linear Regression Models
  • Overview
  • Example
  • Model Description
• Logistic Regression Models
  • Overview
  • Example
  • Steps for Creating the Model
  • Model Description
  • Model Results
  • Predictors and Classifiers
  • Predictor and Classifier Example

Module 4 - Building Predictive Models

• Model Building Processes
  • Data Mining Projects
  • CRISP-DM
  • SEMMA
  • CRISP-DM and SEMMA Compared
• Implementation and Operations Teams
  • A Team Effort
  • Roles and Responsibilities
• Predictive Techniques
  • Probability Values
  • Classification and Clustering
  • Segmentation
  • Association
  • Sequencing
  • Forecasting
• Technology
  • Features and Functions Overview
  • The Tools Landscape
• Model Building Algorithms
  • What and Why
  • Some Examples

Module 5 - Implementing Predictive Capabilities

• Introductory Concepts
  • Distribution View
  • Model Types View
  • Process View
  • Process Overview
• Business Understanding
  • Activities and Deliverables
  • Pragmatics
• Data Understanding
  • Activities and Deliverables
  • Pragmatics
• Data Preparation
  • Activities and Deliverables
  • Pragmatics
• Modeling
  • Activities and Deliverables
  • Pragmatics
• Evaluation
  • Activities and Deliverables
  • Pragmatics
• Deployment
  • Activities and Deliverables
  • Pragmatics

Module 6 - Human Factors in Predictive Analytics

• Analytics Culture
  • Executive Buy-In
  • Strategic Positioning
  • Enterprise Range and Reach
  • Decision Processes
• People and Predictive Analytics
  • The Team
  • The Range of People
  • The Range of Knowledge
  • Readiness
  • Trust and Motivation
  • Expectations and Intent
  • Getting from Analytics to Impact
• Ethics and Predictive Analytics
  • Why Ethics Matters
  • Data and Ethics

Module 7 - Getting Started with Predictive Analytics

• Predictive Analytics Readiness
  • Readiness Checklist
  • Executive Commitment
  • Organizational Buy-In
  • Data Assets
  • Human Assets
  • Technology Assets
• Predictive Analytics Roadmap
  • A Plan to Evolve
  • An Evolving Plan