Certified Analytics Professional (CAP) Cert Prep: Domains 1–4

Learn what it takes to become a Certified Analytics Professional (CAP). Explore the core data science topics covered in the CAP exam.

Introduction

• The growing field of analytics
• What you should know

1. Certified Analytics Professional (CAP)

• Introduction
• CAP history
• CAP domains
• Related certifications
• Career paths

2. Business Problem Framing

• Identifying business problems
• Identifying and analyzing stakeholders
• Collecting requirements
• Determining the feasibility
• Refining the problem

3. Analytics Problem Framing

• Transforming business problems to analytics problems
• Reformulating problem statements
• Defining drivers and relationships to outputs
• Stating assumptions
• Defining success metrics
• Obtaining stakeholder agreement

4. Data

• Working effectively with data
• Identifying and prioritizing data needs
• Acquiring data
• Cleaning, transforming, and validating data
• Identifying relationships in data
• Documenting and reporting findings
• Redefining problem statements

5. Methodology (Approach) Selection

• Identifying available problem-solving methodologies
• Evaluating and selecting descriptive analysis
• Evaluating and selecting predictive analysis
• Evaluating and selecting prescriptive analysis
• Selecting software tools
• Using R to analyze data
• Using Tableau to visualize data

6. Case Study 1

• Bike rental analysis
• Framing a problem
• Using RStudio for predictive analysis
• Using Tableau to visualize statistics
• Using Tableau to making predictions

7. Model Building

• Understanding model building
• Identifying model structures
• Build and verify the models
• Running and evaluating models
• Calibrating models and data
• Integrating the models
• Documenting findings: ROC
• Communicating findings

8. Deployment

• Understanding deployment
• Performing business validation of the model
• Developing a deployment plan and delivering it
• Creating model requirements
• Delivering, monitoring, and sustaining the production model or system
• Understanding deployment approaches
• Understanding DMAIC and CRISP-DM
• Project management approaches to deployment

9. Model Lifecycle Management

• Understanding model lifecycle management
• Tracking model quality
• Recalibrating the model through validation
• Maintaining the model
• Supporting training activities
• Evaluating the business benefit of the model over time

10. Case Study 2

• Business intelligence examples
• Selecting a methodology
• Building a model
• Deploying a model
• Managing the model lifecycle

Conclusion

• Next steps and additional study resources