Learn what it takes to become a Certified Analytics Professional (CAP). Explore the core data science topics covered in the CAP exam.
Overview
Syllabus
Introduction
- The growing field of analytics
- What you should know
- Introduction
- CAP history
- CAP domains
- Related certifications
- Career paths
- Identifying business problems
- Identifying and analyzing stakeholders
- Collecting requirements
- Determining the feasibility
- Refining the problem
- Transforming business problems to analytics problems
- Reformulating problem statements
- Defining drivers and relationships to outputs
- Stating assumptions
- Defining success metrics
- Obtaining stakeholder agreement
- 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
- 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
- Bike rental analysis
- Framing a problem
- Using RStudio for predictive analysis
- Using Tableau to visualize statistics
- Using Tableau to making predictions
- 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
- 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
- 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
- Business intelligence examples
- Selecting a methodology
- Building a model
- Deploying a model
- Managing the model lifecycle
- Next steps and additional study resources
Taught by
Jungwoo Ryoo