Cert Prep: Certified Analytics Professional (CAP)
2h 35mIntermediate2023-02-01
Authors
Jungwoo Ryoo
Teaches IT, cybersecurity, and risk analysis at Penn State
Course details
Want to accelerate your career in data science and analytics? Consider earning the Certified Analytics Professional (CAP) credential. This premier data science certification shows potential employers that you can glean insights from data and use your findings to determine logical next steps. In this course, Jungwoo Ryoo provides test takers with an understanding of how a core set of data science topics are relevant and necessary to obtain a CAP credential in an expedited fashion. Jungwoo covers the seven domains of the exam: business problem framing, analytics problem framing, data, methodology, model building, deployment, and lifecycle management. He also shares case studies that demonstrate how the CAP knowledge domain concepts work in the real world.
Skills covered
RStudioTableauTableau SoftwareRData AnalysisCert PrepData ScienceBusiness Analysis and StrategyBusiness Software and ToolsOpen Source
Concepts
Introduction
- The growing field of analytics
- What you should know
Certified Analytics Professional (CAP)
- Introduction
- CAP history
- CAP domains
- Related certifications
- Career paths
Business Problem Framing
- Identifying business problems
- Identifying and analyzing stakeholders
- Collecting requirements
- Determining the feasibility
- Refining the problem
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
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
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
Case Study 1
- Bike rental analysis
- Framing a problem
- Using RStudio for predictive analysis
- Using Tableau to visualize statistics
- Using Tableau to making predictions
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
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
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
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