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LinkedIn Learning

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

via LinkedIn Learning

Overview

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

Syllabus

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

Taught by

Jungwoo Ryoo

Reviews

4.7 rating at LinkedIn Learning based on 434 ratings

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