Cert Prep: Certified Analytics Professional (CAP)

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.

Concepts

0. Introduction

01 - The growing field of analytics
02 - What you should know

1. Certified Analytics Professional (CAP)

03 - Introduction
04 - CAP history
05 - CAP domains
06 - Related certifications
07 - Career paths

2. Business Problem Framing

08 - Identifying business problems
09 - Identifying and analyzing stakeholders
10 - Collecting requirements
11 - Determining the feasibility
12 - Refining the problem

3. Analytics Problem Framing

13 - Transforming business problems to analytics problems
14 - Reformulating problem statements
15 - Defining drivers and relationships to outputs
16 - Stating assumptions
17 - Defining success metrics
18 - Obtaining stakeholder agreement

4. Data

19 - Working effectively with data
20 - Identifying and prioritizing data needs
21 - Acquiring data
22 - Cleaning, transforming, and validating data
23 - Identifying relationships in data
24 - Documenting and reporting findings
25 - Redefining problem statements

5. Methodology (Approach) Selection

26 - Identifying available problem-solving methodologies
27 - Evaluating and selecting descriptive analysis
28 - Evaluating and selecting predictive analysis
29 - Evaluating and selecting prescriptive analysis
30 - Selecting software tools
31 - Using R to analyze data
32 - Using Tableau to visualize data

6. Case Study 1

33 - Bike rental analysis
34 - Framing a problem
35 - Using RStudio for predictive analysis
36 - Using Tableau to visualize statistics
37 - Using Tableau to making predictions

7. Model Building

38 - Understanding model building
39 - Identifying model structures
40 - Build and verify the models
41 - Running and evaluating models
42 - Calibrating models and data
43 - Integrating the models
44 - Documenting findings - ROC
45 - Communicating findings

8. Deployment

46 - Understanding deployment
47 - Performing business validation of the model
48 - Developing a deployment plan and delivering it
49 - Creating model requirements
50 - Delivering, monitoring, and sustaining the production model or system
51 - Understanding deployment approaches
52 - Understanding DMAIC and CRISP-DM
53 - Project management approaches to deployment

9. Model Lifecycle Management

54 - Understanding model lifecycle management
55 - Tracking model quality
56 - Recalibrating the model through validation
57 - Maintaining the model
58 - Supporting training activities
59 - Evaluating the business benefit of the model over time

10. Case Study 2

60 - Business intelligence examples
61 - Selecting a methodology
62 - Building a model
63 - Deploying a model
64 - Managing the model lifecycle

Conclusion

65 - Next steps and additional study resources