Certified Analytics Professional (CAP) Cert Prep: Domains 5–7

Certified Analytics Professional (CAP) Cert Prep: Domains 5–7
Certified Analytics Professional (CAP) Cert Prep: Domains 5–7
English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 1h 04m | 143 MB

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 the Certified Analytics Professional (CAP) Cert Prep series, 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. In this installment of the series, he provides a study guide for exam domains 5–7. Plus, he shares case studies that demonstrate how the CAP knowledge domain concepts work in the real world.

Topics include:

  • Building, running, and evaluating models
  • Calibrating models and data
  • Validating your model performance
  • Documenting evaluation results
  • Developing a data model deployment approach and plan
  • Project management approaches
  • Tracking model quality with specific criteria
  • Evaluating the business benefit of a model over time
  • Managing data model life cycles
Table of Contents

Introduction
1 Jumpstart your preparation
2 What you should know

Domain 5 Model Building
3 Identifying model structures
4 Running and evaluating models
5 Calibrating models and data
6 Integrating the models
7 Documenting findings ROC
8 Communicating findings

Domain 6 Deployment
9 Performing business validation of the model
10 Developing a deployment plan
11 Creating model requirements
12 Monitoring and sustaining the model
13 Understanding deployment approaches
14 DMAIC and CRISP-DM
15 Project management approaches

Domain 7 Model Life-Cycle Management
16 Overview
17 Tracking model quality
18 Recalibrating the model through validation
19 Maintaining the model
20 Supporting training activities
21 Evaluating the business benefit of the model over time

Case Study
22 Business intelligence
23 Methodology selection
24 Model building
25 Deployment
26 Model life cycle management

Conclusion
27 Next steps and additional resources