English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 1h 31m | 261 MB
In the information age, companies need skilled professionals who can glean useful intelligence from troves of data. These data science roles can be challenging, rewarding, and lucrative. If you’re interested in pursuing a career in this growing field, then the Certified Analytics Professional (CAP) certification may be right for you. In this course, Jungwoo Ryoo provides an expedited overview of each of the first four domains in the CAP exam, helping you get up to speed with some of the core data science concepts covered on the test. After going over the history of CAP and related certifications, he dives into domains 1–4: Business Problem Framing, Analytics Problem Framing, Data, and Methodology.
Topics include:
- Business and analytics problem framing
- Collecting requirements
- Reformulating problem statements
- Metrics of success
- Working effectively with data
- Acquiring, cleaning, and sharing data
- Documenting and reporting findings
- Methodology
- Descriptive, predictive, and prescriptive analysis
Table of Contents
Introduction
1 The growing field of analytics
Certified Analytics Professional (CAP)
2 Introduction
3 CAP history
4 CAP domains
5 Related certifications
6 Career paths
Domain One Business Problem Framing
7 Business problems
8 Stakeholder identification and analysis
9 Collecting requirements
10 Determining the feasibility
11 Problem refinement
Domain Two Analytics Problem Framing
12 Business problems to analytics problems
13 Reformulating problem statements
14 Drivers and relationships to outputs
15 Assumptions
16 Metrics of success
17 Stakeholder agreement
Domain Three Data
18 Working effectively with data
19 Identifying and prioritizing data needs
20 Acquiring data
21 Cleaning and sharing data
22 Identifying relationships
23 Documenting and reporting findings
24 Redefining the problem statements
Domain Four Methodology
25 Selecting approaches
26 Descriptive analysis
27 Predictive analysis
28 Prescriptive analysis
29 Selecting software tools
30 R
31 Tableau
32 Select model testing approaches
Case Studies
33 The context
34 Problem framing
35 Data
36 Visualization
37 Making predictions
Conclusion
38 Next steps
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