Cert Prep: Certified Analytics Professional (CAP)

سرفصل های دوره

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.

01 - Introduction

01 - The growing field of analytics

02 - What you should know

02 - 1. Certified Analytics Professional (CAP)

03 - Introduction

04 - CAP history

05 - CAP domains

06 - Related certifications

07 - Career paths

03 - 2. Business Problem Framing

08 - Identifying business problems

09 - Identifying and analyzing stakeholders

10 - Collecting requirements

11 - Determining the feasibility

12 - Refining the problem

04 - 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

05 - 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

06 - 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

07 - 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

08 - 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

09 - 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

10 - 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

11 - 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

12 - Conclusion

65 - Next steps and additional study resources

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تولید کننده: LinkedIn Learning (Lynda)