Google Data Analytics Professional Certificate Google Data Analytics Capstone: Complete a Case Study Syllabus

Google Data Analytics Capstone: Complete a Case Study syllabus — full chapter and topic list

Expand any chapter to see its topics and sub-topics. This is the whole examinable outline for Google Data Analytics Capstone: Complete a Case Study in Google Data Analytics Professional Certificate, not a summary of it.

1. 1

   Learn About Capstone Basics

   3 topics

   • Purpose of the capstone project
   • Choosing a case study track
   • Applying the six analysis phases
2. 2

   Optional: Building Your Portfolio

   3 topics

   • What makes a strong portfolio
   • Documenting your analysis process
   • Hosting projects online
3. 3

   Optional: Using Your Portfolio

   2 topics

   • Presenting case study results
   • Telling your data story to employers
4. 4

   Putting Your Certificate to Work

   3 topics

   • Completing the case study
   • Job search and interview preparation
   • Networking and personal branding

Google Data Analytics Capstone: Complete a Case Study flashcards for Google Data Analytics Professional Certificate

19 of 50 cards from the Google Data Analytics Capstone: Complete a Case Study deck — real questions with worked answers.

1. What is the primary purpose of the Google Data Analytics capstone project?

   To apply everything learned in the certificate program to a realistic case study, producing a complete, end-to-end analysis that can be added to a professional portfolio and shared with potential employers.

2. How does the capstone case study differ from the earlier courses in the Google Data Analytics Certificate?

   Earlier courses teach individual skills and concepts in guided lessons; the capstone is self-directed — you independently work through an entire analysis from business task to recommendations, making your own decisions about tools, methods, and presentation.

3. Why do employers value a completed capstone case study from a job candidate?

   It provides concrete evidence of hands-on skills — showing the candidate can work with real data, follow a structured analysis process, and communicate insights — rather than just claiming knowledge on a resume.

4. What two pre-built case study tracks does the Google Data Analytics capstone offer?

   Track 1: Cyclistic, a fictional bike-share company (analyzing how casual riders and annual members use bikes differently); Track 2: Bellabeat, a fictional wellness technology company for women (analyzing smart device usage data).

5. In the Cyclistic bike-share case study, what is the central business question?

   How do annual members and casual riders use Cyclistic bikes differently? The goal is to design marketing strategies that convert casual riders into annual members, since members are more profitable.

6. In the Bellabeat case study, what is the central business task?

   Analyze smart device fitness data to gain insight into how consumers use non-Bellabeat smart devices, then apply those insights to one Bellabeat product to guide the company's marketing strategy.

7. Besides the two provided tracks, what third option does the capstone allow?

   Choosing your own case study: selecting your own dataset and business question, which allows a more personalized portfolio piece but requires more independent scoping and data sourcing.

8. What is one advantage and one disadvantage of choosing your own dataset instead of a provided capstone track?

   Advantage: the project is unique and can align with your interests or target industry, making your portfolio stand out. Disadvantage: you must find, vet, and clean the data yourself with less structured guidance, which takes more time.

9. List the six phases of the data analysis process used throughout the Google Data Analytics Certificate.

   Ask, Prepare, Process, Analyze, Share, and Act.

10. What happens in the Ask phase of the data analysis process?

    You define the problem and business task, identify stakeholders and their expectations, and formulate the guiding questions the analysis must answer.

11. What happens in the Prepare phase of the data analysis process?

    You identify and collect the data needed, determine where it is stored and how it is organized, check data credibility (e.g., ROCCC), and address licensing, privacy, security, and accessibility considerations.

12. What does the ROCCC framework stand for when evaluating data credibility in the Prepare phase?

    Reliable, Original, Comprehensive, Current, and Cited — good data sources satisfy all five criteria.

13. What happens in the Process phase of the data analysis process?

    You clean and transform the data: choosing tools, removing duplicates and errors, handling missing or outlier values, ensuring data integrity, and documenting every cleaning step.

14. What happens in the Analyze phase of the data analysis process?

    You organize, format, and aggregate the data, perform calculations, and identify trends, patterns, and relationships that answer the business question and support data-driven decisions.

15. What happens in the Share phase of the data analysis process?

    You communicate findings to stakeholders using effective visualizations and a clear data story, choosing presentation methods suited to the audience and ensuring the work is accessible.

16. What happens in the Act phase of the data analysis process?

    You (and stakeholders) use the insights to make decisions: you deliver final conclusions and top recommendations, and the business applies them to solve the original problem.

17. In the capstone deliverables, what key output is expected from the Ask phase?

    A clear statement of the business task — a concise summary of the problem the analysis will solve and the questions guiding it.

18. In the capstone deliverables, what key outputs are expected from the Process and Analyze phases?

    Process: documentation of all data cleaning and manipulation steps. Analyze: a summary of the analysis with the calculations, trends, and relationships discovered.

19. In the capstone deliverables, what key outputs are expected from the Share and Act phases?

    Share: supporting visualizations and key findings presented clearly for stakeholders. Act: your top three recommendations based on the analysis.

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