Module 1: Learn about capstone basics

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In this post, I provide accurate answers and detailed explanations for Module 1: Learn about capstone basics of Course 8: Google Data Analytics Capstone: Complete a Case Study – Google Data Analytics Professional Certificate.

Whether you’re preparing for quizzes or brushing up on your knowledge, these insights will help you master the concepts effectively. Let’s dive into the correct answers and detailed explanations for each question.

Test your knowledge on professional case studies

1. Test your knowledge on professional case studies

  • portfolio ✅
  • capstone
  • personal website
  • problem statement

2. Which of the following are important strategies when completing a case study? Select all that apply.

  • Communicate the assumptions you made about the data ✅
  • Use a programming language
  • Document the steps you’ve taken to reach your conclusion ✅
  • Answer the question being asked ✅

Explanation:
When completing a case study, it’s important to:

  1. Clearly answer the question being asked.
  2. Document the process you followed, including the steps and methodologies used.
  3. Communicate the assumptions you made about the data to provide context for your solution.

Incorrect Option:

  • Use a programming language: While programming can be helpful, it is not mandatory for all case studies. The focus is on showcasing analytical thinking and problem-solving skills.

3. To successfully complete a case study, your answer to the question the case study asks has to be perfect.

  • True
  • False ✅

Explanation:
The purpose of a case study is not necessarily to get a “perfect” answer. Employers are more interested in understanding your thought process, problem-solving approach, and ability to communicate your findings.

4. Which of the following are qualities of the best portfolios for a junior data analyst? Select all that apply.

  • Personal ✅
  • Unique ✅
  • Large
  • Simple ✅

Explanation:
The best portfolios:

  1. Reflect your personal interests and style.
  2. Highlight unique projects that distinguish you from other candidates.
  3. Are simple and easy to navigate, focusing on your skills and accomplishments rather than quantity.

Incorrect Option:

  • Large: As a junior analyst, having a large portfolio is not expected. Quality matters more than quantity.

5. Which of the following are places where you can store and share your portfolio? Select all that apply.

  • Tableau ✅
  • RStudio
  • GitHub ✅
  • Kaggle ✅

Explanation:
Portfolios can be stored and shared on platforms like:

  • GitHub: For sharing code and technical projects.
  • Kaggle: For showcasing data science competitions and projects.
  • Tableau: For sharing data visualizations and dashboards.
    These platforms allow employers to view your work in a professional and accessible way.

Incorrect Option:

  • RStudio: RStudio is a development environment for working with R but is not a portfolio-sharing platform.

6. Fill in the blank: A _____ is a collection of case studies that you can share with potential employers.

  • portfolio ✅
  • capstone
  • problem statement
  • personal website

Explanation:
A portfolio is a collection of case studies, projects, or work samples that you can share with potential employers to showcase your skills, experience, and problem-solving capabilities.

Related contents:

Module 3: Optional: Use your portfolio

You might also like:

Course 1: Foundations: Data, Data, Everywhere
Course 2: Ask Questions to Make Data-Driven Decisions
Course 3: Prepare Data for Exploration
Course 4: Process Data from Dirty to Clean
Course 5: Analyze Data to Answer Questions
Course 6: Share Data Through the Art of Visualization
Course 7: Data Analysis with R Programming

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