Optional: Using Your Portfolio – Week 3

Course 8 – Google Data Analytics Capstone: Complete a Case Study Quiz Answers

Week 3: Using Your Portfolio

GOOGLE DATA ANALYTICS PROFESSIONAL CERTIFICATE

Complete Coursera Study Guide

TABLE OF CONTENT

Using Your Portfolio – Coursera Answers – Introduction

In this part of the Google Data Analytics Capstone course from Coursera, you will expand on your Google Data Analytics skills and prepare yourself for professional certification. You’ll learn how to discuss your portfolio in interviews, highlight specific skills and create an elevator pitch for your case study. This section of the course also provides useful and practical tips to help you position yourself as a top applicant for data analyst jobs. With these tools and techniques, you can confidently showcase your Google Data Analytics knowledge and make a lasting impression during job interviews.

Learning Objectives

  • Discuss the benefits and uses of case studies and portfolios in the job search.
  • Discuss the use of case studies and portfolios when communicating with recruiters and potential employers.

TEST YOUR KNOWLEDGE ON EFFECTIVE INTERVIEW TECHNIQUES

1. An elevator pitch gives potential employers a quick, high-level understanding of your professional experience. What are the key considerations when creating an elevator pitch? Select all that apply.

  • Consider your audience’s interests (Correct)
  • Focus on your process over the results (Correct)
  • Keep it fresh by not over-practicing it
  • Make sure it’s short enough that it can be explained to someone during an elevator ride (Correct)

Correct: Key considerations of an elevator pitch include keeping it short, considering your audience’s interests, and focusing on your process.

2. What are the key purposes of discussing a case study during an interview? Select all that apply.

  • Recommend real-world solutions based on your own work (Correct)
  • Negotiate a fair salary for the position
  • Outline your thinking about a data analytics scenario for your interviewer (Correct)
  • Ask your potential employer questions about the company

Correct: You may discuss a case study during an interview to outline your thinking about a data analytics scenario or recommend real-world solutions based on your own work.

3. If an interviewer says, “Tell me about yourself,” it’s important to limit your response to topics related to data analytics.

  • True
  • False (Correct)

Correct: If you are asked to tell an interviewer about yourself, your goal is to positively and accurately represent yourself using past and present experiences and skills. Experiences and skills gained from previous work of any kind can be useful to share.

4. During an interview, you will likely respond to technical questions, practical knowledge questions, and questions about your personal experiences. What strategies can help you prepare to respond effectively? Select all that apply.

  • Write down your answers to common questions (Correct)
  • Practice your responses until they feel natural and unrehearsed (Correct)
  • Brainstorm examples from your own experiences that support your answers (Correct)
  • Copy real-world examples from more experienced professionals to include in your responses

Correct: To prepare for an interview, write down answers to common questions, brainstorm examples from your own experiences, and practice your responses until they feel natural and unrehearsed.

5. Imagine that an interviewer asks, “How do you maintain data integrity?” What topics does this question give you the opportunity to discuss? Select all that apply.

  • The reasons you strongly preference SQL over spreadsheets for data cleaning
  • The methods you would use for error checking and data validation (Correct)
  • The importance of reliability and accuracy in good data analysis (Correct)
  • The impact that issues with your data can have on business decisions (Correct)

Correct: This question gives you the opportunity to discuss the methods you use for error checking and validation as part of your data cycle process. In addition, you can point out that reliability and accuracy are essential parts of good data analysis and any issues with your data can have a major impact on data-driven business decisions.

USING YOUR PORFOLIO – CONCLUSION

The course provides you with useful tips to help you position yourself as a top applicant for data analyst jobs. With these tools and techniques, you can confidently showcase your Google Data Analytics knowledge and make a lasting impression during job interviews. If you want to learn more about how to use data analytics in your field, join the learning experience on Coursera today.

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