Module 5: Endless Career Possibilities Answers (Part 4: Q46–54)

This is Part 4 of the Module 5 quiz answers for “Endless Career Possibilities)” from the Google Data Analytics Professional Certificate on Coursera.

Here, we’ll walk through questions 46 to 54 with detailed explanations to support your learning.

To find answers to the remaining questions, check out the full module breakdown below:

46. Describe the difference between a question and a problem in data analytics.

  • A question is a topic to investigate, whereas a problem is a subject to investigate.
  • A question can have many answers, whereas a problem only has one solution.
  • A question is uncertain, whereas a problem is clearly specified.
  • A question is designed to discover information, whereas a problem is an obstacle or complication that needs to be solved. ✅

Explanation:
Questions help guide research, while problems indicate something that needs to be addressed or fixed.

47. Fill in the blank: A business task is described as the _____ a data analyst answers for a business.

  • complaint
  • comment
  • question ✅
  • solution

Explanation:
A business task centers around a specific question or goal that drives the data analysis.

48. Fill in the blank: _____ in data analytics is when the data analysis process does not create or reinforce bias.

  • Predictability
  • Transparency
  • Fairness ✅
  • Consideration

Explanation:
Fairness ensures inclusivity and balance in how data is collected, analyzed, and interpreted.

49. A data analyst at a restaurant analyzes data about past sales and customer trends. They identify that the restaurant does not generate enough revenue to pay the staff whenever the restaurant is open before 4 p.m. The restaurant then adjusts their hours of operation to open at 4 p.m. What is this an example of?

  • Using intuition to create efficiencies
  • Following data ethics
  • Survey data gathering
  • Data-driven decision-making ✅

Explanation:
They used real data on revenue trends to adjust their business strategy, rather than assumptions.

50. A problem is an obstacle to be solved, an issue is a topic to investigate, and a question is designed to discover information.

  • True ✅
  • False

Explanation:
These terms define distinct stages in the data analytics thinking process—problem = obstacle, issue = area of concern, question = inquiry.

51. A data analyst focuses much of their work effort for a business on what?

  • Complaints
  • Strategies
  • Breakthroughs
  • Business tasks ✅

Explanation:
Business tasks direct the analyst’s role—identifying, exploring, and solving problems to support decision-making.

52. When reviewing student data, a data analyst calculates course pass rates across the entire student population in two ways: once for students in academic support programs and once for students not utilizing academic support. What would be an example of a factor creating a bias in this example?

  • Ensuring a large enough sample size is used
  • Picking and choosing data from the data set
  • Not accounting for opportunities to utilize support programs ✅
  • Considering systemic issues that may influence data

Explanation:
Failing to account for varying opportunities to access academic support programs can skew the results and create bias in the analysis.

53. Fill in the blank: A problem is an obstacle to be solved, an issue is a topic to investigate, and a _____ is designed to discover information.

  • question ✅
  • breakthrough
  • business task
  • theme

Explanation:
A question is formulated to guide the process of discovering specific information or insights.

54. Which of the following are examples of fairness in data analysis? Select all that apply.

  • Making sure a sample population represents all groups ✅
  • Considering systematic factors that may influence data ✅
  • Picking and choosing which data to include from a dataset
  • Factoring in social contexts that could create bias in conclusions ✅

Explanation:
Fairness involves ensuring representation, considering systemic factors, and factoring in social contexts. Picking and choosing data selectively introduces bias and should not be selected.

Part 3 (Q31–45)

Congratulations! You’ve completed all 65 questions. Share this post if it helped you, and check out other Coursera quiz answers below.

Related contents:

Module 1: Introducing data analytics and analytical thinking
Module 2: The wonderful world of data
Module 3: Set up your data analytics toolbox
Module 4: Become a fair and impactful data professional
Module 5: *Course challenge*

You might also like:

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
Course 8: Google Data Analytics Capstone: Complete a Case Study

Leave a Reply