Module 2: The Wonderful World of Data Answers (Part 4: Q46–63)

This is Part 4 of the Module 2 quiz answers for “The Wonderful World of Data” from the Google Data Analytics Professional Certificate on Coursera.

Here, we’ll walk through questions 46 to 63 with detailed explanations to support your learning.

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

46. The manager at a music shop notices that more trombones are repaired on the days when Alex and Jasmine work the same shift. After some investigation, the manager discovers that Alex is excellent at fixing slides, and Jasmine is great at shaping mouthpieces. Working together, Alex and Jasmine repair trombones faster. The manager is happy to have discovered this relationship and decides to always schedule Alex and Jasmine for the same shifts. In this scenario, the manager used which quality of analytical thinking?

  • Visualization
  • Problem-orientation
  • Correlation ✅
  • Big-picture thinking

Explanation:
They observed a relationship between staff combinations and performance outcomes.

47. Fill in the blank: In order to get to the root cause of a problem, a data analyst should ask “Why?” ________ times.

  • five ✅
  • three
  • seven
  • four

Explanation:
The five whys technique typically requires asking “why” five times to progress from surface symptoms to fundamental causes.

48. A company is receiving negative comments on social media about their products. To solve this problem, a data analyst uses each of their five analytical skills: curiosity, understanding context, having a technical mindset, data design, and data strategy. This makes it possible for the analyst to use facts to guide business strategy and figure out how to improve customer satisfaction. What is this an example of?

  • Data science
  • Gap analysis
  • Data-driven decision-making ✅
  • Data visualization

Explanation:
Data-driven decision-making applies structured analytical approaches to convert raw information into actionable business insights.

49. Data analysts following data-driven decision-making use the analytical skills of curiosity, having a technical mindset, and data design. What other two analytical skills would they employ? Select all that apply.

  • knowledge
  • data strategy ✅
  • efficiency
  • understanding context ✅

Explanation: Comprehensive analysis requires both strategic resource management (data strategy) and situational awareness (understanding context).

50. Curiosity is the analytical skill of using your instinct to solve problems.

  • True
  • False ✅

Explanation:
Curiosity involves active investigation and learning, not reliance on instinct or unsupported assumptions.

51. Adding descriptive headers to columns of data in a spreadsheet is an example of which analytical skill?

  • Having a technical mindset
  • Understanding context ✅
  • Data strategy
  • Curiosity

Explanation:
Adding descriptive headers demonstrates understanding context by clarifying data meaning and improving interpretability.

52. A company has recently tasked their data science team with figuring out what is causing the decline in production at one of their plants. The data analysts ask a number of questions trying to get to the root cause of the problem. This technique is known as what?

  • Inquiry
  • The five whys ✅
  • Curiosity
  • Strategizing

Explanation:
The “five whys” technique involves asking “why” multiple times to drill down to the root cause of an issue.

53. Fill in the blank: Data analysts use the five analytical skills of curiosity, understanding context, having a technical mindset, data design, and data strategy to make _____ decisions.

  • forward-looking
  • data-driven ✅
  • intuitive
  • more efficient

Explanation:
Data-driven decisions result from applying analytical skills to factual evidence rather than intuition or guesswork.

54. Fill in the blank: Gathering additional information about data to understand the broader picture is an example of understanding _____.

  • data
  • knowledge
  • problems
  • context ✅

Explanation:
Understanding the context means analyzing the surrounding circumstances and factors influencing the data to interpret it accurately and make informed decisions.

55. Data analysts ask, “Why?” five times in order to get to the root cause of a problem.

  • True ✅
  • False

Explanation:
The five whys methodology typically requires five iterative questions to trace problems to their fundamental causes.

56. Curiosity is an analytical skill that involves which of the following?

  • Seeking out new challenges and experiences ✅
  • Working with facts in an orderly manner
  • Collaborating to solve a problem
  • Using your gut instinct

Explanation:
Curiosity drives data analysts to explore, ask questions, and seek new opportunities to understand and solve problems effectively.

57. A data analyst works for an appliance manufacturer. Last year, the company’s profits were down. Lower profits can be a result of fewer people buying appliances, higher costs to make appliances, or a combination of both. The analyst recognizes that those are big issues to solve, so they break down the problems into smaller pieces to analyze them in an orderly way. Which analytical skill is the data analyst using?

  • Data strategy
  • A technical mindset ✅
  • Understanding context
  • Curiosity

Explanation:
A technical mindset approaches complex problems by dividing them into smaller, more manageable components for analysis.

58. Fill in the blank: Correlation is the observation that there is a _____ between two or more pieces of data.

  • visualization
  • competition
  • choice
  • relationship ✅

Explanation:
Correlation identifies measurable connections or associations between distinct pieces of data.

59. An airport wants to make its luggage-handling process faster and simpler for travelers. A data analyst examines and evaluates how the process works currently in order to achieve the goal of a more efficient process. What methodology do they use?

  • Strategy
  • Gap analysis ✅
  • Data visualization
  • The five whys

Explanation:
Gap analysis evaluates current operational efficiency to identify opportunities for process improvement.

60. Fill in the blank: A data analyst with a technical mindset would break things down into smaller steps or pieces and work with them in an orderly and ______ way.

  • curious
  • clever
  • creative
  • logical ✅

Explanation:
A logical approach ensures data analysts systematically break down complex tasks and handle them effectively.

61. As a recently promoted data scientist one of your responsibilities is the implementation of data strategy. What would this responsibility include?

  • Identifying a relationship between two or more pieces of data
  • Evaluating how a process works currently in order to get where you want to be in the future
  • Breaking things down into smaller steps or pieces and working with them in an orderly and logical way
  • Managing the people, processes, and tools involved ✅

Explanation:
Data strategy implementation focuses on coordinating human capital, operational procedures, and technological assets.

62. Fill in the blank: Being able to identify a relationship between two or more pieces of data describes _____.

  • correlation ✅
  • detail-oriented thinking
  • problem-orientation
  • visualization

Explanation: Correlation identifies relationships and patterns between data points, helping analysts make meaningful connections.

63. Fill in the blank: _______ is a method examining and evaluating how a process works currently in order to get where you want to be in the future.

  • The five whys
  • Strategy
  • Gap analysis ✅
  • Data visualization

Explanation:
Gap analysis helps organizations compare current performance with desired goals, identifying areas for improvement.

Part 3 (Q31–45)

Congratulations! You’ve completed all 63 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 3: Set up your data analytics toolbox
Module 4: Become a fair and impactful data professional
Module 5: Endless career possibilities
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

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