Module 1: Introducing Data Analytics and Analytical Thinking Answers (Part 3: Q31–45)
This is Part 3 of the Module 1 quiz answers for “Introducing Data Analytics and Analytical Thinking” from the Google Data Analytics Professional Certificate on Coursera.
Here, we’ll walk through questions 31 to 45 with detailed explanations to support your learning.
To find answers to the remaining questions, check out the full module breakdown below:
31. Consulting with experts in the marketing department about your marketing analysis is an example of what process?
- Data analytics
- Data-driven decision-making ✅
- Data management
- Data science
Explanation:
Data-driven decision-making involves using data insights alongside expert opinions to guide business actions. Consulting subject-matter experts (SMEs) ensures that data analysis aligns with real-world business knowledge and validates conclusions.
Why not the others?
- Data analytics focuses only on analyzing data, not collaboration.
- Data management deals with organizing/storing data, not decision-making.
- Data science uses advanced modeling but doesn’t emphasize consulting SMEs.
32. You have recently subscribed to an online data analytics magazine. You really enjoyed an article and want to share it in the discussion forum. Which of the following would be appropriate in a post? Select all that apply.
- Checking your post for typos or grammatical errors. ✅
- Including an advertisement for how to subscribe to the data analytics magazine.
- Giving credit to the original author. ✅
- Including your own thoughts about the article. ✅
Explanation:
- Checking for errors ensures professionalism.
- Giving credit avoids plagiarism and respects intellectual property.
- Adding your perspective encourages meaningful discussion.
- Why not advertise the magazine? Self-promotion is often against forum guidelines.
33. Which of the following could be elements of a data ecosystem? Select all that apply
- Sharing data ✅
- Producing data ✅
- Gaining insights
- Managing data ✅
Explanation:
A data ecosystem includes processes to create, store, share, and manage data.
Why not “gaining insights”? Insights are the result of analysis, not a core ecosystem component.
34. If you are using data-driven decision-making, what action steps would you take? Select all that apply.
- Surveying customers about results, conclusions, and recommendations
- Gathering and analyzing data ✅
- Sharing your results with subject matter experts ✅
- Drawing conclusions from your analysis ✅
Explanation:
Data-driven decisions rely on:
- Data collection/analysis (facts).
- Expert review (validation).
- Conclusions (actionable insights).
Why not survey customers? Surveys are a data collection method, not a decision-making step here.
35. What do subject-matter experts do to support data-driven decision-making? Select all that apply.
- Collect, transform, and organize data
- Offer insights into the business problem ✅
- Review the results of data analysis and identify any inconsistencies ✅
- Validate the choices made as a result of the data insights ✅
Explanation:
SMEs provide domain knowledge to:
- Clarify business problems.
- Spot errors in analysis.
- Ensure decisions are practical.
Why not “collect/transform data”? That’s the analyst’s job.
36. Fill in the blank: When following data-driven decision-making, a data analyst will consult with ______ .
- subject matter experts ✅
- stakeholders
- managers
- customers
Explanation:
SMEs bridge the gap between data and business context. Stakeholders (e.g., managers) make decisions but may lack deep expertise.
37. What is the purpose of data analysis? Select all that apply.
- To drive informed decision-making ✅
- To create models of data
- To draw conclusions
- To make predictions ✅
Explanation:
Analysis turns raw data into actionable insights (e.g., trends, forecasts).
Why not “create models”? That’s data science, not core analysis.
38. A data analyst is someone who does what?
- Designs new products
- Creates new questions using data ✅
- Solves engineering problems
- Finds answers to existing questions by creating insights from data sources
Explanation:
Data scientists use advanced analytical techniques to derive insights from data and solve complex business problems. This role focuses on interpreting data rather than creating new products or solving unrelated engineering challenges.
39. What tactics can a data analyst use to effectively blend gut instinct with facts? Select all that apply.
- Use their knowledge of how their company works to better understand a business need. ✅
- Focus on intuition to choose which data to collect and how to analyze it.
- Ask how to define success for a project, but rely most heavily on their own personal perspective.
- Apply their unique past experiences to their current work, while keeping in mind the story the data is telling. ✅
Explanation:
Effective analysts balance:
- Business context (how the company operates).
- Data evidence (what the numbers show).
Why not “rely on intuition to choose data”? This introduces bias; data selection should be objective.
40. To get the most out of data-driven decision-making, it’s important to include insights from people very familiar with the business problem. What are these people called?
- Subject-matter experts ✅
- Customers
- Stakeholders
- Competitors
Explanation:
SMEs have deep expertise in specific areas (e.g., marketing, manufacturing). They:
- Clarify problems.
- Validate data relevance.
- Ensure solutions are practical.
41. A music streaming service is looking to increase user engagement on their platform. The CEO decides to leverage the company's user data and tasks the data analysts with uncovering unknown trends and characteristics of the companies user base. This strategy is known as what?
- Data analytics decision-making
- Data science decision-making
- Data management decision-making
- Data-driven decision-making ✅
Explanation:
The CEO is using user data + analyst findings to guide strategy (increasing engagement). This defines data-driven decisions.
42. You read an interesting article in a magazine and want to share it in the discussion forum. What should you do when posting? Select all that apply.
- Check your post for typos or grammatical errors ✅
- Include your email address for people to send questions or comments
- Make sure the article is relevant to data analytics ✅
- Take credit for creating the article
Explanation:
Forum posts should be:
- Professional (error-free).
- On-topic (related to analytics).
Why not include email? Privacy risks; use forum messaging instead.
43. A data scientist is someone who does what?
- Creates new questions using data ✅
- Finds answers to existing questions by creating insights from data sources
- Solves engineering problems
- Solves engineering problems
Explanation:
Data scientists explore data to discover new problems (e.g., “Can we predict churn?”). Analysts solve existing questions.
44. Data analysts act as detectives to uncover clues within the data. Like a detective, a data analyst may use their _______ to solve business problems.
- personal opinion
- rational thought
- gut instinct ✅
- awareness
Explanation:
Like detectives, analysts combine:
- Data evidence (clues).
- Intuition (experience-based hunches).
45. In data-driven decision-making, a data analyst would share their results with subject matter experts and draw conclusions from their analysis. What else would a data analyst do in data-driven decision-making?
- Identification of trends
- Determining the stakeholders.
- Survey customers about results, conclusions, and recommendations
- Gather and analyze data ✅
Explanation:
Data analysts identify trends, gather data, and collaborate with subject-matter experts to ensure that their decisions are based on facts and insights rather than assumptions or opinions.
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