Google Data Analytics Professional Certificate Ask Questions to Make Data-Driven Decisions Flashcards

24 sample cards from the Ask Questions to Make Data-Driven Decisions deck

Sampled from the end of the deck, so these are different cards from the ones shown on the syllabus page.

1. What are the three (or four) V's of big data?

   Volume (the amount of data), Variety (the different kinds of data), and Velocity (how fast the data is processed). A fourth V is Veracity—the quality and reliability of the data.

2. Name two challenges and two benefits of working with big data.

   Challenges: overwhelming volume can obscure which data is relevant, and unstructured/varied data takes significant time and resources to clean and analyze. Benefits: more complete, information-rich picture and better identification of trends and patterns for strategic decisions.

3. In spreadsheets, what is the difference between a formula and a function?

   A formula is a set of instructions the user writes to perform a calculation using values in cells (e.g., =A2+B2). A function is a preset command that automatically performs a specific process or calculation (e.g., =SUM(A2:A10)).

4. What character must every spreadsheet formula or function begin with, and what operators are used for the four basic arithmetic operations?

   Every formula begins with an equal sign (=). Operators: + for addition, - for subtraction, * for multiplication ($\times$), and / for division ($\div$).

5. What do the spreadsheet functions SUM, AVERAGE, COUNT, MIN, and MAX do?

   SUM adds the values in a range; AVERAGE computes their arithmetic mean; COUNT counts how many cells in a range contain numeric values; MIN returns the smallest value; MAX returns the largest value.

6. In the spreadsheet function =SUM(A2:A10), what does the colon notation A2:A10 mean, and what is a cell reference?

   A2:A10 is a range—every cell from A2 through A10 inclusive. A cell reference is a cell's address (column letter + row number, e.g., B5) used in formulas so calculations update automatically when the referenced data changes.

7. A store sells 45 units in week 1, 52 in week 2, and 61 in week 3. Write the average weekly sales as a calculation.

   $\text{Average} = \frac{45 + 52 + 61}{3} = \frac{158}{3} \approx 52.7$ units per week.

8. What is structured data, and how is it organized in a spreadsheet?

   Structured data is data organized in a defined format such as rows and columns. In a spreadsheet, each row is a record (a single observation) and each column is an attribute (a characteristic or feature of the data), with a header labeling each column.

9. Define 'attribute' and 'observation' in a structured dataset.

   An attribute is a characteristic or quality of the data, usually shown as a column header (e.g., Name, Price, Date). An observation is all the attributes for one instance of the data, shown as a single row (also called a record).

10. What is conditional formatting in spreadsheets and why is it useful to analysts?

    Conditional formatting is a tool that automatically changes the appearance of cells (color, style) when their values meet specified conditions—e.g., highlighting blank cells or values above a threshold—making key values, errors, and patterns instantly visible.

11. Give an example of using conditional formatting for data cleaning or monitoring.

    Set a rule to highlight all empty cells in red to spot missing data, or highlight sales values below a target (e.g., values $< 1000$) in yellow so underperforming entries stand out for review.

12. Who counts as a stakeholder in a data project, and name three common stakeholder groups an analyst works with.

    Stakeholders are people who invest time, interest, and resources into a project and are affected by its outcome. Common groups: the executive team (strategic direction), the customer-facing team / project managers (day-to-day coordination), and the data science/analytics team.

13. What is the typical role of the executive team versus the project manager as stakeholders?

    The executive team sets the strategic direction, goals, and priorities of the company and needs high-level insights quickly; the project manager oversees day-to-day project progress, timelines, and budget, and needs regular updates on status and blockers.

14. What key questions should an analyst ask before communicating with any stakeholder?

    Who is my audience? What do they already know? What do they need to know? And how can I best communicate what they need to know? Tailoring the message to the answers builds trust and clarity.

15. List four best practices for clear communication with stakeholders.

    1) Tailor content and level of detail to the audience; 2) stay focused on the business task and expected outcomes; 3) communicate in a timely way and respond promptly (set expectations if you need time); 4) use professional, fact-based, jargon-appropriate language and confirm understanding.

16. Why is it important to focus on the stakeholder's business objective rather than just their literal data request?

    Understanding the underlying objective lets the analyst reframe or improve the question, choose the right data, and deliver insights that actually solve the problem—rather than producing technically correct output that misses the real need.

17. What does 'managing expectations' mean in a data project, and name three techniques for doing it.

    Aligning stakeholders' beliefs about scope, timeline, and results with what is realistically achievable. Techniques: communicate early and often with status updates; be clear about limitations of the data and analysis; set realistic timelines (break work into milestones and build in buffer time); say 'no' or renegotiate when requests are not feasible.

18. What should an analyst do when they discover the available data cannot fully answer a stakeholder's question?

    Be transparent: explain the data's limitations, what can and cannot be concluded, propose alternatives (different data sources, a narrower question, or proxy metrics), and reset expectations before proceeding rather than delivering misleading results.

19. Why is it valuable to take time to reframe or restate a problem before starting analysis?

    Reframing (putting the problem in your own words or from a new perspective) confirms you understand the real objective, exposes hidden assumptions, and prevents wasted effort answering the wrong question—analysts should balance speed with accuracy.

20. What is a dashboard in data analytics?

    A tool that monitors live, incoming data—organizing and visualizing multiple datasets and key metrics in one central, interactive location so stakeholders can track conditions and make decisions at a glance.

21. How does a dashboard differ from a static report?

    A report is a static, point-in-time snapshot of cleaned and sorted data; a dashboard is dynamic and interactive, continuously updating from live data sources so users can monitor changing information and drill into it themselves.

22. Name three benefits and two potential challenges of dashboards for decision-makers.

    Benefits: centralize multiple datasets in one place, provide visual at-a-glance tracking of metrics and progress toward goals, and update automatically (live monitoring). Challenges: they take time to design and can be less efficient if underused, and the underlying data pipeline must be maintained and kept accurate.

23. Outline the basic steps for creating an effective dashboard.

    1) Identify the stakeholders and the questions/metrics the dashboard must answer; 2) design the layout with the audience in mind (most important info first, clear and uncluttered); 3) connect and pull in the data, create the visualizations; 4) share, gather feedback, and iterate/maintain.

24. Give an example of a strategic vs. an operational dashboard.

    A strategic dashboard tracks long-term goals and company-wide KPIs (e.g., quarterly revenue vs. annual target for executives); an operational dashboard tracks shorter time frames and day-to-day performance (e.g., live customer-support ticket volume and response times). A third type, analytical, contains detailed datasets for analysts to explore.

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Ask Questions to Make Data-Driven Decisions flashcards FAQ