Module 2: Make data-driven decisions

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In this post, I provide accurate answers and detailed explanations for Module 2: Make data-driven decisions of Course 2: Ask Questions to Make Data-Driven Decisions – Google Data Analytics Professional Certificate.

Whether you’re preparing for quizzes or brushing up on your knowledge, these insights will help you master the concepts effectively. Let’s dive into the correct answers and detailed explanations for each question.

Here, we’ll walk through questions 1 to 15 with detailed explanations to support your learning.

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

Test your knowledge on the power of data

Practice Quiz

1. What is the difference between qualitative and quantitative data?

  • Qualitative data can be used to measure qualities and characteristics. Quantitative data can be used to measure numerical facts. ✅
  • Qualitative data is specific. Quantitative data is subjective.
  • Qualitative data is about the quality of a product or service. Quantitative data is about how much of that product or service is available.
  • Qualitative data describes the kind of data being analyzed. Quantitative data describes how much data is being analyzed.

Explanation:

  • Qualitative data focuses on non-numerical aspects like qualities, characteristics, or descriptions.
  • Quantitative data involves numerical information that can be measured or counted.

2. Fill in the blank: Data-inspired decision-making can discover _____ when exploring different data sources.

  • what the data has in common ✅
  • which experts can give advice
  • if a decision was properly made
  • where the largest amount of data is

Explanation:
Data-inspired decision-making involves identifying patterns, trends, and commonalities across various data sources to support effective decisions.

3. Which of the following examples describes using data to achieve business results? Select all that apply.

  • A movie theater tracks the number of weekend movie goers for three months.
  • A video streaming service analyzes user preferences to customize movie recommendations. ✅
  • A grocery chain collects data on sale items and pricing from each store.
  • A large retailer performs data analysis on product purchases to create better promotions. ✅

Explanation:

  • These examples show actionable data analysis being applied directly to achieve business outcomes, such as improving recommendations and optimizing promotions.

4. If someone is subjectively describing their feelings or emotions, it is qualitative data.

  • True ✅
  • False

Explanation:
Subjective descriptions of feelings or emotions represent qualitative data, as they capture non-numerical and descriptive information.

Test your knowledge on following the evidence

Practice Quiz

5. Fill in the blank: Pivot tables in data processing tools are used to _____ data.

  • summarize ✅
  • validate
  • clean
  • populate

Explanation:
Pivot tables are a tool used in data analysis to summarize, organize, and analyze data, making it easier to extract meaningful insights from large datasets.

6. In data analytics, how are dashboards different from reports?

  • Dashboards contain static data. Reports contain data that is constantly changing.
  • Dashboards are used to share updates with stakeholders only periodically. Reports give stakeholders continuous access to data.
  • Dashboards monitor live, incoming data from multiple datasets and organize the information into one central location. Reports are static collections of data. ✅
  • Dashboards provide a high-level presentation of historical data. Reports provide a more detailed presentation of live, interactive data.

Explanation:
Dashboards are interactive and display live, real-time data from various sources, while reports are typically static snapshots of data used for periodic updates or analysis.

7. Describe the difference between data and metrics.

  • Data is quantifiable and used for measurement. Metrics are unorganized collections of facts.
  • Data can be used for measurement. Metrics cannot be used for measurement.
  • Data is a collection of facts. Metrics are quantifiable data types used for measurement. ✅
  • Data is quantifiable. Metrics are unquantifiable.

Explanation:
Data is raw, unprocessed information. Metrics are derived from data and represent specific, measurable values, often used to track performance.

8. Return on Investment (ROI) uses which of the following metrics in its definition?

  • Supply and demand
  • Sales and margin
  • Inventory and units
  • Profit and investment ✅

Explanation:
ROI measures the efficiency of an investment by comparing the profit generated to the initial investment cost. The formula is typically:
ROI=ProfitInvestment×100\text{ROI} = \frac{\text{Profit}}{\text{Investment}} \times 100

Test your knowledge on connecting the data dots

Practice Quiz

9. Describe the key differences between small data and big data. Select all that apply.

  • Small data involves datasets concerned with a small number of specific metrics. Big data involves datasets that are larger and less specific. ✅
  • Small data is typically stored in a database. Big data is typically stored in a spreadsheet.
  • Small data focuses on short, well-defined time periods. Big data focuses on change over a long period of time. ✅
  • Small data is effective for analyzing day-to-day decisions. Big data is effective for analyzing more substantial decisions. ✅

Explanation:

  • Small data is focused, specific, and typically used for short-term analysis, such as operational or routine decisions.
  • Big data refers to large, complex datasets that often require advanced tools and techniques for analysis. It is used for long-term trends or substantial decision-making.
  • Storage methods (database vs. spreadsheet) are not a reliable distinction, as both can store small or large datasets depending on their structure.

10. Which of the following is an example of small data?

  • The trade deficit between two countries over a hundred years
  • The number of steps someone walks in a day ✅
  • The bed occupancy rate for a hospital for the past decade
  • The total absences of all high school students

Explanation:

  • This is a small, specific metric tied to an individual, making it an example of small data.
  • The other options involve broader, long-term datasets, such as historical trade deficits, hospital occupancy rates, or school absences, which fall under big data.

11. The amount of exercise time it takes for a single person to burn a minimum of 400 calories is a problem that requires big data.

  • True
  • False ✅

Explanation:

  • This is a small, specific problem and can be addressed using small datasets or even individual metrics, making it a small data problem.
  • Big data typically involves larger-scale analysis across many individuals or datasets, which is unnecessary here.

*Weekly challenge 2*

Graded Quiz

12. Which of the following statements describes an algorithm?

  • A process or set of rules to be followed for a specific task ✅
  • A method for recognizing the current problem or situation and identifying the options
  • A tool that enables data analysts to spot something unusual
  • A technique for focusing on a single topic or a few closely related ideas

Explanation: Algorithms are step-by-step instructions or rules designed to solve a problem or complete a task efficiently.

13. Fill in the blank: If a data analyst is measuring qualities and characteristics, they are considering _____ data.

  • quantitative
  • unbiased
  • cleaned
  • qualitative ✅

Explanation:
Qualitative data captures non-numeric, descriptive attributes such as qualities or characteristics.

14. In data analytics, reports use live, incoming data from multiple datasets; dashboards use static collections of data.

  • True
  • False ✅

15. A pivot table is a data-summarization tool used in data processing. Which of the following tasks can pivot tables perform? Select all that apply.

  • Group data
  • Clean data
  • Calculate totals from data
  • Reorganize data

Explanation:
Pivot tables allow users to summarize, reorganize, and group data for deeper insights.

That’s it for Part 1! Continue your learning journey with the next set of answers.

Next Part: Module 2: Make Data-Driven Decisions Answers (Part 2: Q16–30)

Part 2: Q16–30

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