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

Ask Questions to Make Data-Driven Decisions syllabus — full chapter and topic list

Expand any chapter to see its topics and sub-topics. This is the whole examinable outline for Ask Questions to Make Data-Driven Decisions in Google Data Analytics Professional Certificate, not a summary of it.

1. 1

   Effective Questions

   3 topics

   • Problem types in data analytics
   • Crafting SMART questions
   • Quantitative vs qualitative data
2. 2

   Data-Driven Decisions

   3 topics

   • Data versus gut instinct
   • Metrics and measurements
   • Small data and big data
3. 3

   More Spreadsheet Basics

   3 topics

   • Formulas and functions
   • Structured data in spreadsheets
   • Attributes and conditional formatting
4. 4

   Always Remember the Stakeholder

   3 topics

   • Communicating with stakeholders
   • Managing expectations and limitations
   • Building dashboards for decision-makers

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

25 of 50 cards from the Ask Questions to Make Data-Driven Decisions deck — real questions with worked answers.

1. In the Google Data Analytics framework, what are the six common problem types that data analysts solve?

   1) Making predictions, 2) Categorizing things, 3) Spotting something unusual, 4) Identifying themes, 5) Discovering connections, 6) Finding patterns.

2. What is the 'making predictions' problem type in data analytics?

   Using historical data to make an informed decision about how things may be in the future, e.g., using past ad performance data to predict which advertising method will attract the most customers.

3. What is the difference between the 'categorizing things' and 'identifying themes' problem types?

   Categorizing things assigns individual items to labeled groups based on shared features; identifying themes goes a step further by grouping those categories into broader, higher-level concepts or themes (common in qualitative/user research).

4. What is the 'spotting something unusual' problem type? Give an example.

   Identifying data points or events that are outside the norm (anomaly detection). Example: a smartwatch flagging a sudden unusual spike in a user's heart rate.

5. How do the 'discovering connections' and 'finding patterns' problem types differ?

   Discovering connections finds similar challenges faced by different entities and combines data to solve them (e.g., a logistics firm and its delivery partner sharing delay data); finding patterns uses historical data to understand what happened in the past and how likely it is to recur (e.g., analyzing past machine breakdowns to schedule maintenance).

6. What does the acronym SMART stand for in the context of crafting effective questions?

   Specific, Measurable, Action-oriented, Relevant, Time-bound.

7. What makes a question 'Specific' under the SMART framework?

   It addresses the actual problem, is simple and significant, avoids vagueness, and gets the information needed—e.g., asking 'Do employees know they must be at work by 9 a.m.?' rather than 'Are employees satisfied?'

8. What makes a question 'Measurable' under the SMART framework?

   It can be quantified and assessed—it has answers that can be counted, rated, or tracked, such as 'How many customers rated our service 4 stars or higher?'

9. What makes a question 'Action-oriented' and 'Time-bound' under the SMART framework?

   Action-oriented: the answer leads to a change or specific next step. Time-bound: the question specifies a time frame or period to study, which limits the range of data to analyze.

10. Name the five types of questions data analysts should avoid asking.

    Leading questions, closed-ended questions, and vague/ambiguous questions—specifically: leading questions (suggest the answer), closed-ended questions (yield only yes/no), and questions that are too vague, lack context, or make false assumptions.

11. What is a leading question and why should analysts avoid it? Give an example.

    A question that steers respondents toward a particular answer, biasing the data. Example: 'These are the best sandwiches ever, aren't they?' It pressures agreement instead of eliciting honest feedback.

12. Why are closed-ended questions problematic for data collection, and how should they be rephrased?

    They yield only yes/no answers with no insight into why. Rephrase them open-ended: instead of 'Did you enjoy the class?', ask 'What did you like or dislike about the class?'

13. Define quantitative data and qualitative data.

    Quantitative data is specific and objective information that can be measured with numbers—answering how many, how much, or how often. Qualitative data is subjective or descriptive information about qualities that cannot be counted or easily measured, often answering why.

14. Classify each as quantitative or qualitative: (a) number of 5-star movie reviews, (b) written comments explaining why viewers disliked a film, (c) monthly revenue, (d) customer feelings about a brand.

    (a) Quantitative, (b) Qualitative, (c) Quantitative, (d) Qualitative.

15. How do quantitative and qualitative data complement each other in analysis?

    Quantitative data tells you what is happening (measurable facts and trends), while qualitative data adds context and explains why it is happening; together they give a more complete picture for decision-making.

16. What is data-driven decision-making?

    Using facts—data—to guide business strategy: identifying the business need, finding and analyzing relevant data, and basing the decision on the insights rather than on intuition alone.

17. What is a 'gut instinct' in decision-making, and what is its main risk?

    An intuitive understanding or feeling with no clear explanation or supporting evidence. Its risk is bias and error: decisions based on feelings rather than facts can overlook reality and lead to costly mistakes.

18. What is the ideal balance between data and gut instinct in decision-making?

    A blend: use data as the primary foundation for decisions, while experience and intuition help interpret results, fill gaps when data is incomplete, and sanity-check conclusions. Data-inspired decision-making also explores multiple data sources plus experience.

19. What is the difference between data-driven and data-inspired decision-making?

    Data-driven decision-making uses facts from data to guide the decision directly; data-inspired decision-making explores different data sources and combines them with human experience and observation to find common threads before deciding.

20. Define 'metric' in data analytics.

    A metric is a single, quantifiable type of data that can be used for measurement—for example, revenue per month, click-through rate, or customer retention rate. Metrics are often combined into formulas and used to evaluate goals.

21. What is a metric goal?

    A measurable goal set by a company and evaluated using metrics—e.g., 'increase monthly active users by 10% this quarter'—allowing progress to be tracked with data.

22. What is Return on Investment (ROI) and what is its formula?

    ROI measures how well an investment paid off relative to its cost: $$\text{ROI} = \frac{\text{Net Profit}}{\text{Cost of Investment}} = \frac{\text{Revenue} - \text{Cost of Investment}}{\text{Cost of Investment}}$$ (usually expressed as a percentage).

23. An investment cost \$10{,}000 and generated \$12{,}500 in revenue. What is the ROI?

    $\text{ROI} = \frac{12500 - 10000}{10000} = \frac{2500}{10000} = 0.25 = 25\%$.

24. How do metrics turn raw data into useful information? Give an example.

    A metric applies a defined calculation or measurement rule to raw data so it can be compared and evaluated—e.g., raw sales figures become the metric 'revenue per customer' by dividing total revenue by number of customers, enabling goal tracking.

25. What are the key characteristics of small data?

    Small data involves specific metrics over a short, well-defined time period; it is typically gathered and analyzed in spreadsheets, useful for day-to-day decisions, focused on a small number of specific questions, and manageable for a single analyst.

See more Ask Questions to Make Data-Driven Decisions flashcards →

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

Ask Questions to Make Data-Driven Decisions (Google Data Analytics Professional Certificate) FAQ