Google Data Analytics Professional Certificate Foundations: Data, Data, Everywhere Flashcards

24 sample cards from the Foundations: Data, Data, Everywhere deck

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

1. What happens during the "Plan" stage of the data life cycle?

   Before any data is collected, the organization decides what data it needs, how the data will be managed and handled throughout its life cycle, who will be responsible for it, and what the optimal outcomes are.

2. What happens during the "Capture" stage of the data life cycle?

   Data is collected or brought in from various sources — for example, from outside resources/databases or from a company's own internal documents and systems.

3. What happens during the "Manage" stage of the data life cycle?

   The organization decides how and where the data is stored, the tools used to keep it safe and secure, and the actions taken to maintain it properly — this stage is important to the process of cleaning data.

4. In the data life cycle, how does "Archive" differ from "Destroy"?

   Archiving means storing data in a place where it is still available but no longer used actively; destroying means permanently erasing the data (e.g., shredding paper files or using secure data-erasure software) to protect privacy and comply with regulations.

5. List the six phases of the data analysis process, in order.

   1) Ask, 2) Prepare, 3) Process, 4) Analyze, 5) Share, 6) Act.

6. What happens during the "Ask" phase of data analysis?

   The analyst defines the problem to be solved and fully understands stakeholder expectations — asking effective questions, understanding the context, and looking beyond surface symptoms to the root problem.

7. What happens during the "Prepare" phase of data analysis?

   Data analysts collect and store the data they will use for the upcoming analysis process — identifying what data is needed and gathering it from appropriate sources.

8. What happens during the "Process" phase of data analysis?

   The analyst "cleans" the data — finding and eliminating errors, inaccuracies, duplicates, and inconsistencies that could get in the way of results, and ensuring the data is stored in the right form and place.

9. What happens during the "Analyze" phase of data analysis?

   Analysts use tools (spreadsheets, SQL, etc.) to transform and organize the data so they can draw conclusions, make predictions, and identify insights that drive informed decisions.

10. What happens during the "Share" phase of data analysis?

    The analyst interprets results and communicates them to stakeholders to help them make data-driven decisions — data visualization is a key tool here for making findings clear and compelling.

11. What happens during the "Act" phase of data analysis?

    The business puts the insights to work — taking action and implementing solutions based on everything learned from the analysis to solve the original problem.

12. What is structured thinking?

    Structured thinking is the process of recognizing the current problem or situation, organizing available information, revealing gaps and opportunities, and identifying the options — it starts with a clear-cut problem and clear goals.

13. What is a problem domain?

    The problem domain is the specific area of analysis that encompasses every activity affecting or affected by the problem being solved — defining it keeps the analysis focused and in scope.

14. What is a scope of work (SOW) and what four key elements does it typically include?

    A scope of work is an agreed-upon outline of the work to be performed on a project. It typically includes deliverables, timeline, milestones, and reports.

15. Name the six common problem types that data analysts work with.

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

16. How does "categorizing things" differ from "identifying themes" as problem types?

    Categorizing assigns individual items to defined groups based on shared characteristics, while identifying themes goes a step further by taking those groups/categories and grouping them into broader concepts or themes (common in user experience research).

17. In a spreadsheet dataset, what is an attribute and what is an observation?

    An attribute is a characteristic or quality of the data — typically a column header (e.g., Name, Price, Date). An observation is all of the attributes for a single instance of the data — typically one row.

18. 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 cell values (e.g., =A2+A3), while a function is a preset command that automatically performs a specific process (e.g., =SUM(A2:A10) adds the range without typing each addition).

19. What is a query, and what does SQL stand for?

    A query is a request for data or information from a database. SQL stands for Structured Query Language — a language that lets data analysts communicate with and retrieve data from databases.

20. What are the three basic clauses of a simple SQL query, and what does each do?

    SELECT chooses the columns (fields) you want; FROM specifies the table the data comes from; WHERE filters the rows to only those meeting a condition. By convention, SQL keywords are written in capital letters for readability.

21. What is data visualization, and what is Tableau?

    Data visualization is the graphical representation of data (charts, graphs, maps) that makes findings easier to understand and share. Tableau is a popular visualization tool that lets analysts drag and drop data to create interactive charts, graphs, and dashboards.

22. What is a business task in data analytics?

    A business task is the question or problem that data analysis answers for a business — it defines why the analysis is being done and what decision it will support.

23. Give examples of how data analytics is used across different industries.

    Nearly every industry uses data analysts: healthcare (improving patient care and staffing), retail/e-commerce (understanding customer purchasing patterns), entertainment/streaming (content recommendations), finance (fraud detection and risk), marketing (campaign performance), and manufacturing/logistics (efficiency and supply chains).

24. In a job search, what are transferable skills and why do they matter for aspiring data analysts?

    Transferable skills are skills and qualities developed in past jobs or experiences that can be applied to a new role — for a data analyst, examples include communication, problem-solving, attention to detail, and time management; highlighting them on a resume shows readiness even without prior analytics job titles.

What this deck covers

Foundations: Data, Data, Everywhere flashcards FAQ