Google Data Analytics Professional Certificate Prepare Data for Exploration Syllabus

Prepare Data for Exploration syllabus — full chapter and topic list

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

1. 1

   Data Types and Structures

   3 topics

   • Data formats and collection
   • Data models and structures
   • Wide and long data
2. 2

   Bias, Credibility, Privacy, and Access

   3 topics

   • Identifying bias in data
   • Data credibility and ROCCC
   • Data ethics and privacy
3. 3

   Databases: Where Data Lives

   3 topics

   • Relational databases
   • Metadata and its importance
   • Accessing data with SQL
4. 4

   Organizing and Protecting Your Data

   3 topics

   • Sorting and filtering data
   • File naming conventions and organization
   • Securing and backing up data
5. 5

   Engaging in the Data Community

   2 topics

   • Building an online presence
   • Networking and data communities

Prepare Data for Exploration flashcards for Google Data Analytics Professional Certificate

19 of 50 cards from the Prepare Data for Exploration deck — real questions with worked answers.

1. What is the difference between primary data and secondary data?

   Primary data is collected first-hand by a researcher for their own purposes (e.g., interviews or surveys you run yourself). Secondary data was gathered by other people or from other research (e.g., purchased customer profiles or data from local government archives).

2. What is the difference between internal data and external data?

   Internal (first-party) data lives within a company's own systems and is collected by the organization itself; external data lives outside the company and is generated by other organizations, such as government or industry sources.

3. Distinguish between discrete and continuous data.

   Discrete data is counted and has a limited number of possible values (e.g., number of tickets sold, shoe size). Continuous data is measured and can take almost any numeric value, including decimals (e.g., height, temperature, time in a race).

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

   Qualitative data describes qualities or characteristics and cannot be counted or easily measured (words, categories, subjective descriptions). Quantitative data consists of specific, objective numerical measures or counts that can be analyzed mathematically.

5. Compare nominal and ordinal data (two subtypes of qualitative data).

   Nominal data is categorized without a set order (e.g., first-time vs. returning customer). Ordinal data is categorized with a meaningful order or ranking but no fixed numeric distance between categories (e.g., a movie rating of 1 to 5 stars).

6. What is the difference between structured and unstructured data?

   Structured data is organized in a defined format such as rows and columns (spreadsheets, relational databases), making it easy to search and analyze. Unstructured data has no predefined organization, e.g., emails, audio, video, photos, and social media posts.

7. What is a data model?

   A data model is a framework for organizing data elements and how they relate to one another; it keeps data consistent across systems and is developed at three levels: conceptual, logical, and physical.

8. Describe the three levels of data modeling.

   Conceptual: high-level view of concepts and rules, no technical detail. Logical: details of entities, attributes, and relationships independent of a specific database technology. Physical: how the data is actually implemented in a specific database, including tables and columns.

9. What is the difference between a data model and a data structure?

   A data model is the blueprint describing how data is organized and related; a data structure is the actual format used to organize and store the data, such as an array, table, tree, or file.

10. What is wide data?

    Wide data is a dataset in which every data subject occupies a single row and each variable or measurement has its own separate column, making it easy to compare different variables for the same subject side by side.

11. What is long data?

    Long data is a dataset in which each row holds one observation (one variable/measurement at one point in time) for a subject, so a single subject can span multiple rows; it is useful for storing many variables and time periods compactly.

12. When would you prefer wide data over long data, and vice versa?

    Prefer wide data when comparing different variables across subjects or creating tables and charts that compare columns. Prefer long data when storing many variables per subject, tracking measurements over time, or performing advanced statistical analysis and plotting by groups.

13. What is sampling bias?

    Sampling bias occurs when a sample is not representative of the population as a whole—some members of the population are systematically more or less likely to be included—leading to skewed results (e.g., surveying only morning commuters about city transit).

14. What is observer bias (experimenter bias)?

    Observer bias is the tendency for different people to observe or record the same thing differently, letting the observer's expectations influence the measurements (e.g., two scientists reading the same thermometer and recording slightly different values).

15. What is interpretation bias?

    Interpretation bias is the tendency to interpret ambiguous situations or data in a way that fits one's own outlook—reading the same neutral information as positive or negative depending on personal perspective.

16. What is confirmation bias?

    Confirmation bias is the tendency to search for, favor, and interpret information in a way that confirms pre-existing beliefs while ignoring evidence that contradicts them.

17. What is unbiased (random) sampling and why does it matter?

    Unbiased sampling means every member of the target population has an equal chance of being selected for the sample. It matters because a random, representative sample lets results generalize accurately to the whole population.

18. What does the acronym ROCCC stand for when evaluating data sources?

    Reliable, Original, Comprehensive, Current, and Cited — a checklist for judging whether a data source is good (trustworthy) or bad.

19. In ROCCC, what do 'Reliable' and 'Original' mean?

    Reliable: the data is accurate, complete, and unbiased—it has been vetted and proven fit for use. Original: you can validate the data with the original, first-party source rather than relying only on second- or third-hand copies.

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