Google Data Analytics Professional Certificate Prepare Data for Exploration Flashcards

24 sample cards from the Prepare Data for Exploration deck

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

1. What is a primary key in a relational database?

   A primary key is a column (or set of columns) that uniquely identifies each record in a table; its values must be unique and cannot be null or blank, and a table can have only one primary key.

2. What is a foreign key in a relational database?

   A foreign key is a field in one table that refers to the primary key of another table, creating the relationship between the two tables. A table can have multiple foreign keys, and foreign key values may repeat.

3. What is normalization in the context of databases?

   Normalization is the process of organizing data in a relational database—splitting it into related tables and removing redundancy—to increase data integrity, eliminate duplication, and reduce complexity.

4. What is metadata, and how does it differ from data?

   Metadata is 'data about data'—it describes a data item (who created it, when, file type, structure, etc.) rather than containing the content itself. For example, a photo's metadata includes the timestamp, location, and camera settings, not the image content.

5. Name and define the three common types of metadata.

   Descriptive: identifies and describes a piece of data (e.g., a book's ISBN, title, author). Structural: indicates how data is organized and related to other data (e.g., how pages form chapters). Administrative: indicates technical origin details such as file type, and when and how the data was created.

6. What is a metadata repository and why is it useful?

   A metadata repository is a database specifically created to store metadata. It describes where the metadata came from, its structure, and how it connects across sources, ensuring metadata is accessible, standardized, and quickly retrievable so analysts can confirm data consistency and quality.

7. What is data governance and how does metadata relate to it?

   Data governance is the process of ensuring the formal management of a company's data assets—covering availability, integrity, security, privacy, and compliance. Metadata specialists and metadata standards make governance possible by keeping data consistent, well-documented, and trustworthy across the organization.

8. What is SQL and what is it used for?

   SQL (Structured Query Language) is the standard language used to communicate with databases—to retrieve (query), insert, update, and delete data. Analysts use it because it can handle huge datasets that spreadsheets cannot.

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

   SELECT chooses the columns to retrieve; FROM specifies the table(s) the data comes from; WHERE filters rows to only those meeting a stated condition.

10. In SQL, how do you retrieve every column from a table named 'customers'?

    Use the asterisk wildcard: SELECT * FROM customers; — the * means 'all columns'.

11. How do you write comments and column aliases in SQL, and why use them?

    Comments: use -- for a single line or /* ... */ for blocks; they document the query for humans and are ignored by the database. Aliases: use AS to give a table or column a temporary, more readable name (e.g., SELECT last_name AS surname), which only lasts for that query.

12. In SQL, what does the LIKE operator do, and what does the % wildcard mean?

    LIKE is used in a WHERE clause to match string patterns. The % wildcard matches any sequence of characters; e.g., WHERE state LIKE 'Ca%' returns rows where state begins with 'Ca' (such as California).

13. What does the SQL ORDER BY clause do, and how do you control sort direction?

    ORDER BY sorts the query results by one or more columns. Ascending order is the default (or use ASC); add DESC for descending order, e.g., ORDER BY revenue DESC. It is written after the WHERE clause when both are used.

14. In spreadsheets, what is the difference between 'Sort Sheet' and 'Sort Range'?

    Sort Sheet sorts all of the data in the spreadsheet by one column, keeping every row's data together across columns. Sort Range sorts only a selected group of cells, leaving everything else on the sheet unchanged.

15. What is the difference between sorting and filtering data?

    Sorting arranges data into a meaningful order (e.g., alphabetical or numerical, ascending or descending) while keeping all rows visible. Filtering shows only the rows that meet specified criteria and hides the rest.

16. What are the key best practices for file naming conventions?

    Agree on conventions with your team up front and align to existing project standards; keep names short and meaningful; include the project name, creation date, and version; format dates consistently as yyyymmdd; use hyphens, underscores, or capital letters instead of spaces or special characters (e.g., SalesReport_20231125_v02).

17. How should file versions and folder organization be managed in a data project?

    Indicate versions in file names (e.g., v02) and lead revision numbers with a zero so versions can reach double digits (v02 ... v10). Organize files into hierarchical folders—broad topics first, then more specific subfolders—and separate completed files from in-progress ones, archiving old files to reduce clutter.

18. What is the difference between encryption and tokenization as data security measures?

    Encryption uses a unique algorithm and key to scramble data into unreadable ciphertext; only authorized users with the key can decode it. Tokenization replaces sensitive data elements with a randomly generated token; the original data is stored securely elsewhere, and the token is meaningless if stolen.

19. Why are backups and version control important for securing data, and what tools support them?

    Backups protect against loss, corruption, and attacks by keeping copies of data, and version control tracks changes so earlier versions can be restored and edits attributed. Tools include cloud storage with revision history (Google Sheets version history, BigQuery table history) and access controls like sharing permissions that restrict who can view or edit.

20. Why should a data analyst build an online presence, and what platform is most emphasized for it?

    An online presence lets analysts showcase skills and projects, build a personal brand, connect with recruiters and peers, and stay current with industry trends. LinkedIn is the most emphasized platform—with a strong photo, headline, summary, and work samples—alongside GitHub, Kaggle, Medium, and X for sharing work and insights.

21. What is a professional network, and what are the two main ways to build one as a data analyst?

    A professional network is the group of people you connect with for career advice, referrals, mentorship, and knowledge sharing. Build it both online (LinkedIn, data communities, online forums, following industry leaders) and in person (meetups, conferences, hackathons, and local data analytics associations).

22. What is a mentor in a professional context, and what should you keep in mind when seeking one?

    A mentor is a professional who shares knowledge, skills, and experience to help you grow in your career. Be specific about the guidance you need, respect their time, remember mentors can come from anywhere (they do not need a formal title), and you can have multiple mentors for different goals.

23. Why is understanding how data is collected and formatted important in the 'prepare' phase of data analysis?

    The data collection method and format determine the data's reliability, relevance, and how it can be analyzed: analysts must consider how the data was gathered (first-, second-, or third-party), whether the sample represents the population, the time frame, and the amount of data, because flawed collection leads to misleading conclusions.

24. What is the difference between first-party, second-party, and third-party data?

    First-party data is collected by an individual or group directly from their own customers using their own resources. Second-party data is collected directly by another group from its audience and then sold. Third-party data is sold by a provider that did not collect it from its own audience directly, so its credibility must be checked carefully.

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