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

Foundations: Data, Data, Everywhere syllabus — full chapter and topic list

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

1. 1

   Introducing Data Analytics

   3 topics

   • What is data analytics
   • The role of a data analyst
   • Skills and traits of analytical thinkers
2. 2

   All About Analytical Thinking

   3 topics

   • The five key analytical skills
   • Asking effective questions
   • Data analysis and the data life cycle
3. 3

   The Wonderful World of Data

   2 topics

   • The six data analysis phases
   • Structured thinking and frameworks
4. 4

   Set Up Your Toolbox

   3 topics

   • Spreadsheets fundamentals
   • Introduction to query languages and SQL
   • Data visualization tools overview
5. 5

   Endless Career Possibilities

   3 topics

   • Data analytics across industries
   • Building an analyst career
   • Job search foundations

Foundations: Data, Data, Everywhere flashcards for Google Data Analytics Professional Certificate

18 of 50 cards from the Foundations: Data, Data, Everywhere deck — real questions with worked answers.

1. In the Google Data Analytics course, what is the definition of "data"?

   Data is a collection of facts — such as numbers, words, measurements, observations, or descriptions — that can be used to draw conclusions, make predictions, and assist in decision-making.

2. What is data analysis?

   Data analysis is the collection, transformation, and organization of data in order to draw conclusions, make predictions, and drive informed decision-making.

3. What is data analytics, and how is it broader than data analysis?

   Data analytics is the science of data — it encompasses everything about data, including the people who work with it, the processes of managing and using it, and the tools and techniques used, whereas data analysis is just the specific act of analyzing data.

4. What does a data analyst do?

   A data analyst collects, transforms, and organizes data in order to help an organization draw conclusions, make predictions, and drive informed decision-making.

5. How does data science differ from data analytics?

   Data science is about creating new ways of modeling and understanding the unknown by using raw data (building new questions and models), while data analytics focuses on using existing data to answer existing questions and solve current problems.

6. What is data-driven decision-making?

   Data-driven decision-making is the process of using facts (data) to guide business strategy — a problem is defined, relevant data is found and analyzed, and the insights are used to make decisions.

7. What role should gut instinct play in data-driven decision-making?

   Gut instinct alone can lead to bias and poor decisions; the best outcomes come from blending data with business knowledge and (limited, informed) intuition — data should validate or challenge instinct, not be replaced by it.

8. List the five essential (key) analytical skills of a data analyst.

   1) Curiosity, 2) Understanding context, 3) Having a technical mindset, 4) Data design, 5) Data strategy.

9. As one of the five key analytical skills, what is "curiosity"?

   Curiosity is the desire to know more about something — asking questions, seeking new challenges and experiences, and wanting to learn how things work.

10. As an analytical skill, what does "understanding context" mean?

    Context is the condition in which something exists or happens — a setting or background. Understanding context means being able to group things into categories and see how data fits into the bigger picture (e.g., knowing why an outlier appears in a dataset).

11. What is a "technical mindset" in data analytics?

    A technical mindset is the ability to break things down into smaller steps or pieces and work with them in an orderly, logical way — for example, breaking a big process into a checklist of tasks.

12. As an analytical skill, what is "data design"?

    Data design is how you organize information — structuring data so it is easy to access and use, such as organizing a contact list by first name, last name, or company to make lookups efficient.

13. As an analytical skill, what is "data strategy"?

    Data strategy is the management of the people, processes, and tools used in data analysis — ensuring people know how to use the right data, processes keep data clean and accurate, and the correct tools are in place.

14. What is analytical thinking?

    Analytical thinking involves identifying and defining a problem and then solving it by using data in an organized, step-by-step manner.

15. Name the five key aspects of analytical thinking.

    1) Visualization, 2) Strategy, 3) Problem-orientation, 4) Correlation, 5) Big-picture and detail-oriented thinking.

16. In analytical thinking, what is a correlation?

    A correlation is a relationship between two or more pieces of data — when one changes, the other tends to change as well (like the length of your hair and the amount of shampoo you use).

17. Why must analysts remember that "correlation does not equal causation"?

    Two variables can move together (be correlated) without one causing the other — a third factor or coincidence may explain the relationship — so an analyst cannot conclude cause-and-effect from correlation alone.

18. Contrast big-picture thinking and detail-oriented thinking.

    Big-picture thinking means seeing the complete picture (like a finished jigsaw puzzle) to spot opportunities and avoid tunnel vision, while detail-oriented thinking means figuring out all of the specific pieces and parts that make that bigger picture possible (budgets, schedules, individual data points).

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