Google Data Analytics study guide · 2026

The 8 Google Data Analytics domains, explained

8 areas and 34 modules. Google does not publish weights for these areas, so each gets equal space here. Each guide explains what the area covers, how it is assessed and where candidates slip.

Start with Domain 1 See all domains
Questions
No single exam: graded module challenges in each course (28 across Courses 1-7) plus a capstone case study
Time
Self-paced; about 6 months at 10 hours a week (Google estimates about 240 hours)
Passing score
Complete all required graded assignments in the eight courses; per-assignment thresholds are shown inside each course, not published centrally
Outline
Coursera course list for the Google Data Analytics Professional Certificate as of October 2026: eight required courses, with Course 7 now Introduction to Data Analysis Using Python (optional ninth course on AI job search not included)

Exam blueprint

Google does not publish weights, so every area is drawn the same size.

  1. 1
  2. 2
  3. 3
  4. 4
  5. 5
  6. 6
  7. 7
  8. 8
  1. 1 Foundations: Data, Data, Everywhere
  2. 2 Ask Questions to Make Data-Driven Decisions
  3. 3 Prepare Data for Exploration
  4. 4 Process Data from Dirty to Clean
  5. 5 Analyze Data to Answer Questions
  6. 6 Share Data Through the Art of Visualization
  7. 7 Introduction to Data Analysis Using Python
  8. 8 Google Data Analytics Capstone: Complete a Case Study

All 8 domains

34 official modules 200–275 hrs suggested study

  1. Domain 01

    Foundations: Data, Data, Everywhere

    The course that hands you the vocabulary every later course assumes: the data life cycle, the six-phase analysis process, analytical thinking, the core tools, and what fair analysis means in practice.

    Inside this domain

    • Introducing data analytics and analytical thinking
    • The wonderful world of data
    • Set up your data analytics toolbox
    • Become a fair and impactful data professional

    Watch for Share versus act

    Modules
    4
    Questions est.
    4 module challenges of graded questions
    Study time est.
    20–25 hrs
  2. Domain 02

    Ask Questions to Make Data-Driven Decisions

    The ask phase in depth: turning a vague request into SMART questions, naming the problem type, choosing between a report and a dashboard, building clean spreadsheets, and keeping stakeholders aligned.

    Inside this domain

    • Ask effective questions
    • Make data-driven decisions
    • Spreadsheet magic
    • Always remember the stakeholder

    Watch for Specific but closed

    Modules
    4
    Questions est.
    4 module challenges of graded questions
    Study time est.
    20–30 hrs
  3. Domain 03

    Prepare Data for Exploration

    The prepare phase: choosing and judging data before you touch it. Data types and structures, bias and credibility, ethics and privacy, databases and metadata, first SQL in BigQuery, and keeping files organized and secure.

    Inside this domain

    • Data types and structures
    • Data responsibility
    • Database essentials
    • Organize and protect data
    • and 1 more modules

    Watch for Popular means credible

    Modules
    5
    Questions est.
    4 module challenges of graded questions
    Study time est.
    25–35 hrs
  4. Domain 04

    Process Data from Dirty to Clean

    The process phase: checking integrity, deciding whether you have enough data, finding and fixing dirty data in spreadsheets and SQL, then verifying and documenting every change so others can trust the result.

    Inside this domain

    • The importance of integrity
    • Clean data for more accurate insights
    • Data cleaning with SQL
    • Verify and report on cleaning results
    • and 1 more modules

    Watch for Run it anyway

    Modules
    5
    Questions est.
    4 module challenges of graded questions
    Study time est.
    20–30 hrs
  5. Domain 05

    Analyze Data to Answer Questions

    The analyze phase, and the most technical course before Python: organizing, formatting, combining and calculating data in spreadsheets and SQL, from VLOOKUP and pivot tables to JOINs, subqueries and temporary tables.

    Inside this domain

    • Organize data for more effective analysis
    • Format and adjust data
    • Aggregate data for analysis
    • Perform data calculations

    Watch for Default to INNER JOIN

    Modules
    4
    Questions est.
    4 module challenges of graded questions
    Study time est.
    35–50 hrs
  6. Domain 06

    Share Data Through the Art of Visualization

    The share phase: choosing the right chart, designing it so the insight is seen first and by everyone, building it in Tableau, turning it into a data story, and presenting findings with honest caveats.

    Inside this domain

    • Visualize data
    • Create data visualizations with Tableau
    • Craft data stories
    • Develop presentations and slideshows

    Watch for Pie chart for a trend

    Modules
    4
    Questions est.
    4 module challenges of graded questions
    Study time est.
    25–35 hrs
  7. Domain 07

    Introduction to Data Analysis Using Python

    The certificate's programming course, now in Python: Jupyter notebooks, variables and data types, functions and conditionals, loops and string slicing, and the data structures behind NumPy and pandas.

    Inside this domain

    • Hello, Python!
    • Functions and conditional statements
    • Loops and strings
    • Data structures in Python

    Watch for Slice includes the end

    Modules
    4
    Questions est.
    4 module challenges of graded questions
    Study time est.
    40–50 hrs
  8. Domain 08

    Google Data Analytics Capstone: Complete a Case Study

    The capstone: choose a case study, carry it through ask, prepare, process, analyze, share and act, publish it as a portfolio piece, and learn to talk about it with employers and to use AI tools responsibly.

    Inside this domain

    • Learn about capstone basics
    • Optional: Build your portfolio
    • Optional: Use your portfolio
    • Put your certificate to work

    Watch for Charts as the conclusion

    Modules
    4
    Questions est.
    No module-challenge questions
    Study time est.
    15–20 hrs

Suggested study order

No official weights are published, so follow the outline order, across about 200–275 hrs of study.

  1. 1 Foundations: Data, Data, Everywhere 20–25 hrs
  2. 2 Ask Questions to Make Data-Driven Decisions 20–30 hrs
  3. 3 Prepare Data for Exploration 25–35 hrs
  4. 4 Process Data from Dirty to Clean 20–30 hrs
  5. 5 Analyze Data to Answer Questions 35–50 hrs
  6. 6 Share Data Through the Art of Visualization 25–35 hrs
  7. 7 Introduction to Data Analysis Using Python 40–50 hrs
  8. 8 Google Data Analytics Capstone: Complete a Case Study 15–20 hrs

Try one

Practice questionDomain 1: Foundations: Data, Data, Everywhere

A hospital's data team moves patient visit records older than seven years from the active database to long-term storage, where they are kept to meet legal requirements but no longer used in routine reports. Which stage of the data life cycle does this describe?

One trap from every domain

The answer that looks right, and the clue that gives it away.

  • Domain 1

    Share versus act

    Clue Look for whether people are receiving information or changing what they do.

  • Domain 2

    Specific but closed

    Clue An option that starts with 'did', 'is' or 'don't you think' usually invites a closed or leading answer.

  • Domain 3

    Popular means credible

    Clue No link to the organization that collected the data, or a vague attribution.

  • Domain 4

    Run it anyway

    Clue The scenario mentions a single source, region or short time period.

  • Domain 5

    Default to INNER JOIN

    Clue Words such as 'all', 'including those with no' or 'even if'.

  • Domain 6

    Pie chart for a trend

    Clue A time dimension with several periods.

  • Domain 7

    Slice includes the end

    Clue Any [start:stop] expression; count the stop as excluded.

  • Domain 8

    Charts as the conclusion

    Clue The scenario asks what the stakeholder should do next.

Other Data & Analytics certifications

Study guides for exams that overlap with Google Data Analytics.

Practise what you read

Google Data Analytics domains: common questions

How many domains are in the Google Data Analytics exam?

Google Data Analytics has 8 domains with 34 official modules between them: Foundations: Data, Data, Everywhere, Ask Questions to Make Data-Driven Decisions, Prepare Data for Exploration, Process Data from Dirty to Clean, Analyze Data to Answer Questions, Share Data Through the Art of Visualization, Introduction to Data Analysis Using Python and Google Data Analytics Capstone: Complete a Case Study.

What is the Google Data Analytics exam format?

Questions: No single exam: graded module challenges in each course (28 across Courses 1-7) plus a capstone case study. Time allowed: Self-paced; about 6 months at 10 hours a week (Google estimates about 240 hours). Passing score: Complete all required graded assignments in the eight courses; per-assignment thresholds are shown inside each course, not published centrally.

How long should I study for Google Data Analytics?

Our planning estimate is 200–275 hrs across all domains, split by weight. It is an editorial estimate, not an official figure, so adjust it to your experience and to how you score on practice questions.

Where should I start?

Work through the areas in order, since each builds on the one before. Use the checklist on each page to confirm you can do what it asks before moving on.

Which outline is this guide based on?

This guide follows Coursera course list for the Google Data Analytics Professional Certificate as of October 2026: eight required courses, with Course 7 now Introduction to Data Analysis Using Python (optional ninth course on AI job search not included), published by Google. It was last reviewed in October 2026.

Domain names, weights and modules come from Google's official outline (Coursera course list for the Google Data Analytics Professional Certificate as of October 2026: eight required courses, with Course 7 now Introduction to Data Analysis Using Python (optional ninth course on AI job search not included)). Question counts and study hours are editorial planning estimates, not official figures. Last reviewed October 2026.