Google Data Analytics Certificate Study Guide 2026: Syllabus, Exam Topics & Study Plan -Edureify
📋 2026 Edition  ·  Updated September 2026

Google Data Analytics Certificate Study Guide 2026

Complete exam coverage for the Google Data Analytics Certificate: syllabus, domains, key topics, study plan and practical exam preparation strategy.

Multiple quizzes across 8 courses
Questions
Assessment per course (no single timed exam) min
Duration
80% per graded assessment
Passing score
8
Domains
Unlock My Study Plan → Take Readiness Diagnostic
95%
First-attempt pass rate
47K+
Candidates prepared
4.9★
Average rating
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Overall readiness (locked)
Foundations of Data Analytics
-
Ask Questions to Make Data-Driven Decisions
-
Prepare Data for Exploration
-
Process Data from Dirty to Clean
-
Analyse Data to Answer Questions
-
Share Data Through the Art of Visualisation
-
Data Analysis with R Programming
-
Google Data Analytics Capstone
-
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Exam at a Glance

Google Data Analytics Certificate Exam Overview

Key facts about the Google Data Analytics Certificate exam structure, format and scoring.

🆔
google-data-analytics
Exam code
📝
Multiple quizzes across 8 courses questions
Total questions
⏱
Assessment per course (no single timed exam) minutes
Duration
🎯
80% per graded assessment
Passing score
📋
8 domains
Exam domains
🏆
Certification
Credential type
ℹ️
Scoring method: . The exam may include unscored pilot questions - treat every question seriously.
Focus Areas

What should you study for the Google Data Analytics Certificate exam?

Start with the domains that make up the Google Data Analytics Certificate exam. Use the detailed syllabus below to work through the individual topics.

⚠️
Common mistake: Candidates often memorise terminology but struggle with scenario-based questions. Focus on when to use what, not just what exists.
🔐
Foundations of Data Analytics (13%)
Course 1: Introduces data analytics concepts, the data analysis process, the role of a data analyst, and core tools.
🏗
Ask Questions to Make Data-Driven Decisions (13%)
Course 2: Structured thinking, problem framing, effective stakeholder communication, and spreadsheet basics.
⚡
Prepare Data for Exploration (13%)
Course 3: Data types, data collection, bias, credibility, data organisation, and introduction to SQL.
💰
Process Data from Dirty to Clean (13%)
Course 4: Data cleaning techniques using spreadsheets and SQL, verifying and documenting cleaning steps.
🔄
Analyse Data to Answer Questions (13%)
Course 5: Advanced SQL, spreadsheet analysis functions, and performing aggregations and calculations.
📊
Share Data Through the Art of Visualisation (13%)
Course 6: Data visualisation principles, Tableau, and presenting insights effectively to stakeholders.
🌐
Data Analysis with R Programming (13%)
Course 7: Introduction to R, tidyverse, ggplot2, and generating analytical reports with R Markdown.
🛡
Google Data Analytics Capstone (19%)
Course 8: Capstone project where learners complete an end-to-end case study to demonstrate full data analytics competency.
Full Syllabus

Google Data Analytics Certificate Exam Syllabus and Topics

The Google Data Analytics Certificate exam is divided into 8 domains. Each domain covers specific skills and topics. Expand a domain to see the detailed syllabus.

The Data Analysis Process
The six phases: Ask, Prepare, Process, Analyse, Share, Act
Difference between data analysis, data analytics, and data science
Types of data: quantitative vs qualitative, discrete vs continuous
Structured vs unstructured data
Key tools: spreadsheets, SQL, R, Tableau
Data Analyst Role
Responsibilities of a data analyst in business contexts
Analytical skills: curiosity, context, technical mindset, data design, strategy
Analytical thinking: visualisation, strategy, problem-orientation, correlation, big-picture
Data ecosystems and data-driven decision making
~0 questions
0 marks
13% of exam weight
Structured Thinking
SMART questions: Specific, Measurable, Action-oriented, Relevant, Time-bound
Structured thinking: defining the problem, understanding scope, creating a plan
Issue tree and scope creep management
Quantitative vs qualitative data in problem-solving
Spreadsheet Basics
Google Sheets and Excel: navigation, formulas, and functions
Basic functions: SUM, AVERAGE, COUNT, COUNTA, MAX, MIN
Sorting and filtering data in spreadsheets
Creating pivot tables for summarisation
~0 questions
0 marks
13% of exam weight
Data Sources and Bias
First-party, second-party, and third-party data
Internal vs external data
Data bias types: sampling, observer, interpretation, confirmation
ROCCC framework: Reliable, Original, Comprehensive, Current, Cited
Ethics in data collection: consent, privacy, and currency
Data Organisation and SQL Intro
File naming conventions and folder organisation best practices
Metadata: descriptive, structural, and administrative
Introduction to databases and relational database concepts
Basic SQL: SELECT, FROM, WHERE, ORDER BY, GROUP BY, LIMIT
BigQuery: navigating Google's cloud-based SQL environment
~0 questions
0 marks
13% of exam weight
Identifying and Fixing Data Issues
Common data problems: duplicates, nulls, wrong format, inconsistent labels
Data validation: range, consistency, and cross-field checks
Spreadsheet cleaning: TRIM(), PROPER(), CONCATENATE(), IF(), VLOOKUP()
SQL cleaning: TRIM, UPPER/LOWER, COALESCE, CAST, LIKE
Removing duplicates in both spreadsheets and SQL
Documentation and Verification
Documenting cleaning steps in a changelog
Verification: checking that cleaning achieved expected results
Data integrity: accuracy, completeness, consistency, and trustworthiness
~0 questions
0 marks
13% of exam weight
Advanced SQL
JOINs: INNER, LEFT, RIGHT, FULL OUTER
Aggregate functions: COUNT, SUM, AVG, MAX, MIN with GROUP BY
Subqueries and nested queries
String, numeric, and date functions in SQL
HAVING clause vs WHERE clause
Aliases and temporary tables
Spreadsheet Analysis
Pivot tables: creating, filtering, and interpreting
VLOOKUP and HLOOKUP for data lookup
Advanced formulas: COUNTIF, SUMIF, AVERAGEIF
Conditional formatting for pattern identification
~0 questions
0 marks
13% of exam weight
Visualisation Principles
Choosing the right chart type: bar, line, scatter, pie, histogram, heat map
Pre-attentive attributes: colour, shape, size, and position
Design principles: balance, emphasis, and accessibility
Data storytelling: context, conflict, and resolution narrative
Tableau
Connecting to data sources in Tableau Public
Creating basic charts: bar, line, scatter, and map
Calculated fields and filters in Tableau
Dashboard design: layout, interactivity, and storytelling
Publishing and sharing Tableau dashboards
~0 questions
0 marks
13% of exam weight
R Fundamentals
R and RStudio: console, script, environment, and files panes
Data types in R: vectors, data frames, matrices, factors
Basic operations, assignment, and functions
Importing data: read_csv(), read_excel()
Tidyverse packages: dplyr, tidyr, readr, ggplot2
Data Manipulation with dplyr and Visualisation with ggplot2
dplyr verbs: filter(), select(), arrange(), mutate(), summarise(), group_by()
Piping with |> or %>%
tidyr: pivot_longer(), pivot_wider(), separate(), unite()
ggplot2: aes(), geom_bar(), geom_line(), geom_point(), geom_histogram()
Facets, labels, and themes in ggplot2
R Markdown: creating reproducible reports with code and narrative
~0 questions
0 marks
13% of exam weight
Case Study Process
Applying the Ask-Prepare-Process-Analyse-Share-Act framework to a real dataset
Choosing a track: Cyclistic bike-share case study or custom dataset
Formulating a clear business question and hypothesis
Cleaning and preparing the dataset using SQL, R, or spreadsheets
Performing exploratory data analysis (EDA)
Creating visualisations in Tableau or ggplot2
Writing a structured case study report or creating a portfolio presentation
Publishing findings on GitHub, Kaggle, or Google Sites
~0 questions
0 marks
19% of exam weight
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Study Plan

Google Data Analytics Certificate Structured Study Roadmap

Choose a preparation timeline based on how much time you have available. For a plan based on your actual readiness and weak domains, use the personalised Edureify study experience. Get My Training Plan →

Weeks 1-2
Core Services + Highest-Weighted Domain
Deep-dive into the most heavily tested domain. Spend more time here when its exam weight is significantly higher.
Official exam guideDomain 1 completeCore conceptsPractice questions
Week 3
Domain 2 - Hands-on Practice
Focus on scenario-based study and reinforce concepts through practical application where applicable.
Domain 2Scenario walkthroughsHands-on practicePractice questions
Week 4
Domain 3 - Deeper Concepts
Work through complex concepts and decision scenarios.
Domain 3Scenario drillsPractice examReview
Week 5
Remaining Domains + Weak Area Targeting
Identify your weaker domains and spend focused time closing those gaps.
Remaining domainsDiagnosticTargeted reviewStudy notes
Week 6
Full Simulations + Final Preparation
Use timed simulations to test your preparation and review the reasoning behind incorrect answers.
Full mock examsWrong-answer reviewFinal reviewExam logistics
Exam Strategy

Tips to pass Google Data Analytics Certificate on your first attempt

Practical advice for applying what you know, managing questions and preparing for exam conditions.

🗓
Focus heavily on SQL — JOIN types, GROUP BY with HAVING, and aggregate functions appear in quizzes across multiple courses and are core to the capstone.
🔍
The Ask-Prepare-Process-Analyse-Share-Act framework is tested throughout: know what happens in each phase and which tools are used at each stage.
⏭
SMART questions appear early and are tested repeatedly: every data analyst role question connects back to defining the right problem before collecting data.
📊
ROCCC (Reliable, Original, Comprehensive, Current, Cited) is the Google framework for evaluating data credibility — memorise the acronym and what each letter means.
🔁
Data cleaning quizzes are detailed: know the difference between TRIM (removes spaces), COALESCE (handles nulls), CAST (changes data type), and how to use each in SQL context.
🧪
Tableau is tested both conceptually and practically: know when to use calculated fields, how to add filters to dashboards, and the difference between measures and dimensions.
📝
R quizzes focus on tidyverse: dplyr pipes, ggplot2 layers (data + aes + geom), and the difference between pivot_longer and pivot_wider are favourite quiz topics.
🎯
The capstone project is the most important deliverable for employability: choose a clean, interesting dataset and write a case study that can be shared with potential employers on LinkedIn or GitHub.
🗓
Peer-reviewed assignments require you to review others' work too — take this seriously, as you must complete reviews to get your own work graded.
🔍
The certificate is best combined with a portfolio: completing the Cyclistic case study or creating additional projects on Kaggle significantly strengthens job applications.
Recommended Resources

Google Data Analytics Certificate Study Resources

Use a focused set of resources alongside the study guide rather than trying to study from everything available.

Official
Official Exam Guide
Start with the authoritative exam objectives and blueprint.
→ Vendor website
Practice Tests
Google Data Analytics Certificate Practice Test
Practice questions with explanations and domain-level performance analysis.
→ Start free practice test
Mock Exam
Google Data Analytics Certificate Mock Exam
Timed preparation under realistic exam-style conditions.
→ Take free mock exam
Training
Google Data Analytics Certificate Certification Training
Structured preparation with personalised learning support and adaptive practice.
→ Google Data Analytics Certificate certification online training
AI Tutor
Google Data Analytics Certificate AI Tutor
Get help understanding concepts and work on weak areas with AI-powered learning support.
→ Try Google Data Analytics Certificate AI tutor
Reference
Google Data Analytics Certificate Cheat Sheet
Quick-reference summaries for final revision.
→ Get free cheat sheet
Diagnostic
Google Data Analytics Certificate Readiness Test
Assess your preparation and identify weaker exam domains.
→ Check my readiness
⚠️
Avoid brain dumps. Sites selling real or stolen exam questions may violate certification-provider rules and can leave candidates studying outdated material.
Reviews

What candidates say after passing

★★★★★
The Ask phase is the one candidates skip most eagerly and suffer for most consistently.Edureify AI's analysis scenarios always required me to identify the specific business question before touching the data - the same discipline the certification tests throughout its framework.
Brandon S.
DevOps Lead
★★★★★
SQL in the Process phase is tested at a practical level the certification doesn't always advertise prominently.Edureify AI's data cleaning scenarios using SQL - handling nulls, standardizing formats, removing duplicates - built the technical skills that the R and SQL assessments actually require.
Carlos M.
Cloud Engineer
★★★★★
Correlation vs. causation scenarios are more nuanced in the certification than in general statistics education.Edureify AI's business recommendation scenarios consistently required me to recommend further investigation rather than causal action - and to explain why the correlation alone doesn't justify the action.
Kavya M.
Risk Analyst
★★★★★
The Act phase is where the certification differentiates itself from pure technical training.Edureify AI's scenario conclusions always required a specific, actionable recommendation - not just findings. That discipline is what the certification tests in its Share and Act phases, and what makes data analysis actually valuable in business.
Isabella K.
IT Auditor
FAQ

Frequently asked questions about Google Data Analytics Certificate

Most candidates with relevant background can structure their preparation over several weeks, depending on their existing knowledge, available study time and exam difficulty. Use the study roadmap above as a starting point and use the readiness diagnostic to identify where you need more preparation.
The guide covers the exam overview, domains, detailed syllabus and topics, study roadmap, exam preparation tips and links to practice, mock, readiness, cheat-sheet, AI Tutor and training resources.
The guide is designed to organize your preparation around the exam syllabus. You should combine it with practice questions and timed simulations so that you can test both your knowledge and your ability to apply it.
Yes. Start with the exam overview and domain breakdown, then work through the detailed topics using the study roadmap. Candidates with less experience may need additional time for foundational concepts.
Take the Edureify readiness diagnostic to assess your preparation and identify the domains where you need to focus more.
Edureify AI can help explain concepts, identify weaker areas from practice performance and support a more personalised preparation process.

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