Google Data Analytics Certificate Study Guide (2026)

Google Data Analytics Certificate Study Guide 2026 – Pass on Your First Attempt

This Google Data Analytics Certificate study guide covers all exam domains, key concepts, and real exam-style scenarios to help you pass on your first attempt. Learn what topics matter most, avoid common mistakes, and follow a structured plan based on the official exam blueprint.

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

To pass the Google Data Analytics Certificate certification exam, you should focus on:

  • Foundations of Data Analytics: 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: Course 2: Structured thinking, problem framing, effective stakeholder communication, and spreadsheet basics.
  • Prepare Data for Exploration: Course 3: Data types, data collection, bias, credibility, data organisation, and introduction to SQL.
  • Process Data from Dirty to Clean: Course 4: Data cleaning techniques using spreadsheets and SQL, verifying and documenting cleaning steps.
  • Analyse Data to Answer Questions: Course 5: Advanced SQL, spreadsheet analysis functions, and performing aggregations and calculations.
  • Share Data Through the Art of Visualisation: Course 6: Data visualisation principles, Tableau, and presenting insights effectively to stakeholders.
  • Data Analysis with R Programming: Course 7: Introduction to R, tidyverse, ggplot2, and generating analytical reports with R Markdown.
  • Google Data Analytics Capstone: Course 8: Capstone project where learners complete an end-to-end case study to demonstrate full data analytics competency.

The exam tests your ability to apply concepts in real scenarios, not just memorize definitions.

Google Data Analytics Certificate Exam Syllabus and Topics

The Google Data Analytics Certificate exam is divided into 8 domains. Each domain tests specific skills and contributes to your overall score.

Foundations of Data Analytics

Course 1: Introduces data analytics concepts, the data analysis process, the role of a data analyst, and core tools.

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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

Ask Questions to Make Data-Driven Decisions

Course 2: Structured thinking, problem framing, effective stakeholder communication, and spreadsheet basics.

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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

Prepare Data for Exploration

Course 3: Data types, data collection, bias, credibility, data organisation, and introduction to SQL.

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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

Process Data from Dirty to Clean

Course 4: Data cleaning techniques using spreadsheets and SQL, verifying and documenting cleaning steps.

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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

Analyse Data to Answer Questions

Course 5: Advanced SQL, spreadsheet analysis functions, and performing aggregations and calculations.

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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

Share Data Through the Art of Visualisation

Course 6: Data visualisation principles, Tableau, and presenting insights effectively to stakeholders.

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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

Data Analysis with R Programming

Course 7: Introduction to R, tidyverse, ggplot2, and generating analytical reports with R Markdown.

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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

Google Data Analytics Capstone

Course 8: Capstone project where learners complete an end-to-end case study to demonstrate full data analytics competency.

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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
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Learning Progress

Foundations of Data Analytics 85%
Ask Questions to Make Data-Driven Decisions 92%

Practice Test Scores

95%
Latest Score
Above passing threshold

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