Google Data Analytics Syllabus

Overview: This 6-month, self-paced program requires approximately 260 hours to complete, with a recommended commitment of 10 hours per week. Designed for beginners, it covers the full data analytics workflow—from data collection and cleaning to analysis and visualization—using industry-standard tools like spreadsheets, SQL, Python, R, and Tableau. Learners progress through a series of nine courses that build practical, job-ready skills without requiring prior experience or a degree.

Module 1: Ask Questions to Make Data-Driven Decisions

Estimated time: 30 hours

• Understanding the role of a data analyst
• Formulating effective business questions
• Identifying data types and sources
• Applying data ethics and bias awareness

Module 2: Prepare Data for Exploration

Estimated time: 40 hours

• Collecting and verifying data
• Using spreadsheets for data organization
• Identifying and handling missing or duplicate data
• Standardizing and formatting data sets

Module 3: Process Data from Dirty to Clean

Estimated time: 45 hours

• Identifying common data integrity issues
• Validating data cleaning results
• Documenting the cleaning process
• Using SQL for filtering and transforming data

Module 4: Analyze Data to Answer Questions

Estimated time: 50 hours

• Performing calculations using spreadsheets
• Querying databases with SQL
• Applying data analysis techniques in R
• Using Python for data manipulation and analysis

Module 5: Share Data Findings with Stakeholders

Estimated time: 45 hours

• Designing effective data visualizations
• Creating dashboards in Tableau
• Presenting insights clearly and concisely
• Using R for visualization and reporting

Module 6: Final Project

Estimated time: 50 hours

• Perform a complete data analysis using real-world datasets
• Document each stage: question, preparation, cleaning, analysis, and sharing
• Present findings using Tableau and R

Prerequisites

• No prior experience required
• No college degree necessary
• Basic computer literacy

What You'll Be Able to Do After

• Collect, organize, and clean data from various sources
• Use spreadsheets, SQL, Python, and R for data analysis
• Apply best practices in data cleaning and transformation
• Create compelling visualizations and dashboards in Tableau
• Communicate data insights effectively to non-technical stakeholders