Google Data-Driven Decision Making Specialization course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
Overview (80-120 words) describing structure and time commitment.
Module 1: Foundations of Data-Driven Thinking
Estimated time: 10 hours
- Understand the concept of data-driven decision-making in business
- Identify key metrics and performance indicators (KPIs)
- Explore real-world examples of data-informed strategies
- Recognize the value of evidence-based decisions in organizations
Module 2: Collecting and Organizing Data
Estimated time: 10 hours
- Learn how to gather relevant data for business problems
- Understand different data types and sources
- Evaluate data quality and reliability
- Organize data using spreadsheets and structured formats
Module 3: Analyzing and Interpreting Data
Estimated time: 12 hours
- Perform basic descriptive analysis and comparisons
- Identify trends, patterns, and outliers in datasets
- Apply analytical reasoning to support business recommendations
- Use simple tools to explore data effectively
Module 4: Communicating Insights and Making Decisions
Estimated time: 10 hours
- Learn data storytelling techniques
- Create simple visualizations to present findings
- Communicate insights clearly to stakeholders
- Develop actionable, data-backed recommendations
Module 5: Applying Data-Driven Frameworks
Estimated time: 8 hours
- Apply structured problem-solving approaches
- Use data-driven frameworks in real-world scenarios
- Develop a mindset focused on continuous improvement through data
Module 6: Final Project
Estimated time: 10 hours
- Analyze a real-world business dataset
- Interpret findings and identify key insights
- Present a data-driven recommendation with visual support
Prerequisites
- Familiarity with basic business concepts
- Basic computer literacy
- Access to spreadsheet software (e.g., Google Sheets or Excel)
What You'll Be Able to Do After
- Understand how organizations use data to make informed decisions
- Collect, organize, and interpret business data effectively
- Apply analytical reasoning to solve common business problems
- Communicate data insights clearly to non-technical stakeholders
- Make actionable, evidence-based recommendations using data