Google Data Analysis with Python Specialization course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
A practical, career-focused specialization that teaches real-world data analysis using Python. This course is designed for beginners and structured by Google to provide hands-on experience with Python for data analysis. You'll learn to clean, analyze, and visualize data using industry-standard tools, with a focus on real-world analytics workflows. The program takes approximately 13–16 weeks to complete, with a recommended 4–6 hours of work per week, including hands-on exercises and a final project.
Module 1: Python Foundations for Data Analysis
Estimated time: 12 hours
- Introduction to Python variables and data types
- Working with lists, dictionaries, and control flow
- Writing functions for data tasks
- Using Jupyter Notebooks for interactive coding
Module 2: Data Manipulation with Pandas and NumPy
Estimated time: 16 hours
- Loading datasets using Pandas
- Handling missing values and duplicates
- Filtering, sorting, and subsetting data
- Grouping and aggregating data with Pandas
Module 3: Exploratory Data Analysis (EDA)
Estimated time: 12 hours
- Applying descriptive statistics to datasets
- Identifying patterns, trends, and outliers
- Using correlation and distribution analysis
- Interpreting EDA results for decision-making
Module 4: Data Visualization and Reporting
Estimated time: 12 hours
- Creating visualizations with Matplotlib and Seaborn
- Designing charts for clarity and impact
- Communicating insights to stakeholders
- Building simple data analysis reports in Python
Module 5: Applying Data Analysis to Real-World Problems
Estimated time: 10 hours
- Connecting analysis to business questions
- Using data to support recommendations
- Reviewing real-world analytics workflows
Module 6: Final Project
Estimated time: 18 hours
- Load and clean a real-world dataset
- Perform exploratory data analysis
- Create visualizations and present findings in a report
Prerequisites
- Familiarity with basic computer operations
- No prior programming experience required
- Access to a web browser and internet connection
What You'll Be Able to Do After
- Use Python to manipulate and analyze real datasets
- Clean and prepare data for analysis
- Perform exploratory data analysis to uncover insights
- Create clear data visualizations for reporting
- Apply data-driven thinking to business problems