Google Advanced Data Analytics Professional Certificate Course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
Overview: This advanced professional certificate program is designed for learners with prior data analytics experience who want to elevate their skills in Python, statistics, machine learning, and data storytelling. The course spans approximately 150 hours of hands-on learning, structured across seven modules that build from foundational data science concepts to a comprehensive capstone project. Learners will engage with real-world scenarios using tools like Python, Jupyter Notebook, and Tableau, culminating in a portfolio-ready project that demonstrates analytical proficiency and business insight translation.
Module 1: Foundations of Data Science
Estimated time: 21 hours
- Introduction to data science and its applications
- Understanding the PACE workflow (Plan, Analyze, Construct, Execute)
- Roles and responsibilities of data professionals
- Foundational analytics tools and environments
Module 2: Python for Data Analysis
Estimated time: 20 hours
- Python syntax and programming fundamentals
- Data structures: lists, dictionaries, and control flow
- Data manipulation with pandas and NumPy
- Hands-on labs for data cleaning and transformation
Module 3: Translate Data into Insights
Estimated time: 30 hours
- Exploratory Data Analysis (EDA) techniques
- Best practices in data visualization
- Visual storytelling with Tableau and Python
- Building interactive dashboards and reports
Module 4: The Power of Statistics
Estimated time: 20 hours
- Probability distributions and sampling
- Hypothesis testing and confidence intervals
- A/B testing and experimental design
- Applying statistical tests in real-world scenarios
Module 5: Regression Analysis
Estimated time: 20 hours
- Linear regression modeling and interpretation
- Logistic regression for classification tasks
- Analysis of Variance (ANOVA) and chi-square tests
- Evaluating model performance using Python
Module 6: Machine Learning Fundamentals
Estimated time: 20 hours
- Introduction to supervised learning workflows
- Naive Bayes classifiers and their applications
- Decision trees and model interpretation
- Evaluating machine learning model performance
Module 7: Capstone Project
Estimated time: 30 hours
- Apply PACE framework to a real-world business problem
- Conduct end-to-end data analysis and modeling
- Present findings using visualizations and storytelling
Prerequisites
- Completion of Google Data Analytics Certificate or equivalent experience
- Strong foundation in basic statistics and data analysis
- Familiarity with Python programming recommended
What You'll Be Able to Do After
- Apply Python and Jupyter Notebook for advanced data analysis
- Conduct exploratory data analysis and generate actionable insights
- Build and evaluate regression and machine learning models
- Create compelling data visualizations and dashboards in Tableau
- Communicate analytical findings effectively to business stakeholders