The Google Advanced Data Analytics Professional Certificate is designed for experienced data analysts who want to advance into more technical and analytical roles. This program builds on foundational data analytics skills and introduces advanced techniques used to analyze large datasets, uncover patterns, and generate predictive insights.
Learners gain hands-on experience with Python, Jupyter Notebook, Tableau, statistics, regression analysis, and machine learning. Through real-world projects and applied coursework, this certificate prepares learners for roles such as senior data analyst, data science analyst, and junior data scientist.
Estimated Time to Complete
Approximately 6 months at 10 hours per week, self-paced
Why Take This Course?
📈 Advance Your Data Analytics Skills – Move beyond descriptive analytics into predictive modeling and machine learning.
🐍 Hands-On Technical Training – Work with Python, Jupyter Notebook, and Tableau to analyze and model data.
🧠 Learn Advanced Analytical Methods – Apply statistics, regression analysis, and machine learning to real-world problems.
📊 Portfolio-Ready Projects – Build projects that demonstrate advanced analytical and data science skills.
🎓 Career-Focused Credential – Earn a shareable Google Professional Certificate recognized by employers.
What You’ll Learn
✔ How to structure and manage advanced data analysis projects using industry workflows.
✔ How to analyze and interpret data using statistics and probability.
✔ How to clean, transform, and visualize complex datasets using Python and Tableau.
✔ How to apply regression analysis and machine learning models to business problems.
✔ How to communicate insights clearly through data storytelling and visualizations.
Course Modules & Skills Gained
📌 Course 1: The Nuts and Bolts of Machine Learning
• Understand supervised and unsupervised learning techniques.
• Apply feature engineering using Python.
• Build and evaluate models such as Naive Bayes, decision trees, random forests, and K-means.
• Explore model tuning, performance evaluation, bagging, and boosting techniques.
📌 Course 2: Foundations of Data Science
• Understand the role of data analytics and data science within organizations.
• Learn the PACE (Plan, Analyze, Construct, Execute) project workflow.
• Explore tools, career paths, and professional responsibilities of data professionals.
• Develop communication skills for presenting data-driven insights.
📌 Course 3: Google Advanced Data Analytics Capstone
• Complete an optional capstone project addressing a real-world business problem.
• Develop datasets, visualizations, and analytical models.
• Apply regression, statistics, and machine learning techniques.
• Update and strengthen a professional data analytics portfolio.
📌 Course 4: Regression Analysis: Simplify Complex Data Relationships
• Model relationships between variables using regression techniques.
• Apply linear, multiple, and logistic regression models.
• Conduct ANOVA tests and interpret results.
• Use predictive modeling to support data-driven decision-making.
📌 Course 5: Go Beyond the Numbers: Translate Data into Insights
• Perform exploratory data analysis using Python.
• Clean, transform, and validate datasets.
• Create accessible and effective data visualizations using Tableau.
• Apply data storytelling techniques to communicate insights.
📌 Course 6: The Power of Statistics
• Apply descriptive and inferential statistics to analyze data.
• Work with probability, sampling, and distributions.
• Construct confidence intervals and conduct hypothesis testing.
• Use Python for statistical analysis and interpretation.
📌 Course 7: Accelerate Your Job Search with AI
• Identify skills and define career goals.
• Use AI tools to create resumes, job search plans, and application trackers.
• Practice interview responses and prepare job-ready materials.
• Build a personalized job search portfolio.
Who Should Enroll?
This program is ideal for:
✅ Graduates of the Google Data Analytics Certificate or equivalent programs.
✅ Data analysts seeking advanced technical and analytical skills.
✅ Professionals transitioning toward data science or senior analytics roles.
✅ Learners with experience in Python, statistics, and data analysis tools.
📌 Designed for experienced learners who want to deepen their analytical expertise and advance their careers in data analytics.