What will you learn in Google Advanced Data Analytics Professional Certificate Course
Apply Python, Jupyter Notebook, and Tableau for data cleaning, visualization, and business storytelling.
Conduct exploratory data analysis (EDA), statistical modeling, hypothesis testing, regression, and predictive modeling.
Build and evaluate linear/logistic regression models, assess with ANOVA, chi‑square, and more.
Develop foundational machine learning skills including naive Bayes and decision trees.
Program Overview
Module 1: Foundations of Data Science
⏳ ~21 hours
Topics: Introduction to data science, PACE (Plan-Analyze-Construct-Execute) workflow, data professional roles, foundational analytics tools.
Hands-on: Core project using PACE and foundational assessments.
Module 2: Python for Data Analysis
⏳ ~20 hours
Topics: Python syntax, data structures (lists, dictionaries), pandas and NumPy for data manipulation.
Hands-on: Extensive hands-on Python labs and quizzes.
Module 3: Translate Data into Insights
⏳ ~30 hours
Topics: Exploratory Data Analysis (EDA), best practices, visual storytelling using Tableau and Python.
Hands-on: Build dashboards, interpret insights, and complete real-world scenarios.
Module 4: The Power of Statistics
⏳ ~20 hours
Topics: Probability distributions, hypothesis testing, A/B testing, experimental design.
Hands-on: Apply statistical tests and complete analytical assignments.
Module 5: Regression Analysis ⏳ ~20 hours
Topics: Linear and logistic regression models, coefficient interpretation, ANOVA, chi-square.
Hands-on: Regression modeling tasks using Python.
Module 6: Machine Learning Fundamentals
⏳ ~20 hours
Topics: Naive Bayes, decision trees, basics of supervised learning workflows.
Hands-on: Implement models and evaluate performance.
Module 7: Capstone Project
⏳ ~30 hours
Topics: Apply cumulative learning to a simulated real-world business challenge—analysis, modeling, reporting.
Hands-on: Complete capstone deliverables for portfolio inclusion (optional but useful).
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Job Outlook
Designed for roles such as Senior Data Analyst, Junior Data Scientist, and Data Science Analyst.
Median salary is around USD 118,000; strong demand with over 84,000 openings in the field.
Best suited for learners with prior analytics experience (or completion of the Google Data Analytics Certificate).
Specification: Google Advanced Data Analytics Professional Certificate
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FAQs
- Prior analytics knowledge is strongly recommended.
- The beginner certificate is not mandatory but helpful.
- Comfort with Python, statistics, and data analysis is essential.
- Beginners may find the pace challenging without preparation.
- Best for learners with at least some analytics background.
- Faster and more affordable than a Master’s degree.
- Focuses on practical, job-ready skills.
- No thesis or deep theoretical research.
- Recognized by employers but not equivalent to a graduate degree.
- Ideal for career entry or skill advancement.
- Focuses on advanced analytics and applied machine learning.
- Strong foundation for junior data science roles.
- Covers regression, statistics, and supervised ML basics.
- Not as in-depth in deep learning or AI.
- Serves as a stepping stone toward data science careers.
- Uses Python, Jupyter, and Tableau in the curriculum.
- Tableau has a free public version for practice.
- Python and Jupyter are open-source and free.
- Paid software is not required for learning.
- Employers value skills in both free and enterprise tools.
- A capstone project simulating a real business case.
- End-to-end analysis using Python, statistics, and ML.
- Dashboards built in Tableau for visualization.
- Projects demonstrate both technical and storytelling skills.
- Portfolio-ready deliverables to share with employers.
