Google Advanced Data Analytics Professional Certificate

This repository contains my coursework, project files, and case studies completed during the Google Advanced Data Analytics Professional Certificate program. The certification curriculum covers data science fundamentals, statistical analysis, regression modeling, and machine learning, structured through the PACE (Plan, Analyze, Construct, Execute) project workflow.

My verified certificate can be found here: Certificate.pdf

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Program Structure & Coursework

Course 1: Foundations of Data Science

• Folder: Course1 - Foundations of Data Science
• Focus: Understanding the role of data professionals, using the PACE workflow framework, and project planning.
• Key Work Products:
  • Automatidata project proposal.docx: Client project proposal defining deliverables and boundaries.
  • Course 1 PACE strategy document.docx: Implementation of the PACE framework structure for Automatidata.

Course 2: Get Started with Python

• Folder: Course2 - Get Started with Python
• Focus: Data structure manipulation, scripting fundamentals, and utilizing Python libraries (Pandas and NumPy) for data inspection.
• Key Work Products:
  • Course 2 Automatidata project lab.ipynb: Python script inspecting and preparing NYC TLC dataset.
  • Course 2_ Executive summary.pdf / Course 2_ Executive summary.pptx: Client-facing executive report summarizing data characteristics.

Course 3: Go Beyond the Numbers: Translate Data into Insights

• Folder: Course3 - Go Beyond the Numbers - Translate Data into Insights
• Focus: Exploratory Data Analysis (EDA), data cleaning, creating visualizations, and interactive dashboards.
• Key Work Products:
  • Course 3 Automatidata project lab.ipynb: Extensive data cleaning and EDA on trip distances, fares, and payment types.
  • Course 3 Automatidata Tableau Workbook.twbx: Interactive visualization dashboard.
  • Course 3 Executive summary.pptx: Technical presentation communicating visual patterns to stakeholders.

Course 4: The Power of Statistics

• Folder: Course4 - The Power of Statistics
• Focus: Descriptive statistics, probability distributions, sampling, and hypothesis testing.
• Key Work Products:
  • Course 4 Automatidata project lab.ipynb: Two-sample A/B hypothesis test comparing fare amounts between credit card and cash payment groups.
  • Course 4 Executive Summary.pptx: Presentation translating statistical test results into business recommendations.

Course 5: Regression Analysis: Simplify Complex Data Relationships

• Folder: Course5 - Regression Analysis - Simplify Complex Data Relationships
• Focus: Building and evaluating multiple linear and logistic regression models to understand feature relationships and make predictions.
• Key Work Products:
  • Course 5 Automatidata project lab.ipynb: Multiple linear regression model predicting taxi fare amounts based on trip features.
  • Course 5 Automatidata Executive Summary.pptx: Summary of regression coefficients, assumptions validation, and predictive performance.

Course 6: The Nuts and Bolts of Machine Learning

• Folder: Course6 - The Nuts and Bolts of Machine Learning
• Focus: Supervised machine learning, feature engineering, hyperparameter tuning, and ensemble modeling.
• Key Work Products:
  • Course 6 Automatidata project lab.ipynb: Random Forest and XGBoost classification models built to predict high-tip rides (>20%).
  • Course 6 Automatidata Executive Summary.pptx: Presentation evaluating ML model metrics (precision, recall, F1-score) and business utility.

Course 7: Google Advanced Data Analytics Capstone

• Folder: Course7 - Google Advanced Data Analytics Capstone
• Focus: End-to-end predictive modeling application using a real-world project workspace.
• Project: Salifort Motors HR Retention Analysis
• Key Work Products:
  • Course 7 Salifort Motors project lab.ipynb: Completed machine learning pipeline to analyze employee satisfaction and predict churn.
  • HR_capstone_dataset.csv: Dataset tracking employee performance, tenure, and department metrics.