Welcome to my project portfolio for the Google Advanced Data Analytics Professional Certificate, issued by Google and hosted on Coursera.
This repository presents a curated collection of hands-on projects and case studies completed as part of the certification program. These projects demonstrate my proficiency in statistics, machine learning, exploratory data analysis, and business-oriented data storytelling using Python.
You can find the certificate of completion here:
📌 Google_Advanced_Data_Analytics_Certificate.pdf
📦google-advanced-data-analytics
┣ 📄 README.md
┣ 📄 Google_Advanced_Data_Analytics_Certificate.pdf
┣ 📂 employee-turnover-analysis
┃ ┗ 📓 employee_turnover.ipynb
┃ ┗ 📄 HR_capstone_dataset.csv
┃ ┗ 📄 README.md
┣ 📂 tiktok-workplace-scenario
┃ ┗ 📄 README.md
┃ ┗ 📄 tiktok_dataset.csv
┃ ┣ 📓 tiktok_hypothesis_testing.ipynb
┃ ┗ 📓 tiktok_modeling.ipynb
This project simulates a real-world HR use case: analyzing employee retention and building a model to predict future turnover.
- Performed hypothesis testing to investigate salary vs. job satisfaction.
- Engineered features and trained a Random Forest classifier to predict turnover.
- Delivered interpretability via feature importances.
📄 Explore more in the employee-turnover-analysis folder.
A workplace-inspired project where I built a classifier to distinguish between claim and opinion content, assisting moderators.
- Conducted statistical testing to explore view counts between verified/unverified accounts.
- Built a robust machine learning model prioritizing recall to reduce moderation risks.
- Achieved near-perfect performance using text and engagement data.
📄 Details are in the tiktok-workplace-scenario folder.
- 🧮 Statistical Hypothesis Testing
- 🧠 Machine Learning (Classification & Evaluation)
- 📊 EDA & Visualization
- 🛠️ Python,
pandas,numpy,matplotlib,scikit-learn,xgboost,seaborn - 📈 Model tuning with cross-validation
- 📁 Project documentation and reproducibility
This program deepened my understanding of advanced analytics, emphasizing both statistical rigor and machine learning application in business settings. The hands-on projects equipped me to:
- Design and validate predictive models
- Present data-driven business insights
- Apply best practices in responsible AI and data ethics