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Denis0242/README.md

Denis Agyapong

Product Data Analyst | Python, SQL, Experimentation, Funnel & Retention Analytics

🌍 Links:

📍 Oakland, CA
📧 vantjohnn@gmail.com
🔗 LinkedIn
📊 Tableau Public
🐙 GitHub

I build end-to-end product analytics projects that simulate real-world decision-making — from tracking user behavior and diagnosing funnel drop-offs to evaluating A/B tests and predicting churn.

My work focuses on answering core product questions:

  • Where are users dropping off?
  • What features drive activation and retention?
  • Did this experiment actually improve the product?
  • Which users are at risk of churn and why?

🔍 What I Do

  • Analyze user behavior using funnel, cohort, and retention analysis
  • Design and evaluate A/B tests with statistical rigor
  • Build KPI frameworks (DAU, WAU, MAU, conversion, retention)
  • Develop interactive dashboards for product decision-making
  • Apply machine learning for churn prediction and user segmentation
  • Translate data insights into actionable product recommendations

🚀 Featured Product Analytics Projects

🧪 Experimentation & A/B Testing

A/B Testing Simulator (FastAPI + Statistics)
🔗 https://github.com/Denis0242/AB_test_with_stats

  • Built an end-to-end experimentation framework to simulate real product experiments
  • Implemented hypothesis testing, confidence intervals, and power analysis
  • Supports go/no-go product decisions based on statistical significance
  • Designed to mirror real experimentation workflows used by product teams

📊 Customer Product Journey Analytics

Customer Product Analytics Dashboard (Streamlit + Plotly)
🔗 https://github.com/Denis0242/customer-product-journey

  • Analyzed full user lifecycle from acquisition → activation → retention
  • Identified drop-off points and behavioral patterns across segments
  • Built interactive dashboards to monitor product performance and engagement
  • Provided insights to optimize user journey and improve conversion

🏥 Healthcare Product Analytics

CareFlow – Patient Journey & Risk Analytics (Streamlit + ML)
🔗 https://github.com/Denis0242/CareFlow

  • Modeled patient journey flows to identify inefficiencies in healthcare systems
  • Applied predictive analytics to detect high-risk patients
  • Built dashboards to track operational KPIs and care outcomes
  • Simulates product analytics use cases in healthcare environments

🔁 Churn & Retention Analysis

Customer Churn Prediction (Streamlit + Machine Learning)
🔗 https://github.com/Denis0242/Customer-Churn

  • Built predictive models to identify users at high risk of churn
  • Analyzed retention drivers and behavioral patterns
  • Used feature importance to explain churn causes
  • Provided actionable retention strategies based on insights

📈 Healthcare KPI Dashboard

Kaiser Healthcare Analytics Dashboard (Streamlit + BI Concepts)
🔗 https://github.com/Denis0242/Kaiser_Dashboard

  • Developed KPI dashboards for operational and quality metrics
  • Performed EDA on healthcare datasets to uncover trends
  • Simulated real-world reporting workflows in healthcare analytics

📡 Telecom Data Analysis

Telecom Usage & Behavior Dashboard (Streamlit + EDA)
🔗 https://github.com/Denis0242/Telecom_Analysis

  • Explored user activity patterns across telecom services
  • Analyzed usage, session behavior, and traffic distribution
  • Built dashboards for monitoring performance and anomalies

🛠️ Tools & Technologies

Languages & Analysis
Python (Pandas, NumPy, Scikit-learn, Statsmodels), SQL

Visualization & Dashboards
Streamlit, Plotly, Power BI, Tableau

Experimentation & Analytics
A/B Testing, Hypothesis Testing, Funnel Analysis, Cohort Analysis, Retention Metrics

Data Engineering & Workflow
Prefect, Git, GitHub, FastAPI

📌 How to Use My Projects

Each repository includes:

  • Business problem definition
  • Dataset overview
  • Analytical approach
  • Key insights and recommendations
  • Instructions to run locally or view live dashboards

🎯 What I’m Targeting

I am currently focused on roles such as:

  • Product Data Analyst
  • Product Analyst
  • Data Analyst (Product-focused)

🤝 Let’s Connect

💡 Final Note

This portfolio is built to reflect how product teams actually use data —
not just to analyze, but to drive decisions, improve features, and grow products.

Pinned Loading

  1. AB_test_with_stats AB_test_with_stats Public

    End-to-end A/B testing framework using frequentist and Bayesian methods to evaluate product experiments, track KPIs, and support data-driven go/no-go decisions.

    Python

  2. Customer-Churn Customer-Churn Public

    📊 Customer churn prediction and retention analytics project using Python, machine learning, and Streamlit to identify at-risk customers, uncover churn drivers, and support data-driven product decis…

    Jupyter Notebook

  3. customer-product-journey customer-product-journey Public

    Product analytics dashboard that analyzes the full customer lifecycle — from discovery and conversion to retention and churn prediction — integrating customer analytics, unit economics, growth metr…

    Python

  4. Kaiser_Dashboard Kaiser_Dashboard Public

    End-to-end healthcare analytics project using Python and Streamlit for data cleaning, EDA, KPI development, ETL support, and interactive dashboarding in a healthcare setting.

    Python

  5. CareFlow CareFlow Public

    Product Data Science project demonstrating causal inference, experimentation analysis, interpretable machine learning, and decision-driven analytics for product and growth teams.

    Jupyter Notebook

  6. Telecom_Analysis Telecom_Analysis Public

    End-to-end telecom analytics project using Python and Streamlit for EDA, KPI development, behavioral segmentation, and interactive dashboards.

    Jupyter Notebook