Hi there! 👋
Welcome to my Portfolio Guide, where I provide a walkthrough of all my data analytics projects and courses.
Feel free to chat with me on LinkedIn about my projects!
These projects reflect practical, real-world applications that deliver results for businesses, stakeholders, and decision-makers.
| Project Name | Description | SQL Functions |
|---|---|---|
| 📼 PostgreSQL Data Integration Project | A business report aiming to determine the highest-grossing postal codes within districts each quarter. Data extraction, transformation, and trigger development were key elements. Impact: Enabled stakeholders to identify high-potential areas for new store locations. | SQL, data extraction, data transformation, table creation, trigger development |
| 📻 Sparkify Data Pipeline & Database | Building an ETL pipeline to extract and transform data for a music streaming service. Structured database schema using a star schema and SQL queries. Impact: Streamlined data flow and enhanced data accuracy for internal reporting. | SELECT, INSERT, UPDATE, DELETE, JOIN, GROUP BY, ORDER BY, COUNT, DISTINCT, LIMIT, Python |
| Project Name | Area | Description | Libraries |
|---|---|---|---|
| 📻 Sparkify Data Pipeline & Database | Data Wrangling & ETL Pipeline | An ETL pipeline for extracting data from JSON files, transforming it for a star schema database, and loading it into corresponding tables. Impact: Improved data quality and created a scalable ETL solution. | os, glob, psycopg2, pandas, SQL |
| In progress : Analysis of User Behavior in Entertainment Consumption | Data Analysis & Machine Learning | A study analyzing user behavior across various entertainment platforms and formats, including box office movies, adult videos, and streaming services. Outcome: Insights will inform media companies about content trends and audience behavior. | numpy, matplotlib, pandas, seaborn, scikit-learn |
| Project Name | Description | Tableau Dashboard |
|---|---|---|
| Explore the US Census 2015 | Tableau dashboard analyzing poverty rates by race and gender across US counties. Outcome: Visualized disparities, helping policymakers consider interventions. | Poverty Rates by Race and Gender Across US Counties |
| Tableau story exploring employment rate variations across US counties by work type. Outcome: Highlighted employment and income disparities, guiding HR policy planning. | Employment Rate Variations by Work Type and Gender |
These projects showcase my ability to learn and apply key data analytics skills in a structured environment. They reflect self-guided learning and exploration of various tools and techniques.
| Project Name | Area | Description | Libraries |
|---|---|---|---|
| 📺 TMDb Movie Analysis | Data Wrangling & EDA | This project explores relationships in the TMDb Movies dataset using Python, focusing on identifying insights for decision-making. Key Learning: Advanced data wrangling and visualization techniques. | numpy, pandas, matplotlib, seaborn |
| 💸 CharityML Project - Finding Donors | Supervised Learning | Build and optimize a model to predict income for targeting potential donors for a charity organization. Key Learning: Machine learning model optimization. | numpy, matplotlib, scikit-learn, pandas |
| 💻 Website A/B Testing Analysis | A/B Testing | Analyze website A/B test results to determine which variation leads to better user engagement and conversion rates, using statistical methods like hypothesis testing and confidence intervals. Key Learning: Hypothesis testing, decision-making with data. | pandas, numpy, matplotlib, seaborn, scipy |
| In progress : Game to Movie Adaptation | Data Wrangling & EDA | Investigates the impact of movie and TV adaptations on video game sales and reception, exploring the relationship between adaptations and game sales. Key Learning: Exploratory Data Analysis (EDA) and data collection using web scraping. | numpy, matplotlib, pandas, seaborn, requests, BeautifulSoup |