Hi there! 👋
Welcome to my Portfolio Guide where I provide a walkthrough to all of my data analytics projects and courses.
Feel free to chat with me on LinkedIn about my projects!
Click on the project's title (bold and coloured in Blue) to view my projects! Thank you!
Level: Intermediate SQL
Functions: Aggregations, Joins, CTEs, Window functions (aggregates, ranking, running total, partitioned averages), CASE WHEN statements, subqueries, nested subqueries, DATETIME functions, data type conversion, text and string manipulation
| Project Name | Description | SQL Functions |
|---|---|---|
| 📼 PostgreSQL Data Integration Project | The business report aims to determine the highest grossing postal codes within districts each quarter, which can be used by stakeholders to identify areas for opening new stores or stores that need to be reviewed for closure. The report involves data extraction, table creation, custom transformation, and trigger development using SQL. | SQL, data extraction, data transformation, table creation, trigger development |
| 📻 Sparkify Data Pipline & Database | This project involves building an ETL pipeline to extract data from JSON files containing music streaming data, transforming the data to fit a structured database schema using a star schema, and loading the data into the corresponding tables. | SELECT, INSERT, UPDATE, DELETE, JOIN, GROUP BY, ORDER BY, COUNT, DISTINCT, LIMIT python |
| Guided 📱 Ultimate My SQL BootCamp - Upcoming |
Skills: Data cleaning, wrangling, visualization, analysis, A/B testing
Libraries: pandas, numpy, matplotlib, seaborn, os, glob, scipy
| Project Name | Area | Description | Libraries |
|---|---|---|---|
| 📺 TMDb Movie Analysis | Data Wrangling & EDA | This project is a data analysis of the TMDb Movies dataset using Python, exploring the relationships between variables and identifying insights to inform business decisions. The project analyzes various factors that contribute to a movie's success, such as budget, genre, and release date, and visualizes the findings using Matplotlib and Seaborn. | numpy, pandas, matplotlib, seaborn |
| 📻: Sparkify Data Pipline & Database | Data Wrangling & ETL Pipline | This project involves building an ETL pipeline to extract data from JSON files containing music streaming data, transforming the data to fit a structured database schema using a star schema, and loading the data into the corresponding tables. It also involves data wrangling and cleaning to prepare the data for loading into the database. | os, glob, psycopg2, pandas SQL |
| 💸 CharityML Project - Finding Donors | Supervised Learning | Build and optimize a model to predict income for individuals to target donations for a charity organization. | numpy, matplotlib, scikit-learn, pandas |
| 💻Website A/B Testing Analysis | A/B Testing | Analyze the results of a website A/B test to determine which variation is more effective in terms of user engagement and conversion rate. The project uses statistical methods, including hypothesis testing and confidence intervals, and visualizations to draw insights from the data. | pandas, numpy, matplotlib, seaborn, scipy |
| In progress : Analysis of User Behavior in Entertainment Consumption | Data Analysis & Machine Learning | This study focuses on analyzing user behavior in entertainment consumption across various platforms and formats, including box office movies, adult videos, and streaming platforms. The project involves three sub-projects: analyzing box office trends and user behavior in movie theaters, analyzing adult video views, and analyzing streaming platform usage. | numpy, matplotlib, pandas, seaborn, cikit-learn |
| In progress : Game to Movie Adaptation | Data Wrangling & EDA | This project investigates the impact of movie and TV show adaptations on video game sales and reception. By collecting and analyzing data from various sources, the project explores the relationship between adaptation releases, their quality, marketing efforts, and game sales. The findings aim to inform game developers, film producers, and marketers about strategic decisions for game adaptations and their promotion. | numpy, matplotlib, pandas, seaborn, requests, BeautifulSoup |
| Project Name | Description | Tableau Dashboard |
|---|---|---|
| Explore the US Census 2015 | Tableau dashboard analyzing poverty rates by race and gender across US counties, revealing significant disparities and highlighting the need for tailored interventions. | Poverty Rates by Race and Gender Across US Counties (US Census 2015) |
| Tableau story exploring variations in employment rates across US counties by work type, identifying patterns and disparities in median household income. | Employment Rate Variations by Work Type and Gender in the United States (US Census 2015) |