Portfolio Projects

This repository contains a curated selection of data analysis and visualization projects created to showcase practical skills in Python, SQL, Excel, and Power BI. Each project is structured to be easy to explore and reproduce, with clear descriptions and the source artifacts (scripts, SQL, spreadsheets, and reports) included where applicable.

Contents

• sql_movie_data_analysis/ — SQL-based exploratory analysis of a movie dataset (includes imdb_top_1000.csv and movie_data_analysis.sql).
• sql_covid_data_analysis/ — SQL exploration of COVID-19 cases and vaccinations (includes CovidDeaths.csv, CovidVaccinations.csv, and covid_data_exploration.sql).
• excel_dashboard/ — Excel-based dashboard and workbook(s) demonstrating pivot tables, formulas, and presentation-ready visualizations.
• bi_sales_analysis/ — Power BI sales analysis project featuring interactive dashboards, data modeling, and DAX measures for business intelligence reporting.
• Other folders may contain Python notebooks/scripts, Excel workbooks, and Power BI report files. Projects are grouped by technology and labelled with a short README when needed.

Technologies & Skills Demonstrated

• Python: data cleaning, exploratory data analysis, visualization, and scripting for repeatable workflows.
• SQL: data extraction, aggregation, window functions, and writing reproducible analysis queries.
• Excel: structured analysis, pivot tables, formulas, and dashboarding for quick business insights.
• Power BI: interactive reports, data modelling, measures (DAX), and visual storytelling.

Highlights

• Movie data analysis (SQL): A focused example demonstrating how to ingest a CSV dataset, perform exploratory queries, rank and aggregate movie information, and prepare the results for visualization or reporting. See sql_movie_data_analysis/movie_data_analysis.sql and sql_movie_data_analysis/imdb_top_1000.csv for the dataset and queries.

• COVID-19 data exploration (SQL): An end-to-end analytical exercise using the CovidDeaths.csv and CovidVaccinations.csv datasets (see sql_covid_data_analysis/covid_data_exploration.sql). The analysis demonstrates practical techniques for real-world data: joins between cases and vaccination tables, data cleansing and type conversion, rolling aggregates and window functions to compute cumulative vaccinations, use of CTEs and temporary tables for intermediate calculations, and the creation of views to support downstream visualizations. Example outputs include percentage of population infected, deaths per population, and percent of population vaccinated — all prepared for reporting or integration into Power BI dashboards.

• Other demonstrative work (Python/Excel/Power BI): Python notebooks and scripts illustrate data preparation and visualization workflows; Excel workbooks show pivot-table-driven analysis and dashboards; Power BI files (where included) present interactive reports built from prepared datasets.

• Excel dashboard (Excel): A dedicated workbook demonstrating end-user focused analysis and storytelling. The project includes structured data sheets, pivot tables for fast aggregation, formula-driven KPIs, charting for trend and comparison views, and a presentation-ready dashboard layout suitable for stakeholders. See the excel_dashboard/ folder for the workbook(s) and notes on recreating or extending the dashboard.

• BI sales analysis (Power BI): An interactive sales dashboard built in Power BI that showcases data modeling techniques, DAX measures for key performance indicators, and visual storytelling for business stakeholders. The project demonstrates the ability to connect multiple data sources, create relationships, build calculated columns and measures, and design intuitive reports that enable data-driven decision making. Explore the bi_sales_analysis/ folder for the Power BI report file and supporting documentation.

How to explore this repository

1. Clone or download the repository to your machine.

2. Inspect the folder structure and open project-level README files where present.

3. For SQL projects: open the .sql files in your preferred SQL client or a text editor. The imdb_top_1000.csv file is included as a sample dataset in sql_movie_data_analysis/.

4. For Python projects: use a virtual environment, install dependencies (if a requirements.txt is present), and run Jupyter notebooks or Python scripts.

   • Example (optional):

     python -m venv .venv; .\.venv\Scripts\Activate.ps1; pip install -r requirements.txt

5. For Excel workbooks: open with Microsoft Excel or a compatible viewer.

6. For Power BI reports: open .pbix files with Power BI Desktop to interact with the visualizations.

Contributing

This repository is intended to showcase individual portfolio work. If you would like to contribute enhancements or sample data, please open an issue or submit a pull request and include a short description of your proposed change.

Thank you for reviewing this portfolio. The projects here are designed to demonstrate a practical, end-to-end approach to data analysis: collecting and preparing data, extracting insights with SQL and Python, and presenting results with Excel and Power BI.