DATABASE ANALYSIS PROJECTS

DATABASE ANALYSIS PROJECTS

To demonstrate practical skills in data analysis, you can develop projects that integrate multiple data sources, perform complex queries, and use predictive models. Projects range in complexity from beginner SQL reports to advanced, end-to-end data pipelines involving real-time analysis. 

Beginner: Foundational skills and basic querying

These projects focus on core database analysis tasks like data cleaning, manipulation, and fundamental SQL queries.

• Analyze sales data for a retail store. Use a dataset from a public repository or create your own with sample data from a fictional store.
  • Key tasks:
    • Calculate total revenue and sales over specific periods.
    • Identify the best-selling products.
    • Analyze sales trends over time, such as daily or monthly sales averages.
  • Skills demonstrated: SELECT, GROUP BY, ORDER BY, and aggregate functions like SUM() and AVG().
• Manage inventory for a bookstore. Build a database to track books, authors, sales, and inventory levels.
  • Key tasks:
    • Determine the most expensive or most popular books.
    • Analyze book sales by city.
    • Simulate inventory updates after a purchase.
  • Skills demonstrated: CREATE TABLE, INSERT, UPDATE, DELETE, and multi-table JOIN commands.

Intermediate: Multi-source integration and analytical reporting

These projects require integrating data from different sources and performing more complex analytical tasks.

• Analyze global happiness and economic trends. Combine a global happiness dataset with economic data to find correlations.
  • Key tasks:
    • Join datasets on country and year to create a combined data source.
    • Explore the relationship between a country's GDP per capita and its happiness ranking.
    • Create visualizations to compare happiness trends over time across different regions.
  • Skills demonstrated: Complex JOINs, statistical analysis, and data visualization.
• Perform customer segmentation for a marketing campaign. Use SQL to analyze customer purchase data and segment them into different groups based on buying behavior.
  • Key tasks:
    • Group customers by their order history, such as total value and frequency of purchases.
    • Identify customers who have only made a single purchase.
    • Recommend promotional offers for specific customer segments.
  • Skills demonstrated: GROUP BY with HAVING, subqueries, and advanced SQL techniques.

Advanced: End-to-end pipelines and predictive analytics

These projects mimic real-world scenarios, involving large datasets, machine learning, and full-stack data workflows.

• Build a real-time fraud detection system. Create an end-to-end data pipeline to analyze financial transactions and detect fraudulent activity.
  • Key tasks:
    • Ingest streaming data using a tool like Apache Kafka and process it with a framework like Apache Spark.
    • Store the data in a non-relational database like MongoDB for flexibility.
    • Use machine learning models to identify fraudulent transactions and send automated alerts.
  • Skills demonstrated: Real-time data streaming, cloud computing (e.g., AWS EC2), machine learning, and database integration.
• Analyze and predict movie box office success. Develop a predictive model based on movie attributes like genre, cast, and marketing spend.
  • Key tasks:
    • Perform web scraping to gather relevant data points.
    • Use Natural Language Processing (NLP) to analyze sentiment from movie reviews.
    • Build a regression model to forecast box office revenue.
    • Create a dashboard to present your findings to stakeholders.
  • Skills demonstrated: ETL (Extract, Transform, Load), NLP, statistical modeling, machine learning, and data visualization.