Project Ideas in SQL

Create a portfolio.

Interesting SQL projects, along with their titles, difficulty levels, descriptions, questions to solve, and links for data sources:

Customer Churn Analysis

  • Difficulty Level: Intermediate
  • Description: Analyze customer behavior and identify factors contributing to customer churn in a telecommunications company.
  • Questions to Solve:
    • What are the common characteristics of churned customers?
    • Which services or products are most associated with churn?
    • Can we predict churn using historical data?
  • Data Source: Telco Customer Churn Dataset on Kaggle

Employee Performance and Retention

  • Difficulty Level: Intermediate
  • Description: Analyze employee performance data to identify patterns and factors leading to employee retention and turnover.
  • Questions to Solve:
    • What factors correlate with high employee performance?
    • How does employee tenure affect retention rates?
    • Can we predict employee turnover based on performance metrics?
  • Data Source: Employee Performance Dataset on Kaggle

Sales Data Analysis

  • Difficulty Level: Intermediate
  • Description: Analyze sales data from a retail store to understand sales trends, customer preferences, and product performance.
  • Questions to Solve:
    • Which products generate the most revenue?
    • How do sales vary by season or month?
    • What is the average customer spend per transaction?
  • Data Source: Sales Dataset on Kaggle

COVID-19 Data Analysis

  • Difficulty Level: Intermediate
  • Description: Analyze the impact of COVID-19 across different countries using public health data.
  • Questions to Solve:
    • How does the infection rate vary by region?
    • What trends can be observed over time in vaccination rates?
    • Which countries had the highest mortality rates?
  • Data Source: COVID-19 Dataset on Kaggle

Movie Recommendation System

  • Difficulty Level: Advanced
  • Description: Build a movie recommendation system using user ratings and movie metadata.
  • Questions to Solve:
    • How can we identify similar movies based on user ratings?
    • What features of a movie (genre, director) influence its rating?
    • Can we predict a user’s rating for an unseen movie?
  • Data Source: MovieLens Dataset

E-commerce Product Review Analysis

  • Difficulty Level: Advanced
  • Description: Analyze product reviews to determine factors influencing customer satisfaction and product ratings.
  • Questions to Solve:
    • What are the common keywords in positive vs. negative reviews?
    • How do review scores correlate with product features?
    • Can we predict future product ratings based on historical reviews?
  • Data Source: Amazon Product Review Dataset on Kaggle

Restaurant Performance Analysis

  • Difficulty Level: Intermediate
  • Description: Analyze restaurant sales and review data to understand factors that contribute to a restaurant's success.
  • Questions to Solve:
    • What factors (e.g., location, cuisine type) are associated with higher ratings?
    • How do promotional offers impact sales?
    • Which menu items are most popular?
  • Data Source: Yelp Dataset Challenge

Public Transportation Analysis

  • Difficulty Level: Advanced
  • Description: Analyze public transportation data to understand usage patterns and identify areas for improvement.
  • Questions to Solve:
    • What times of day see the highest ridership?
    • How does weather affect public transportation usage?
    • Can we identify routes that consistently underperform?
  • Data Source: Chicago Transit Authority Ridership Data

Stock Market Analysis

  • Difficulty Level: Advanced
  • Description: Analyze historical stock prices to identify trends and patterns in the stock market.
  • Questions to Solve:
    • How do stock prices change in response to market events?
    • What is the correlation between different stocks?
    • Can we predict future stock prices using historical data?
  • Data Source: Yahoo Finance API

Customer Segmentation

  • Difficulty Level: Intermediate
  • Description: Segment customers based on purchasing behavior and demographic data to target marketing efforts effectively.
  • Questions to Solve:
    • What are the main characteristics of each customer segment?
    • How can we tailor marketing campaigns based on customer segments?
    • What segments show the highest potential for upselling?
  • Data Source: Online Retail Dataset on UCI Machine Learning Repository