Real-World SQL Projects: 5 Hands-On Case Studies

Import data to SQL Server by using SQL Server Management Studio to create a table, insert rows, and run a query to view IPL players' names, teams, prices, and roles.

Explore data with SQL case studies by computing total team spending with group by, listing top three all-rounders, and identifying each team's highest priced player via max price and join.

Rank players by price within each team using a window function to list the top two; identify the most expensive players and compute each player's percentage contribution to team spending.

Learn to classify players by price into high, medium, and low; count by team; compare Indian and overseas averages; and find top earners by role with subqueries.

Publish a SQL case study's files to GitHub by creating a public repository, adding a readme, uploading DDL and DML SQL files, and committing changes to showcase on your resume.

Import and inspect 2024 t20 cricket match data in SQL Server. Load fields like team one, team two, winner, margin, date, and ground, then run select queries for data exploration.

Explore 2024 cricket data to identify matches between India and South Africa, use where with and/or, compute top wins with group by, then rank teams by wins using window functions.

Explore data exploration techniques on cricket margins, filtering runs and excluding wickets. Compute average margins by winner, group by winner, extract numeric values, and rank teams chasing targets.

Explore head-to-head records between two teams using variables, grouping, unions, and joins; identify month with the most T20i matches in 2024 and compute win percentages with CTEs and window ranking.

Publish your SQL case study files to a public GitHub repository by creating repo, adding readme, uploading data exploration, table creation, and insertion files, and committing changes for resume URL.

Learn to import flight data into SQL Server and explore five related tables: passengers, flights, airports, tickets, and airlines, using joins, case statements, and window functions.

Learn to identify the busiest airport by joining flights and airports, grouping by airport name, and counting takeoffs; also explore tickets, airlines, and Indigo flights with origin and destination airports.

Propel real-world sql projects by identifying the top airline by departures per origin, categorizing flight duration with case statements, and computing each passenger's first and last flight dates with totals.

Learn data exploration with real-world sql projects by solving q7 to q10, including top-priced routes, highest spenders by frequent flyer status, most frequently used airline, and airline revenue.

Publish files to a GitHub repository named sql_case_study_flights, include Readme, upload data exploration and table-creation files, commit changes, and share the repo URL; concepts include join, CTE, and window function.

Import hospital data into SQL Server to build a star schema with a patient visits fact table and surrounding dimension tables, performing data cleaning and year-by-year merging.

Remove null first names, format names to proper case, create full name, split city state country, standardize gender, and prepare dim_department_clean and unified patient_visit via union all.

Perform three data exploration sql case studies: count distinct patients per doctor, split revenue and visits by payment method, and analyze average bill amounts by patient age groups.

Explore computing department revenue and visits, ranking departments by revenue within each category, and calculating average satisfaction and wait time per department through joins and groupings.

Explore data exploration techniques in real-world SQL projects by comparing weekday vs weekend hospital visits, monthly totals with cumulative sums, doctors' average satisfaction, and most prescribed treatments per diagnosis.

Publish the SQL project files to GitHub by saving the file, naming it hospital_data_exploration, creating a repository, adding SQL files, and committing changes to share the URL in your resume.

Import the superstore sales data into SQL Server by creating and populating the customer, product, shipping, sales, and target sales tables, then prepare to tackle questions related to this data.

Explore data exploration in real-world SQL projects by identifying top ten customers by total sales, computing monthly 2023 sales, and detecting products never sold using left joins.

Compute 2022 new customers by first-order dates with a 2022 filter, then determine category profit margins from profit and sales, and present date-wise category sales with running totals.

Explore real-world SQL projects by analyzing latest customer orders with window functions, classifying customers by total sales, identifying top products per category, and comparing actual versus target sales using unpivot.

Publish SQL files to GitHub by saving the file as superstore_data_exploration, creating a new repository with a README, uploading all files, and committing changes; share the URL for resume.

Transform aspiring analysts into SQL wizards by exploring five real-world case studies, teaching data cleaning, pattern recognition, and answering critical business questions with SQL.