Scenario:
You've been hired as a junior database designer for a startup that is rapidly scaling. Your task is to create an ERD (Entity-Relationship Diagram) for a new e-commerce application that connects customers, orders, products, reviews, and shipments. However, you aren't quite sure what the best practices are for designing scalable relationships.
Task:
Use a generative AI tool (like ChatGPT or Bard) to outline best practices for database design, such as normalization, foreign key setup, and indexing strategies.
Once you've gathered insights, sketch your ERD using an online diagram tool.
After sketching, validate your ERD using AI to see if there are any areas that could be improved (e.g., unnecessary redundancy, missing relationships).
Time Limit: 1 hour.
Deliverable: A clean, scalable ERD with written documentation explaining each relationship, attributes, and why certain decisions were made.
Learning Goals:
Demonstrate how AI can help in decision-making processes for design.
Understand basic concepts of database normalization.
Get feedback from AI on the quality of their design.
Scenario:
The company you work for is experiencing slow performance with some critical reports, and your boss asks you to optimize SQL queries to make them run faster. Your task is to use AI tools to help rewrite, test, and optimize the SQL queries from a sample dataset.
Task:
You will receive a set of suboptimal SQL queries and the corresponding database schema.
Your job is to leverage an AI tool like ChatGPT to rewrite and optimize each query.
Run the AI-suggested optimized queries and compare their performance using tools like MySQL Workbench or SQL Server.
Note down the differences in runtime between the original and optimized versions and document what made the AI-revised query more efficient.
Time Limit: 50 minutes.
Learning Goals:
Understand the fundamentals of SQL query optimization.
Learn how AI can assist in improving query logic and performance.
Develop an appreciation for performance metrics in SQL queries.
Scenario:
Your manager wants a quick insight into the sales trends of the past quarter. Instead of doing the usual manual data analysis, you decide to use an AI tool to help you generate meaningful charts and visualizations.
Task:
Use a dataset (preloaded in Excel or CSV).
Employ AI to help you choose which charts best convey certain insights from the data, e.g., total revenue over time, customer segment performance, or product category popularity.
You can use a combination of tools like Excel, Power BI, or even GPT-based plugins to help you generate these visualizations.
Once you've created the visualizations, use AI to help you draft a short presentation summarizing the key trends and their potential implications for the company.
Time Limit: 1 hour.
Learning Goals:
Learn how to use AI to turn data into stories.
Understand which visual tools are appropriate for different kinds of data.
Develop presentation skills for sharing data insights.
Scenario:
Your team has been tasked with reviewing the security of your current database setup. You’ve recently heard about some data breaches and want to ensure your company's data is secure.
Task:
Research common database security vulnerabilities using GenAI tools.
With the help of AI, compile a checklist of best practices for database security, including things like encryption, access control, and user permissions.
Apply those best practices to a provided database schema.
Write a brief report outlining what actions should be taken to secure the database based on your findings.
Time Limit: 1 hour and 15 minutes.
Learning Goals:
Understand how AI can assist with security assessment.
Learn about database vulnerabilities and common security solutions.
Practice how to present security recommendations.
Scenario:
As a data analyst, you’re often asked to debug SQL queries that have errors. Today, you’ve been handed a script from a colleague that isn’t running as expected, and you’re expected to debug it within a strict deadline.
Task:
Use AI tools to understand and debug an SQL script that contains multiple syntax and logical errors.
Document each correction you make and explain why the initial query was incorrect and how you fixed it.
Time Limit: 45 minutes.
Learning Goals:
Practice debugging skills.
Learn how AI can help identify issues in SQL queries.
Strengthen understanding of error types in SQL and effective resolutions.
Note: adapted from Dr. Alex Osterwalder, Strategyzer
Scenario:
You are a freelance data analyst and have been hired to analyze a client's dataset. Instead of working directly with your client, you will use a chatbot like ChatGPT acting as the client to simulate the requirements gathering process.
Task:
The AI (ChatGPT or another GenAI tool) will act as your client, providing ambiguous or evolving requirements.
You must interact with the AI, ask relevant questions, understand requirements, and prepare an analysis plan based on the dataset.
You'll need to show the client (AI) your progress through simple queries, visualizations, or summaries and adapt to their feedback.
Time Limit: 1 hour and 15 minutes.
Learning Goals:
Understand the importance of requirement gathering in data analysis.
Develop communication skills to clarify client needs.
Use AI as a practice tool for improving client interaction skills.
These activities are designed to be engaging, challenging, and reflective of real-world situations, ensuring that students understand how to use generative AI tools to augment their skill set. The goal is to make them comfortable with AI-assisted problem-solving, emphasizing critical thinking, efficiency, and adaptability.