Data Analysis Dojo - Mastery Edition

A gamified learning web app for data analysis with Python, following Learn->Practice->Apply->Assess structure, using core Python libraries (Pandas, NumPy, Matplotlib, Scikit-learn, NLTK) with focus on skill mastery and auto-gradable exercises.

This is a Next.js project bootstrapped with create-next-app.

Project Overview

Data Analysis Dojo is a web application designed to help users master data analysis skills through a structured, gamified learning experience. The application follows a Learn -> Practice -> Apply -> Assess (L->P->A->A) cycle for each module.

Key Features

• Structured Learning Path: Content follows a Learn -> Practice -> Apply -> Assess cycle per module
• Skill Matrix: Track proficiency levels (Novice, Apprentice, Journeyman, Master) for specific skills
• Gamification: Earn XP, level up, and earn belts by completing activities and passing assessments
• Interactive Coding: Practice Python data analysis skills with an integrated code editor
• Auto-Grading: Automated evaluation of code submissions for immediate feedback
• AI-Powered Assistance: Optional Gemini API integration for code explanations and concept elaboration

Tech Stack

• Frontend: Next.js with TypeScript and Tailwind CSS
• Backend: Next.js API routes
• Database: PostgreSQL with Prisma ORM
• Authentication: NextAuth.js
• Code Editor: Monaco Editor
• AI Integration: Google Gemini API (optional)

Features

• Interactive learning modules for data analysis
• AI-powered explanations using the Gemini API
• PDF upload and analysis for data science documents
• Skill tracking and progress visualization
• Gamified learning experience

Getting Started

Prerequisites

• Node.js (v18 or later)
• PostgreSQL database

Installation

1. Clone the repository:

   git clone https://github.com/bigmanTariq/data-ai-idk.git
   cd data-analysis-dojo
   

2. Install dependencies:

   npm install
   

3. Set up environment variables: Create a .env file in the root directory with the following variables:

   DATABASE_URL="postgresql://username:password@localhost:5432/data_analysis_dojo?schema=public"
   NEXTAUTH_URL="http://localhost:3000"
   NEXTAUTH_SECRET="your-nextauth-secret-key"
   ENCRYPTION_KEY="your-encryption-key-for-api-keys"
   ENCRYPTION_IV="your-encryption-iv"
   

4. Set up the database:

   npm run prisma:migrate
   npm run prisma:generate
   

5. Seed the database with sample data:

   npm run prisma:seed
   

6. Start the development server:

   npm run dev
   

7. Open http://localhost:3000 in your browser.

Demo Account

You can use the following demo account to explore the application:

• Email: [email protected]
• Password: password123

Project Structure

• /src/app: Next.js app router pages
• /src/components: React components
• /src/lib: Utility functions and shared code
• /prisma: Database schema and migrations

Learning Path Structure

The application follows a structured learning path:

1. Learn: Conceptual quizzes to test understanding of core concepts
2. Practice: Focused coding exercises targeting specific skills
3. Apply: Integrated challenges combining multiple skills
4. Assess: Comprehensive tests to demonstrate mastery (Belt Tests)

Gemini API Integration

The application includes optional integration with Google's Gemini API to provide AI-powered assistance:

• Code Explanations: Get detailed explanations of code snippets during practice exercises
• Concept Elaboration: Receive in-depth explanations of data analysis concepts
• Alternative Approaches: Explore different ways to solve coding problems

Setting Up Gemini API

1. Obtain a Gemini API key from Google AI Studio
2. Log in to the Data Analysis Dojo application
3. Navigate to Settings > Gemini API Integration
4. Enter your API key (stored securely and encrypted)

Note: The Gemini API integration is entirely optional. All core learning features work without it.

Learn More

• Next.js Documentation - learn about Next.js features and API.
• Learn Next.js - an interactive Next.js tutorial.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.