Academic Lab Code Studio
Learn Data Science through 8 hands-on guided projects directly in VS Code.
Academic Lab Code Studio is a VS Code extension that teaches data analysis and machine learning through practical, guided projects. Each project includes step-by-step instructions, executable code cells, and built-in validation.
Features
- 8 Complete Projects covering fundamentals to advanced topics
- Built-in Datasets from Seaborn and Scikit-learn (zero setup)
- Interactive Project Panel showing progress and steps
- One-Click Code Insertion into Jupyter notebooks
- Step Validation to verify your work
- Progress Tracking with achievements
Projects Included
Module 1: Fundamentals (Beginner)
| # |
Project |
Dataset |
Duration |
Topics |
| 1 |
Restaurant Tips Analysis |
Seaborn tips (244 rows) |
2-3h |
EDA, groupby, visualization |
| 2 |
Titanic Survival Analysis |
Seaborn titanic (891 rows) |
3-4h |
Missing data, feature engineering |
| 3 |
Palmer Penguins Comparison |
Seaborn penguins (344 rows) |
3-4h |
Statistical tests, outliers |
| # |
Project |
Dataset |
Duration |
Topics |
| 4 |
Sales Dashboard |
Superstore (9,994 rows) |
4-5h |
Time series, KPIs, dashboards |
| 5 |
Customer Segmentation |
E-commerce transactions |
4-5h |
RFM, K-Means clustering |
| # |
Project |
Dataset |
Duration |
Topics |
| 6 |
House Price Prediction |
California Housing (20K) |
4-5h |
Linear Regression, metrics |
| 7 |
Wine Quality Classification |
Sklearn wine (178 rows) |
4-5h |
Classification, cross-validation |
| 8 |
Flight Passengers Forecast |
Seaborn flights (144 rows) |
5-6h |
Time series forecasting |
Total: ~30 hours of practical content
Installation
From VS Code Marketplace
- Open VS Code
- Go to Extensions (Ctrl+Shift+X)
- Search for "Academic Lab Code Studio"
- Click Install
From VSIX
code --install-extension academic-lab-code-studio-1.0.0.vsix
Quick Start
- Open Command Palette (Ctrl+Shift+P / Cmd+Shift+P)
- Type "Academic Lab: Start Project"
- Select a project from the list
- Follow the steps in the sidebar panel
- Click on each step to insert code into your notebook
- Execute cells and move to the next step
Requirements
- VS Code 1.85.0 or higher
- Python extension for VS Code
- Jupyter extension for VS Code
- Python 3.8+ with:
- pandas
- numpy
- matplotlib
- seaborn
- scikit-learn
Recommended Python Setup
pip install pandas numpy matplotlib seaborn scikit-learn statsmodels
Commands
| Command |
Description |
Shortcut |
Academic Lab: Start Project |
Start a new project |
- |
Academic Lab: Show All Projects |
Browse available projects |
- |
Academic Lab: Insert Current Step |
Insert step code |
Ctrl+Shift+I |
Academic Lab: Next Step |
Go to next step |
Ctrl+Shift+] |
Academic Lab: Previous Step |
Go to previous step |
Ctrl+Shift+[ |
Academic Lab: Validate Step |
Validate current step |
- |
Academic Lab: Browse Datasets |
View available datasets |
- |
Academic Lab: Reset Progress |
Reset project progress |
- |
Settings
| Setting |
Description |
Default |
academicLab.autoInsertFirstStep |
Auto-insert first step when starting |
true |
academicLab.showValidationHints |
Show hints on validation failure |
true |
academicLab.datasetCacheDir |
Directory for downloaded datasets |
datasets |
academicLab.theme |
UI theme (default, minimal, detailed) |
default |
Datasets
Built-in (Zero Setup)
- Seaborn: tips, titanic, penguins, flights, diamonds, iris
- Scikit-learn: california_housing, wine, breast_cancer, diabetes
External (Auto-download)
- Superstore Sales (GitHub)
- Online Retail Transactions (UCI)
Custom
Upload your own CSV files for personalized projects.
Development
Setup
git clone https://github.com/academic-lab/code-studio.git
cd code-studio
npm install
Build
npm run compile
Test
npm test
Package
npm run package
Debug
- Open in VS Code
- Press F5 to launch Extension Development Host
- Test the extension in the new window
Contributing
Contributions are welcome! Please read our Contributing Guide for details.
Adding New Projects
- Create a new folder in
projects/
- Add
project.json with steps and cells
- Test the project end-to-end
- Submit a pull request
License
MIT License - see LICENSE for details.
Support
Acknowledgments
- Seaborn and Scikit-learn for built-in datasets
- The data science community for inspiration
- All contributors and testers
Happy Learning!
Made with love by Academic Lab
