This repository is a curated collection of hands-on data science projects tailored for beginners. Whether you're just starting your journey in data science or looking to strengthen your skills, these projects provide a practical and interactive way to apply your knowledge.
3,146
stars
104
commits
Jupyter Notebook
primary language
Jul 29, 2026
updated
README
Beginner Level Data Science Projects
A curated collection of beginner-friendly data science projects with real datasets, clear explanations, and working code. Learn by building.
Table of Contents
Why This Repo?
This repository is designed for anyone getting started with data science -- students, career switchers, and self-learners. Each project is a standalone Jupyter notebook that you can clone and run immediately.
You will learn:
- Data Cleaning and Preprocessing -- preparing real-world messy data
- Exploratory Data Analysis -- visualizations and statistical insights
- Machine Learning -- classification, regression, and anomaly detection
- Deep Learning -- CNNs, transfer learning, and NLP models
- Computer Vision -- detection, recognition, and pose estimation
Categories
Projects are grouped into category folders, each with its own README listing every project in it:
- Classification (19)
- Regression (16)
- Time Series (6)
- NLP (20)
- Clustering (4)
- Recommendation Systems (5)
- Anomaly Detection (2)
- EDA Visualization (10)
- Miscellaneous Applied (10)
- Computer Vision (22)
- Robotics (1)
Learning Path
Start from the top and work your way down. Projects are ordered by difficulty within each level.
Level 1 -- Fundamentals
Get comfortable with pandas, sklearn, and basic ML workflows.
| # | Project | What You'll Learn | Category |
|---|---|---|---|
| 1 | Titanic Survival Prediction | EDA, data cleaning, feature engineering, 7 classifiers, GridSearchCV | Classification |
| 2 | Iris Flower Classification | Multi-class classification on the classic iris measurements, data loading | Classification |
| 3 | Customer Churn | Logistic regression from scratch, prediction on new data | Classification |
| 4 | Heart Failure Prediction | Feature analysis, multiple classifiers, model evaluation | Classification |
| 5 | Rental Prices of AirBnb | Linear regression, outlier analysis, label encoding | Regression |
Level 2 -- Text and NLP
Learn to work with text data, preprocessing pipelines, and NLP techniques.
| # | Project | What You'll Learn | Category |
|---|---|---|---|
| 6 | Message Spam Filtering | TF-IDF, text preprocessing, SVM classification | NLP |
| 7 | Cyber-Bullying Prediction | NLP pipeline, GridSearchCV, model comparison | NLP |
| 8 | Sentiment Analysis | Logistic regression from scratch, Twitter data, NLTK | NLP |
| 9 | AirBnb Reviews Sentimental Analysis | Full NLP pipeline: preprocessing, ML, deep learning, LLMs | NLP |
Level 3 -- Computer Vision and Deep Learning
Work with images, neural networks, and pre-trained models.
| # | Project | What You'll Learn | Category |
|---|---|---|---|
| 10 | Gender Classification | EfficientNetV2, transfer learning, Keras | Computer Vision |
| 11 | Face Detection | Haar cascades, MTCNN, OpenCV | Computer Vision |
| 12 | Face Recognition | LBPH algorithm, real-time webcam recognition | Computer Vision |
| 13 | Eye Disease Detection | ResNet34, data augmentation pipeline, medical imaging | Computer Vision |
| 14 | Alzheimer Detection | Clinical data analysis, Random Forest on medical data | Computer Vision |
Level 4 -- Advanced Topics
Tackle more complex real-world problems.
| # | Project | What You'll Learn | Category |
|---|---|---|---|
| 15 | Network Intrusion Detection System | Ensemble methods, XGBoost, KDD Cup dataset | Anomaly Detection |
| 16 | Object Detection | YOLOv8, Faster R-CNN, RetinaNet, Detectron2 | Computer Vision |
| 17 | Pose Estimation | YOLOv8, MediaPipe, activity classification | Computer Vision |
| 18 | Robotics and Computer Integrated Manufacturing | MobileNetV2, transfer learning, industrial imaging | Robotics |
All Projects
Getting Started
Prerequisites
- Python 3.9+
- Jupyter Notebook or JupyterLab
Core Libraries
pip install pandas numpy matplotlib seaborn scikit-learn jupyter
Additional Libraries (by project type)
| Project Type | Install |
|---|---|
| Deep Learning | pip install tensorflow keras |
| Computer Vision | pip install opencv-python |
| NLP | pip install nltk |
| Object Detection | pip install ultralytics |
Each project has its own requirements.txt for exact dependencies:
cd "Project Folder Name"
pip install -r requirements.txt
jupyter notebook
Quick Start
git clone https://github.com/tkarim45/Beginner-Data-Science-Projects.git
cd Beginner-Data-Science-Projects
# Pick a project and run it
cd "Iris Flower Classification"
pip install -r requirements.txt
jupyter notebook
Contributing
Contributions are welcome! Please read our Contributing Guide before submitting a PR.
Quick rules:
- One project per PR
- Include a README, requirements.txt, and working notebook
- Host large datasets externally (>10 MB)
- Do not commit model binaries
License
This project is licensed under the MIT License -- use it freely for learning, teaching, or building.
If this repo helped you, consider giving it a star -- it helps others find it too.
Contributors
Similar repos
Languages
Jupyter Notebook
99.7%
This repository is a curated collection of hands-on data science projects tailored for beginners. Whether you're just starting your journey in data science or looking to strengthen your skills, these projects provide a practical and interactive way to apply your knowledge.
3,146
stars
104
commits
Jupyter Notebook
primary language
Jul 29, 2026
updated
README
Beginner Level Data Science Projects
A curated collection of beginner-friendly data science projects with real datasets, clear explanations, and working code. Learn by building.
Table of Contents
Why This Repo?
This repository is designed for anyone getting started with data science -- students, career switchers, and self-learners. Each project is a standalone Jupyter notebook that you can clone and run immediately.
You will learn:
- Data Cleaning and Preprocessing -- preparing real-world messy data
- Exploratory Data Analysis -- visualizations and statistical insights
- Machine Learning -- classification, regression, and anomaly detection
- Deep Learning -- CNNs, transfer learning, and NLP models
- Computer Vision -- detection, recognition, and pose estimation
Categories
Projects are grouped into category folders, each with its own README listing every project in it:
- Classification (19)
- Regression (16)
- Time Series (6)
- NLP (20)
- Clustering (4)
- Recommendation Systems (5)
- Anomaly Detection (2)
- EDA Visualization (10)
- Miscellaneous Applied (10)
- Computer Vision (22)
- Robotics (1)
Learning Path
Start from the top and work your way down. Projects are ordered by difficulty within each level.
Level 1 -- Fundamentals
Get comfortable with pandas, sklearn, and basic ML workflows.
| # | Project | What You'll Learn | Category |
|---|---|---|---|
| 1 | Titanic Survival Prediction | EDA, data cleaning, feature engineering, 7 classifiers, GridSearchCV | Classification |
| 2 | Iris Flower Classification | Multi-class classification on the classic iris measurements, data loading | Classification |
| 3 | Customer Churn | Logistic regression from scratch, prediction on new data | Classification |
| 4 | Heart Failure Prediction | Feature analysis, multiple classifiers, model evaluation | Classification |
| 5 | Rental Prices of AirBnb | Linear regression, outlier analysis, label encoding | Regression |
Level 2 -- Text and NLP
Learn to work with text data, preprocessing pipelines, and NLP techniques.
| # | Project | What You'll Learn | Category |
|---|---|---|---|
| 6 | Message Spam Filtering | TF-IDF, text preprocessing, SVM classification | NLP |
| 7 | Cyber-Bullying Prediction | NLP pipeline, GridSearchCV, model comparison | NLP |
| 8 | Sentiment Analysis | Logistic regression from scratch, Twitter data, NLTK | NLP |
| 9 | AirBnb Reviews Sentimental Analysis | Full NLP pipeline: preprocessing, ML, deep learning, LLMs | NLP |
Level 3 -- Computer Vision and Deep Learning
Work with images, neural networks, and pre-trained models.
| # | Project | What You'll Learn | Category |
|---|---|---|---|
| 10 | Gender Classification | EfficientNetV2, transfer learning, Keras | Computer Vision |
| 11 | Face Detection | Haar cascades, MTCNN, OpenCV | Computer Vision |
| 12 | Face Recognition | LBPH algorithm, real-time webcam recognition | Computer Vision |
| 13 | Eye Disease Detection | ResNet34, data augmentation pipeline, medical imaging | Computer Vision |
| 14 | Alzheimer Detection | Clinical data analysis, Random Forest on medical data | Computer Vision |
Level 4 -- Advanced Topics
Tackle more complex real-world problems.
| # | Project | What You'll Learn | Category |
|---|---|---|---|
| 15 | Network Intrusion Detection System | Ensemble methods, XGBoost, KDD Cup dataset | Anomaly Detection |
| 16 | Object Detection | YOLOv8, Faster R-CNN, RetinaNet, Detectron2 | Computer Vision |
| 17 | Pose Estimation | YOLOv8, MediaPipe, activity classification | Computer Vision |
| 18 | Robotics and Computer Integrated Manufacturing | MobileNetV2, transfer learning, industrial imaging | Robotics |
All Projects
Getting Started
Prerequisites
- Python 3.9+
- Jupyter Notebook or JupyterLab
Core Libraries
pip install pandas numpy matplotlib seaborn scikit-learn jupyter
Additional Libraries (by project type)
| Project Type | Install |
|---|---|
| Deep Learning | pip install tensorflow keras |
| Computer Vision | pip install opencv-python |
| NLP | pip install nltk |
| Object Detection | pip install ultralytics |
Each project has its own requirements.txt for exact dependencies:
cd "Project Folder Name"
pip install -r requirements.txt
jupyter notebook
Quick Start
git clone https://github.com/tkarim45/Beginner-Data-Science-Projects.git
cd Beginner-Data-Science-Projects
# Pick a project and run it
cd "Iris Flower Classification"
pip install -r requirements.txt
jupyter notebook
Contributing
Contributions are welcome! Please read our Contributing Guide before submitting a PR.
Quick rules:
- One project per PR
- Include a README, requirements.txt, and working notebook
- Host large datasets externally (>10 MB)
- Do not commit model binaries
License
This project is licensed under the MIT License -- use it freely for learning, teaching, or building.
If this repo helped you, consider giving it a star -- it helps others find it too.
Similar repos
Contributors
Languages
Jupyter Notebook
99.7%