Data Science Learning Path
Estimated Total Duration: 12-18 months (studying 15-20 hours per week)
Milestone 1: Programming Foundations
Duration: 2-3 months
Python Programming Fundamentals
- Variables, data types, and basic operations
- Control structures (if/else, loops)
- Functions and modules
- Object-oriented programming basics
- File handling and basic I/O
Capstone Projects:
- Command-line calculator
- Simple data processing script
- Basic object-oriented system (e.g., library management)
Milestone 2: Mathematics and Statistics
Duration: 2-3 months
Essential Mathematics
- Linear algebra fundamentals
- Calculus basics
- Probability theory
- Descriptive statistics
- Inferential statistics
Statistical Programming
- NumPy for numerical computing
- Basic statistical analysis with SciPy
- Hypothesis testing
- Probability distributions
- Confidence intervals
Capstone Projects:
- Statistical analysis report on real-world dataset
- A/B testing simulation
- Probability modeling application
Milestone 3: Data Analysis and Visualization
Duration: 2-3 months
Data Manipulation
- Pandas for data manipulation
- Data cleaning and preprocessing
- Feature engineering
- Data aggregation and grouping
- Working with different data formats
Data Visualization
- Matplotlib fundamentals
- Seaborn for statistical visualization
- Plotly for interactive visualizations
- Dashboard creation with Streamlit
Capstone Projects:
- Exploratory data analysis report
- Interactive dashboard
- Data cleaning pipeline
Milestone 4: Machine Learning Fundamentals
Duration: 3-4 months
Supervised Learning
- Linear regression
- Logistic regression
- Decision trees
- Random forests
- Support vector machines
- Model evaluation and validation
Unsupervised Learning
- Clustering (K-means, hierarchical)
- Dimensionality reduction
- Principal Component Analysis
- Pattern recognition
Tools and Libraries
- Scikit-learn
- Feature selection techniques
- Cross-validation
- Hyperparameter tuning
Capstone Projects:
- House price prediction model
- Customer segmentation analysis
- Classification model for real-world problem
Milestone 5: Advanced Topics
Duration: 3-4 months
Deep Learning
- Neural network basics
- TensorFlow/Keras
- Convolutional neural networks
- Recurrent neural networks
- Transfer learning
Big Data and Production
- SQL and database management
- Big data concepts
- Basic cloud computing (AWS/GCP)
- Model deployment
- API development with Flask/FastAPI
Additional Skills
- Time series analysis
- Natural Language Processing basics
- Model optimization
- A/B testing in production
Capstone Projects:
- Deep learning image classifier
- End-to-end ML pipeline
- Deployed model with API
Final Capstone Project
Duration: 1-2 months
- Comprehensive data science project combining multiple skills
- End-to-end implementation from data collection to deployment
- Documentation and presentation
- GitHub portfolio preparation
Continuous Learning Elements
- Participate in Kaggle competitions
- Contribute to open-source projects
- Join data science communities
- Read research papers
- Attend workshops and webinars
Assessment Criteria for Each Milestone
- Complete all projects
- Create documentation for each project
- Pass milestone quizzes
- Code review assessment
- Practical application demonstration
Resources
Learning Platforms
- Coursera
- edX
- DataCamp
- Fast.ai
- Kaggle Learn
Books
- "Python for Data Analysis" by Wes McKinney
- "Introduction to Statistical Learning"
- "Hands-On Machine Learning with Scikit-Learn"
- "Deep Learning" by Ian Goodfellow
Communities
- Stack Overflow
- GitHub
- Reddit (r/datascience, r/machinelearning)
- LinkedIn groups
- Local data science meetups