Data Science Intensive
Data Science Intensive Syllabus
Day 1 - Python & the Environment
- Demonstration
- Extending the model
- Environment setup
- Students replicate the model
- Model tinkering
- Python idioms
- Python practice
- Further environment setup: Github, virtualenvs etc.
- Advanced Python
- Advanced Python practice
Day 2 - Machine Learning Theory & Practice
- Supervised Learning
- Linear Regression
- Classification
- Decision Trees
- K-Nearest Neighbours
- Support Vector Machines
- Exercises
Day 3 - Data Wrangling
- Pandas and raw files
- Introduction to relational databases and SQL
- SQL exercises
- Cleaning messy data
- NoSQL: motivation and survey
Day 4 - Clustering, Dimensionality Reduction
- Introduction to Unsupervised Learning
- k-means
- Principal Component Analysis
- Cross-validation
Day 5 - Advanced Machine Learning
- Logisitic Regression
- Bayesian Techniques
- Random Forests
- Gradient Boosted Trees
- Feature Engineering
- Regularisation
