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Coursebook for Data Science Toolbox
Course Material by Block
Content is arranged by blocks (single week of teaching content). You will not miss any material moving through this sequentially as the reference information is cited throughout.
- 00 About
- Block 01 Introduction and Exploratory Data Analysis
- Block 02 Regression, Testing, and Model Selection
- Block 03 Latent Structure, PCA and Clustering
- Block 04 Non-parametrics and Missing Data
- Block 05 Supervised Learning and Ensembles
- Block 06 Decision Trees and Random Forests
- Block 07 Perceptrons and Neural Networks
- Block 08 Topic Models and Bayesian Methods
- Block 09 Algorithms for Data Science
- Block 10 Parallel Algorithms
- Block 11 Ethics and Privacy
Reference information
- Assessments
- Timetable
- Appendix 1: Preparation
- Appendix 2: Replicability
- Appendix 3: GitHub
- Appendix 4: How to Read a Paper
- Programme Catalogue Course Handbook
- Block 12 Parallel Infrastructure and Spark is unassessed but provided for reference.