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 Topic Models and Bayesian Methods
• Block 08 Algorithms for Data Science
• Block 09 Perceptrons and Neural Networks
• Block 10 Parallel Algorithms
• Block 11 Parallel Infrastructure and Spark
• Block 12 Ethics and Privacy

Reference information

• Assessments
• Timetable
• Appendix 1: Preparation
• Appendix 2: Assessment List
• Appendix 3: Replicability
• Appendix 4: GitHub
• Appendix 5: Bluecrytal High Performance Computing
• Appendix 6: How to Read a Paper
• Programme Catalogue Course Handbook