Unit name | Data Science Toolbox |
---|---|
Unit code | MATHM0029 |
Credit points | 20 |
Level of study | M/7 |
Teaching block(s) |
Teaching Block 4 (weeks 1-24) |
Unit director | Dr. Lawson |
Open unit status | Not open |
Pre-requisites |
Probability 1, Statistics 1 and Statistics 2 (or equivalent) |
Co-requisites |
Introduction to Mathematical Cybersecurity |
School/department | School of Mathematics |
Faculty | Faculty of Science |
This unit will be partly assessed by coursework with a focus on real cybersecurity datasets.
ILO1 Be able to access and process cyber security data into a format suitable for mathematical reasoning
ILO2 Be able to use and apply basic machine learning tools
ILO3 Be able to make and report appropriate inferences from the results of applying basic tools to data
ILO4 Be able to use high throughput computing infrastructure and understand appropriate algorithms
ILO5 Be able to reason about and conceptually align problems involving real data to appropriate theoretical methods and available methodology to correctly make inferences and decisions
2 lectures per week for 12 weeks, plus a 2 hour biweekly practical. Lectures include 24 hours of new material.
Ongoing practical assignments (50%) - marked on best 4 out 5 ongoing practical assessments. Practicals will be held every two weeks and will be submitted biweekly. The first practical is formative, the remaining 5 are also assessed.
Exam (50%) - to assess the underlying ideas and interpretation
All ILOs will be examined across the pieces of coursework, though some pieces of coursework will focus more on one particular ILO.
Tukey, J. W. Exploratory data analysis, Addison-Wesley, 1977.
Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. The elements of statistical learning. (2nd edition) Springer New York, 2009.
Karau, H., A. Konwinski, P. Wendell and M. Zaharia. Learning spark: lightning-fast big data analysis, O'Reilly Media, 2015
Scikit online tutorialsl: http://scikit-learn.org/stable/tutorial/index.html