Undergraduate Course: Data, Algorithms and Decisions (MATH08076)
Course Outline
| School | School of Mathematics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 8 (Year 1 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | The course focuses on applications, with case studies of real-world problems and data, and guest lectures from practitioners from inside and outside the university. |
| Course description |
Data :
1. Graphical and Tabular Representations of Data
2. Descriptive Statistics
3. Samples and Populations
4. Inference
5. Applications
Decisions and Algorithms :
6. Mathematical Modelling for Decision Making
7. Graphical Approach to Linear Programming
8. Integer Constraints
9. Algorithms
10. Nonlinear Optimization: Basics and Algorithms
1. Data : Graphical and tabular presentations and inferential reasoning from data. Areas of application that could be included are gene expressions; forensic statistics; extremes; ecology; environment; astronomy; medicine and biology.
2. Algorithms : The idea of an algorithm, representation of algorithms, importance of execution speed and memory usage. Examples to come mainly from section 3 below.
3. Decisions : Fundamentals of Operational Research / Decision Mathematics in wide use in the modern world with an emphasis on mathematical modelling of real world problems, ie., translating decision making problems into the mathematical language. The ideas will be applicable in numerous sectors in the economy including but not limited to airlines, healthcare, travel, telecommunication and entertainment.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | Mathematics : Grade A at Highers or A-level |
Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
| Not being delivered |
Learning Outcomes
On completion of this course, the student will be able to:
- translate decision making problems and problems involving data to mathematical language.
- use basic graphical and tabular methods for presentation of data and their interpretation.
- select and use appropriate algorithms to solve real world problems.
- use spreadsheet applications and specialized software (such as R) to solve optimization and statistical problems.
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Reading List
F.S. Hillier and G. J. Lieberman - Introduction to Operations Research, McGraw-Hill Higher Education, 2009 (ISBN: 978-0071324830)
H. A. Taha - Operations Research: An Introduction (9th Edition) Pearson, 2010, (ISBN: 978-0132555937)
W. L. Winston - Operations Research: Application and Algorithms Brooks/Cole, 1998 (ISBN: 978-0534380588)
B. Goldacre - I Think You Will Find It's a Bit More Complicated Than That, Fourth Estate, 2014 (ISBN: 978-0007462483)
B. Goldacre - Bad Science, Harper Perennial, 2009 (ISBN: 978-0007284870)
D. Cox and C. Donnelly - Principles of Applied Statistics. Cambridge University Press, 2009 (ISBN: 978-1858059082)
G. M. Clarke and D. Cooke - A Basic Course in Statistics (5th revised edition), John Wiley, 2004 (ISBN: 978-0340814062) |
Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | DAD |
Contacts
| Course organiser | Dr Burak Buke
Tel: (0131 6)50 5086
Email: b.buke@ed.ac.uk |
Course secretary | Ms Louise Durie
Tel: (0131 6)50 5050
Email: L.Durie@ed.ac.uk |
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