Undergraduate Programme and Module Handbook 2008-2009 (archived)
Module MATH1711: DATA ANALYSIS, MODELLING & SIMULATION
Department: Mathematical Sciences
MATH1711: DATA ANALYSIS, MODELLING & SIMULATION
| Type | Open | Level | 1 | Credits | 20 | Availability | Available in 2008/09 | Module Cap | None. | Location | Durham |
|---|
Prerequisites
- A level in Mathematics subject at grade C or better, or equivalent.
Corequisites
- None.
Excluded Combination of Modules
- Statistics (MATH1541) and Foundation Mathematics (MATH1641) may not be taken with or after this module.
Aims
- The module is a first course in practical data analysis and computer modelling.
- No prior statistical or computing knowledge is assumed.
- The emphasis of the module is upon the understanding of real-life statistical and mathematical problems, and develops the basic concepts and methods by example.
- Most practical sessions are devoted to computing to apply and illustrate the material presented in lectures.
Content
- Sources of data.
- Descriptive statistics.
- Exploring two-variable relationships.
- Methods for more than two variables.
- Data analysis topics.
- Introduction to Maple.
- Smoothing data.
- Discrete Models.
- Continuous Models.
- Stochastic Models.
Learning Outcomes
Subject-specific Knowledge:
- By the end of the module students will: be able to solve a range of predictable or less predictable problems in Data Analysis, Modelling.
- have an awareness of the basic concepts of theoretical mathematics in the field of Data Analysis, Modelling topic areas: Principles of data collection.
- Basic analysis of variance and multiple regression.
- Computing for data analysis.
- Discrete, continuous and stochastic models.
Subject-specific Skills:
- students will have basic mathematical skills in the following areas: Modelling, Computation.
- students will have basic skills in exploratory data analysis and in smoothing data
Key Skills:
Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module
- Lectures demonstrate what is required to be learned and the application of the theory to practical examples.
- Tutorials provide the practice and support in applying the methods to relevant situations as well as active engagement and feedback to the learning process.
- Summative weekly coursework provides an incentive for students to consolidate the learning of material as the module progresses (there are no higher level modules in the department of Mathematical Sciences which build on this module). It serves as a guide in the correct development of students' knowledge and skills, as well as an aid in developing their awareness of standards required.
- The end-of-year written examination provides a substantial complementary assessment of the achievement of the student.
Teaching Methods and Learning Hours
| Activity | Number | Frequency | Duration | Total/Hours | |
|---|---|---|---|---|---|
| Lectures | 40 | 2 per week | 1 Hour | 40 | |
| Tutorials | 10 | 1 every second week | 1 Hour | 10 | ■ |
| Practicals | 17 | 1 per week | 1 Hour | 17 | ■ |
| Preparation and Reading | 133 | ||||
| Total | 200 |
Summative Assessment
| Component: Examination | Component Weighting: 90% | ||
|---|---|---|---|
| Element | Length / duration | Element Weighting | Resit Opportunity |
| Written examination | 3 hours | 100% | |
| Component: Coursework | Component Weighting: 10% | ||
| Element | Length / duration | Element Weighting | Resit Opportunity |
| One written assignment each teaching week | 100% | ||
Formative Assessment:
45 minute collection paper in the first week of Epiphany term.
■ Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University