Module Database Search



MODULE DESCRIPTOR
Module Title
Problem Solving and Modelling
Reference CM1102 Version 5
Created June 2022 SCQF Level SCQF 7
Approved July 2016 SCQF Points 30
Amended July 2022 ECTS Points 15

Aims of Module
To provide students with the understanding of problems which arise in computing situations and the practical skills for the effective modelling and efficient computational solution of these problems. To develop both the relevant skills for constructive use of conceptual modelling and design in a variety of settings, and a range of transferable skills to be of value in employment.

Learning Outcomes for Module
On completion of this module, students are expected to be able to:
1 Recognise and solve simple computational problems using appropriate mathematical techniques.
2 Apply basic statistical techniques and derive measures of probability for a given set of data.
3 Describe the steps involved in modelling systems and processes.
4 Apply analytical and modelling skills to a range of problems relevant to computing and digital media domains.
5 Demonstrate skills in problem solving and effective communication in a variety of settings, relevant to future study, research and in the workplace.

Indicative Module Content
Problem solving techniques and the modelling process. Categories of data: discrete, continuous, qualitative. Categories of problem type: analytic, computational, geometric, probabilistic. Coordinate geometry: graph drawing, straight lines, parabolas and other functions. Parameterisation. Distance metrics. Transformations: matrices, vectors, matrix-vector multiplication, types of transformations. Functions: multivariate and iterating functions, computational complexity, logarithms, exponentials, trigonometric. Sets and relationships between sets. Sequences and series and their relationship to iteration and loops. Descriptive statistics: Measures of central tendency and dispersion, linear regression and correlation, rank correlation. Probability: Probability trees, enumeration, commonly used probability distributions, and fault analysis, random number generation. Boolean Algebra: Boolean variables and operators, Boolean identities. Application of Modelling Techniques to real world examples, and for conceptual modelling within a range of situations relevant to computing and digital media domains. Use of computational tools and packages.

Module Delivery
This module is delivered throughout the teaching session using a mixture of lectures, tutorials and computer laboratory sessions (where appropriate).

Indicative Student Workload Full Time Part Time
Contact Hours 80 N/A
Non-Contact Hours 220 N/A
Placement/Work-Based Learning Experience [Notional] Hours N/A N/A
TOTAL 300 N/A
Actual Placement hours for professional, statutory or regulatory body    

ASSESSMENT PLAN
If a major/minor model is used and box is ticked, % weightings below are indicative only.
Component 1
Type: Coursework Weighting: 50% Outcomes Assessed: 3, 4, 5
Description: A coursework comprising a report and presentation on modelling exercises.
Component 2
Type: Coursework Weighting: 50% Outcomes Assessed: 1, 2
Description: A coursework comprising individual class quizzes.

MODULE PERFORMANCE DESCRIPTOR
Explanatory Text
The module is assessed on a pass/unsuccessful basis. The Module Grade is based on performance in Component 1 (coursework) and Component 2 (coursework) as detailed below.
Module Grade Minimum Requirements to achieve Module Grade:
Pass Pass in Component 1 and pass in Component 2.
Fail Fail in Component 1 and/or fail in Component 2.
NS Non-submission of work by published deadline or non-attendance for examination

Module Requirements
Prerequisites for Module None, in addition to course entry requirements.
Corequisites for module None.
Precluded Modules None.

INDICATIVE BIBLIOGRAPHY
1 VINCE, J., 2020, Foundation mathematics for computer science: a visual approach
2 ROSEN, K., 2019. Discrete Mathematics and Its Applications (8th ed). McGraw-Hill.
3 Campbell, M., 2019, Learn RStudio IDE: Quick, Effective, and Productive Data Science
4 E. Wolfgang. 2017. Introduction to Artificial Intelligence. Springer.
5 Yannakakis, G. N., Togelius, J. 2018. Artificial Intelligence and Games. Springer.

Robert Gordon University, Garthdee House, Aberdeen, AB10 7QB, Scotland, UK: a Scottish charity, registration No. SC013781