Module ICE-0101:
Essential Mathematics
Essential Mathematics 2024-25
ICE-0101
2024-25
School of Computer Science & Engineering
Module - Semester 1
20 credits
Module Organiser:
Tim Smith
Overview
Topics areas covered in this module may include: Algebra and Functions: understand and use the laws of indices. Solution of linear and quadratic equations and manipulation of formulae. Solution of simultaneous equations. The use of fractions.
Statistical Sampling: the importance of sample populations in the statistical analysis of data understand and the use sampling techniques, including simple random, systematic and opportunity sampling. Selection and critique of sampling techniques in the context of a statistical problem.
Data presentation and Interpretation: Histograms and their relationship to statistical distributions. Scatter diagrams and correlation. Measures of central tendency and variation, including standard deviation. Dealing with outliers in datasets.
Probability: understanding independent and mutually exclusive events. Conditional probability. Venn diagrams. Tree diagrams. Calculating probabilities.
Statistical Distributions: understand and use probability distributions, including the normal distribution and binomial distribution. Use of different distributions to model real world situations and evaluate their appropriateness.
Statistical hypothesis testing: understand and apply the language of hypothesis testing. Conduct statistical hypothesis tests, including in relation to the normal or binomial distribution.
Assessment Strategy
-threshold -Grade D- to D+Basic understanding of mathematical and statistical techniques but some errors present. Is able to formulate appropriate solutions to accurately solve tasks and questions but with some inaccuracies and misconceptions evident. Outputs can be understood, but lack structure and/or coherence.
-good -Grade B- to B+A good understanding of mathematical and statistical techniques with few errors. Is able to formulate appropriate solutions to accurately solve tasks and questions with only very minor inaccuracies and misconceptions evident. Outputs can be understood, which demonstrate good structure and/or coherence.
-excellent -Grade A- and aboveAn excellent understanding of mathematical and statistical techniques with virtually no errors. Is able to formulate appropriate solutions to accurately solve tasks and questions with virtually no inaccuracies and misconceptions evident. Outputs can be understood, which demonstrate excellent structure and/or coherence.
-another level-Grade C- to C+A clearer understanding of mathematical and statistical techniques. Is able to formulate appropriate solutions to accurately solve tasks and questions but with some minor inaccuracies and misconceptions evident. Outputs can be understood, with possible improvements to structure and/or coherence.
Learning Outcomes
- Correctly solve mathematical problems.
- Correctly utilize statistical techniques to analyse data.
- Understand and evaluate assumptions made when modelling or solving problems
- Understand risk, probability and variation in statistics
Assessment method
Class Test
Assessment type
Summative
Description
Test 2: sampling, data presentation and probability
Weighting
33%
Due date
29/11/2024
Assessment method
Class Test
Assessment type
Summative
Description
Test 3: distributions and hypothesis testing
Weighting
34%
Due date
17/12/2024
Assessment method
Class Test
Assessment type
Summative
Description
Test 1: algebra and functions
Weighting
33%
Due date
29/11/2024