Module Details
Module Code: |
MATH C7001 |
Full Title:
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Data Analysis for Computing
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Valid From:: |
Semester 1 - 2021/22 ( September 2021 ) |
Language of Instruction: | English |
Module Owner:: |
Gabriel Matthews
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Module Description: |
This module aims to develop the students' problem-solving skills by introducing them to the key role of descriptive statistics and probability in the solution of practical problems. In addition, the interpretation of statistical data and decision making under uncertainty are key transferable business skills which the student will be exposed to in this module.
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Module Learning Outcome |
On successful completion of this module the learner will be able to: |
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Module Learning Outcome Description |
MLO1 |
Calculate, present and interpret numerical and graphical summaries of statistical data. |
MLO2 |
Demonstrate an understanding of basic probability theory and be able to apply probability in the solution of practical problems in computing. |
MLO3 |
Recognise the appropriate probability distribution to model different problems and be able to compute probabilities for Binomial, Poisson & Normal distributions. |
MLO4 |
Construct and use decision trees and Bayes' Theorem as an aid to fault finding and problem solving. |
MLO5 |
Perform correlation and simple linear regression analyses. |
MLO6 |
Use an appropriate software tool for compiling descriptive statistics and graphs and calculating probabilities. |
Pre-requisite learning |
Module Recommendations
This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named DkIT module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
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No recommendations listed |
Module Indicative Content |
Descriptive Statistics
Tally charts and frequency distributions. Symmetrical and Skewed Distributions. Barcharts, histograms, boxplots, dotplots. Measures of central tendency and dispersion including five-number summaries.
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Probability Theory
Approaches to probability. Events, sample spaces and axioms of probability. Addition and multiplication laws. Normal, hypergeometric, binomial and poisson models. Computer simulations of probabilities and sampling variation. Normal probability plots.
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Conditional probability
Bayes theorem, decision trees.
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Probability Distributions
Random variables. Probability distributions and densities. Binomial, Poisson and Normal models.
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Regression Analysis
Scatterplots, Correlation & Simple Linear Regression Analysis.
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Excel
Investigation & use of some of the graphical & statistical functionality in excel. Calculating and simulating probabilities.
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Module Assessment
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Assessment Breakdown | % |
Course Work | 100.00% |
Module Special Regulation |
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AssessmentsFull Time On Campus
Part Time On Campus
Reassessment Requirement |
A repeat examination
Reassessment of this module will consist of a repeat examination. It is possible that there will also be a requirement to be reassessed in a coursework element.
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DKIT reserves the right to alter the nature and timings of assessment
Module Workload
Workload: Full Time On Campus |
Workload Type |
Contact Type |
Workload Description |
Frequency |
Average Weekly Learner Workload |
Hours |
Lecture |
Contact |
Interactive student-centred lectures. |
Every Week |
2.00 |
2 |
Tutorial |
Contact |
Weekly exercise classes. |
Every Week |
1.00 |
1 |
Practical |
Contact |
Weekly lab-based exercises involving the use of an appropriate software tool. |
Every Week |
1.00 |
1 |
Independent Study |
Non Contact |
Students will be expected to conduct independent review of content & research related topics |
Every Week |
2.00 |
2 |
Directed Reading |
Non Contact |
Guided Preparation for lectures & labs, including the completion of exercises. |
Every Week |
2.00 |
2 |
Total Weekly Learner Workload |
8.00 |
Total Weekly Contact Hours |
4.00 |
Workload: Part Time On Campus |
Workload Type |
Contact Type |
Workload Description |
Frequency |
Average Weekly Learner Workload |
Hours |
Lecture |
Contact |
Interactive student-centred lectures. |
Every Week |
2.00 |
2 |
Tutorial |
Contact |
Weekly exercise classes. |
Every Week |
1.00 |
1 |
Practical |
Contact |
Weekly lab-based exercises involving the use of an appropriate software tool. |
Every Week |
1.00 |
1 |
Independent Study |
Non Contact |
Students will be expected to conduct independent review of content & research related topics |
Every Week |
2.00 |
2 |
Directed Reading |
Non Contact |
Guided Preparation for lectures & labs, including the completion of exercises. |
Every Week |
2.00 |
2 |
Total Weekly Learner Workload |
8.00 |
Total Weekly Contact Hours |
4.00 |
Module Resources
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Recommended Book Resources |
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Diez, David and Cetinkaya-Rundel Mine. (2017), OpenIntro Statistics, 3rd. 1-3,7, Creative Commons License, 1 - 157 and 315 - 330.
| Supplementary Book Resources |
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Weiss, N.. (2011), Introductory Statistics, 9th edition. Pearson, [ISBN: 978032169794].
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Milton, J.C., Arnold, J.S.. Introduction to Probability and Statistics: Principles and Applications for Engineering and the Computing Sciences
ISBN: 007246836X, Addison-Wesley, 2003.
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Tufte, E.. The Visual Display of Quantitative Information.
ISBN: 0961392142, Graphics Press, 2001.
| This module does not have any article/paper resources |
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Other Resources |
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website, Wolfram Inc.. MathWorld Probability,
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website, Khan Academy,
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website, Seeing Statistics,
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website, Diez, David and Cetinkaya-Rundel Mine,,
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