Module Details
Module Code: 
MATH C7001 
Full Title:

Data Analysis for Computing

Valid From: 
Semester 1  2021/22 ( September 2021 ) 
Language of Instruction:  English 
Module Owner:: 
Gabriel Matthews

Module Description: 
This module aims to develop the students' problemsolving 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.

Module Learning Outcome 
On successful completion of this module the learner will be able to: 
# 
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. 
Prerequisite 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).

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 fivenumber summaries.

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.

Conditional probability
Bayes theorem, decision trees.

Probability Distributions
Random variables. Probability distributions and densities. Binomial, Poisson and Normal models.

Regression Analysis
Scatterplots, Correlation & Simple Linear Regression Analysis.

Excel
Investigation & use of some of the graphical & statistical functionality in excel. Calculating and simulating probabilities.

Module Assessment

Assessment Breakdown  % 
Course Work  100.00% 
Module Special Regulation 

AssessmentsFull Time
Part Time
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.

DKIT reserves the right to alter the nature and timings of assessment
Module Workload
Workload: Full Time 
Workload Type 
Contact Type 
Workload Description 
Frequency 
Average Weekly Learner Workload 
Hours 
Lecture 
Contact 
Interactive studentcentred lectures. 
Every Week 
2.00 
2 
Tutorial 
Contact 
Weekly exercise classes. 
Every Week 
1.00 
1 
Practical 
Contact 
Weekly labbased 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 
Workload Type 
Contact Type 
Workload Description 
Frequency 
Average Weekly Learner Workload 
Hours 
Lecture 
Contact 
Interactive studentcentred lectures. 
Every Week 
2.00 
2 
Tutorial 
Contact 
Weekly exercise classes. 
Every Week 
1.00 
1 
Practical 
Contact 
Weekly labbased 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

Recommended Book Resources 


Diez, David and CetinkayaRundel Mine. (2017), OpenIntro Statistics, 3rd. 13,7, Creative Commons License, 1  157 and 315  330.
 Supplementary Book Resources 


Weiss, N.. (2011), Introductory Statistics, 9th edition. Pearson, [ISBN: 978032169794].

Milton, J.C., Arnold, J.S.. Introduction to Probability and Statistics: Principles and Applications for Engineering and the Computing Sciences
ISBN: 007246836X, AddisonWesley, 2003.

Tufte, E.. The Visual Display of Quantitative Information.
ISBN: 0961392142, Graphics Press, 2001.
 This module does not have any article/paper resources 

Other Resources 


[website], Wolfram Inc.. MathWorld Probability,

[website], Khan Academy,

[website], Seeing Statistics,

[website], Diez, David and CetinkayaRundel Mine,,
 