Module Details

Module Code: MATH C7001
Full Title Data Analysis for Computing
Valid From: Semester 1 - 2019/20 ( June 2019 )
Language of Instruction:English
Duration: 1 Semester
Credits: 5
Module Author Gabriel Matthews
Departments: Unknown
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.
 
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.
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).
No recommendations listed
 
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.
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 Content & Assessment
Assessment Breakdown%
Course Work40.00%
End of Module Formal Examination60.00%
Special Regulation
 

Assessments

Full Time

Course Work
Assessment Type Other % of Total Mark 10
Marks Out Of 100 Pass Mark 40
Timing Week 6 Learning Outcome 1,2,6
Duration in minutes 50
Assessment Description
An in-class lab test which will require students to use appropriate software tool to implement elements of exploratory data analysis.
Assessment Type Class Test % of Total Mark 20
Marks Out Of 0 Pass Mark 0
Timing Week 9 Learning Outcome 1,2,3,4,6
Duration in minutes 55
Assessment Description
The student will be required to sit a one-hour written class assessments covering the theoretical elements of the course
Assessment Type Other % of Total Mark 10
Marks Out Of 100 Pass Mark 40
Timing Week 12 Learning Outcome 1,2,3,5,6
Duration in minutes 60
Assessment Description
An in-class lab test which will require students to use an appropriate software tool to calculate probabilities an carry out a regression analysis.
No Project
No Practical
End of Module Formal Examination
Assessment Type Formal Exam % of Total Mark 60
Marks Out Of 100 Pass Mark 40
Timing End-of-Semester Learning Outcome 1,2,3,4,5
Duration in minutes 120
Assessment Description
End-of-Semester Final Examination

Part Time

Course Work
Assessment Type Other % of Total Mark 10
Marks Out Of 100 Pass Mark 40
Timing Week 6 Learning Outcome 1,2,6
Duration in minutes 50
Assessment Description
An in-class lab test which will require students to use appropriate software tool to implement elements of exploratory data analysis.
Assessment Type Class Test % of Total Mark 20
Marks Out Of 100 Pass Mark 40
Timing Week 9 Learning Outcome 1,2,3,4,6
Duration in minutes 55
Assessment Description
The student will be required to sit a one-hour written class assessments covering the theoretical elements of the course
Assessment Type Other % of Total Mark 10
Marks Out Of 100 Pass Mark 40
Timing Week 12 Learning Outcome 1,2,3,5,6
Duration in minutes 50
Assessment Description
An in-class lab test which will require students to use an appropriate software tool to calculate probabilities and carry out a regression analysis.
No Project
No Practical
End of Module Formal Examination
Assessment Type Formal Exam % of Total Mark 60
Marks Out Of 100 Pass Mark 40
Timing End-of-Semester Learning Outcome 1,2,3,4,5
Duration in minutes 120
Assessment Description
End-of-Semester Final Examination
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 & Resources

Workload: Full Time
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
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
 
Resources
Recommended Book Resources
  • Diez, David and Cetinkaya-Rundel Mine. (2017), OpenIntro Statistics, 3rd. 1-3,7, 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, Addison-Wesley, 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
 
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