Full Title:Data Analysis for Computing
Language of Instruction:English
Module Code:MATH C7001
 
Credits: 5
Valid From:Semester 1 - 2014/15 ( September 2014 )
Module Delivered in 2 programme(s)
Module Description:This module aims to develop the students problem solving skills, to provide them with a fundamental understanding of probability theory and the techniques of descriptive statistics.
Learning Outcomes:
On successful completion of this module the learner should be able to
  1. Apply fundamental concepts and techniques in exploratory data analysis.
  2. Demonstrate an understanding of the basic concepts of probability and use these to solve relevant problems in computing.
  3. Recognise the appropriate probability distribution to model given problems and compute probabilities for Binomial, Poisson & Normal distributions.
  4. Understand correlation and conduct analysis for simple linear regression models.
  5. Use an appropriate software tool for exploratory data analysis.
 

Module Content & Assessment

Indicative Content
Descriptive Statistics
Frequency tables, measures of central tendency & variation; Graphical representation of data
Probability Theory
Basic Laws of Probability, Probabilistic Problem Solving
Conditional probability
Bayes theorem, decision trees
Probability distributions
Binomial, Poisson and normal distributions
Regression Analysis
Scatterplots, Correlation & Simple Linear Regression Analysis.
Excel
Investigation & use of some of the graphical & statistical functionality in excel.
Assessment Breakdown%
Course Work40.00%
End of Module Formal Examination60.00%

Full Time

Course Work
Assessment Type Assessment Description Outcome addressed % of total Marks Out Of Pass Marks Assessment Date Duration
Continuous Assessment Weekly/(Bi-Weekly) short individual & group exercises or quizzes. 1,2,3,4 10.00 100 40 n/a 30
Practical/Skills Evaluation An in-class lab test which will require students to use appropriate software tool to implement elements of exploratory data analysis. 1,5 7.50 100 40 Week 6 60
Class Test The student will be required to sit a one-hour written class assessments covering the theoretical elements of the course 1,2 15.00 100 40 Week 8 60
Practical/Skills Evaluation An in-class lab test which will require students to use appropriate software tool to generate probabilities or carry out regression analysis. 3,4,5 7.50 100 40 Week 12 60
No Project
No Practical
End of Module Formal Examination
Assessment Type Assessment Description Outcome addressed % of total Marks Out Of Pass Marks Assessment Date Duration
Formal Exam End-of-Semester Final Examination 1,2,3,4 60.00 100 40 End-of-Semester 120
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 Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Interactive student-centred lectures. 2.00 Every Week 2.00
Tutorial Weekly exercise classes. 1.00 Every Week 1.00
Practical Weekly lab-based exercises involving the use of an appropriate software tool. 1.00 Every Week 1.00
Independent Study Students will be expected to conduct independent review of content & research related topics 2.00 Every Week 2.00
Directed Reading Guided Preparation for lectures & labs, including the completion of exercises. 2.00 Every Week 2.00
Total Weekly Learner Workload 8.00
Total Weekly Contact Hours 4.00
Workload: Part Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Interactive student-centred lectures. 2.00 Every Week 2.00
Tutorial Weekly exercise classes. 1.00 Every Week 1.00
Practical Weekly lab-based exercises involving the use of an appropriate software tool. 1.00 Every Week 1.00
Independent Study Students will be expected to conduct independent review of content & research related topics 2.00 Every Week 2.00
Directed Reading Guided Preparation for lectures & labs, including the completion of exercises. 2.00 Every Week 2.00
Total Weekly Learner Workload 8.00
Total Weekly Contact Hours 4.00
Resources
Supplementary Book Resources
  • Weiss, N. 2011, Introductory Statistics, 9th edition Ed., 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

Module Delivered in

Programme Code Programme Semester Delivery
DK_KCOMP_7 Bachelor of Science in Computing 3 Mandatory
DK_KCMP7_6 [Exit Award from L7] Higher Certificate in Science in Computing 3 Mandatory