| Module Code: |
H8SDA |
|
Long Title
|
Strategic Data Analysis
|
|
Title
|
Strategic Data Analysis
|
| Module Level: |
LEVEL 8 |
| EQF Level: |
6 |
| EHEA Level: |
First Cycle |
| Module Coordinator: |
Ade Fajemisin |
| Module Author: |
Ade Fajemisin |
| Departments: |
School of Computing
|
| Specifications of the qualifications and experience required of staff |
Master’s degree or PhD in a computing or cognate discipline. May have industry experience also.
|
| Learning Outcomes |
| On successful completion of this module the learner will be able to: |
| # |
Learning Outcome Description |
| LO1 |
Illustrate the relationship between decisions and data |
| LO2 |
Demonstrate how to use data to help understand and manage the business on a strategic level |
| LO3 |
Define how uncertainty can be incorporated into decisions |
| LO4 |
Analyse and discuss how people perceive and decide about risk |
| LO5 |
Use analytical and problem-solving skills in decision making |
| LO6 |
Demonstrate, in oral and written form, the skills and attributes of effective decision makers in applying the knowledge and techniques of decision making to case studies and other real-world contexts |
| Dependencies |
Module Recommendations
This is prior learning (or a practical skill) that is required before enrolment on this module. While the prior learning is expressed as named NCI 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 |
Co-requisite Modules
|
| No Co-requisite modules listed |
| Entry requirements |
Learners should have attained the knowledge, skills and competence gained from stage 3 of the BSc (Hons) in Data Science
|
Module Content & Assessment
| Indicative Content |
|
Probabilistic modelling: From Data to a Decisive Knowledge
Probabilities and conditional probabilities, random variables
|
|
Probabilistic graphical models
Representation and types
|
|
Bayesian networks applied to decision making
Introduction, Naive Bayes classifier, Kalman filter
|
|
Learning Bayesian networks from data for decision making
Inference and learning
|
|
Decision making under risk and uncertainty
Risk management, Representation of Conditional Preferences Under Uncertainty
|
|
Decision Trees
Decision tree analysis in risk management
|
|
Markov decision processes
Model Formulation, Finite-Horizon Markov Decision Processes, Infinite-Horizon Models: Foundations, Average Reward and Related Criteria
|
|
Multicriteria decision making 1
Multi-attribute utility theory
|
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Multicriteria decision making 2
Outranking methods
|
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Introduction to social choice theory
Group decision making, voting system, computational social choice
|
|
Introduction to multiobjective optimisation
Pareto dominance, exact method, heuristics
|
|
Case studies
Medical diagnosis, clinical decision support. Ethics aspects
|
| Assessment Breakdown | % |
|---|
| Coursework | 30.00% |
| End of Module Assessment | 70.00% |
AssessmentsFull Time
| Coursework |
| Assessment Type: |
Continuous Assessment |
% of total: |
Non-Marked |
| Assessment Date: |
n/a |
Outcome addressed: |
1,2,3,4,5,6 |
| Non-Marked: |
Yes |
Assessment Description:
Ongoing independent and group problem solving activities and feedback. |
|
| Assessment Type: |
Continuous Assessment |
% of total: |
30 |
| Assessment Date: |
n/a |
Outcome addressed: |
1,2,3 |
| Non-Marked: |
No |
Assessment Description:
This assessment will evaluate learner’s comprehension of fundamental data analysis theory and concepts, and their applicability to problems with uncertainty. In addition, learners will be required to propose and document a solution approach to a problem with uncertainty |
|
| End of Module Assessment |
| Assessment Type: |
Terminal Exam |
% of total: |
70 |
| Assessment Date: |
End-of-Semester |
Outcome addressed: |
1,2,3,4,5,6 |
| Non-Marked: |
No |
Assessment Description:
Terminal exam with five questions of which the student must answer four to address the students' understanding of the underlying theories and concepts |
|
| Reassessment Requirement |
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.
|
Reassessment Description
The repeat strategy for this module is an examination. All learning outcomes will be assessed in the repeat exam.
|
NCIRL reserves the right to alter the nature and timings of assessment
Module Workload
| Module Target Workload Hours 0 Hours |
| Workload: Full Time |
| Workload Type |
Workload Description |
Hours |
Frequency |
Average Weekly Learner Workload |
| Lecture |
Classroom & Demonstrations (hours) |
24 |
Per Semester |
2.00 |
| Tutorial |
Other hours (Practical/Tutorial) |
24 |
Per Semester |
2.00 |
| Independent Learning |
Independent learning (hours) |
202 |
Per Semester |
16.83 |
| Total Weekly Contact Hours |
4.00 |
Module Resources
| Recommended Book Resources |
|---|
-
Bernard, M,. (2017), Data Strategy: How to profit from a world of big data, analytics and the internet of things, Kogan Page Ltd.
-
Provost, F. & Fawcett, T.. (2013), Data Science for Business, O'Reilly Media.
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Kochenderfer, M. J. & Amato, C.G.,. (2015), Decision Making Under Uncertainty – Theory and Application, MIT Press.
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Koski, T. & Noble, J.. (2009), Bayesian Networks: An Introduction, Wiley.
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Triantaphyllou, E.. (2001), Multi-criteria decision making methods: a comparative study, Kluwer Academic.
| | Supplementary Book Resources |
|---|
-
Levenson, A.. (2015), Strategic Analytics: Advancing Strategy Execution and Organizational Effectiveness, Berrett-Koehler Publishers.
| | This module does not have any article/paper resources |
|---|
| This module does not have any other resources |
|---|
|
