Predictive Analysis Technologies
| Module title | Predictive Analysis Technologies |
|---|---|
| Module code | BEP3150 |
| Academic year | 2021/2 |
| Credits | 15 |
| Module staff |
| Duration: Term | 1 | 2 | 3 |
|---|---|---|---|
| Duration: Weeks | 11 |
| Number students taking module (anticipated) | 30 |
|---|
Module description
This module will build on your knowledge of business analytics and modelling with big data analytics to improve your understanding of analytics techniques in theory and practice, to analyse current data and to make predictions about the future. You will get hands-on experience in handling data to model classification and regression problems, applying statistical and machine learning methods. Throughout the module, Python will be the programming language used to integrate predictive analytics into real-life business challenges and operations. There are no pre-requisites but useful complementary modules to have taken in the first and second year include GEO1419 Introduction to Data Science and BEP2140 Business Analytics. Complementary modules in the final year include BEP3140 Modelling with Big Data Analytics.
Module aims - intentions of the module
By taking this module, you will learn to build a predictive model from the ground up. From data identification and preparation, to data analysis and interpretation, you will learn to integrate predictive analytics technologies into real-life business operations. Regression analysis, forecasting techniques, simulation and data mining will be used to analyse current and historical data to determine patterns and make predictions about future trends and events. In this module, you will work in Python, strengthening your ability to work with a worldwide used procedural programming language.
Intended Learning Outcomes (ILOs)
ILO: Module-specific skills
On successfully completing the module you will be able to...
- 1. Critically evaluate the utility of Python for integrating predictive analytics into real-life business challenges and operations
- 2. Implement linear and logistic regression models
ILO: Discipline-specific skills
On successfully completing the module you will be able to...
- 3. Perform data preparation, modelling and interpretation
- 4. Apply predictive analytics to real-life business cases
ILO: Personal and key skills
On successfully completing the module you will be able to...
- 5. Think analytically
- 6. Apply digital tools & online resources to a range of analytical situations and data processing/modelling scenarios
Syllabus plan
Topics discussed on the module include (not exclusively):
- Introduction to predictive analytics
- Predictive modelling in Python
- Predicting and predicted variables
- Data preparation, modelling and interpretation
- Linear and logistic regression models
- Challenges with predictive modelling
- Predictive analytics applications and case studies
Learning activities and teaching methods (given in hours of study time)
| Scheduled Learning and Teaching Activities | Guided independent study | Placement / study abroad |
|---|---|---|
| 22 | 128 | 0 |
Details of learning activities and teaching methods
| Category | Hours of study time | Description |
|---|---|---|
| Scheduled Learning and Teaching Activity | 11 | Lectures and workshops (11 x 1 hour) |
| Scheduled Learning and Teaching Activity | 11 | Tutorials (11 x 1 hour) |
| Guided Independent Study | 128 | Reading, research and assessment preparation |
Formative assessment
| Form of assessment | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|
| In class quizzes and multiple choice exercises | During each class | 1-6 | Verbal, in class |
Summative assessment (% of credit)
| Coursework | Written exams | Practical exams |
|---|---|---|
| 40 | 0 | 60 |
Details of summative assessment
| Form of assessment | % of credit | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|---|
| Time Constrained Assessment (TCA) | 40 | 1 hour open book assessment | 1,4 | Written |
| Practical exam | 60 | 2 hour lab based, open book practical exam | 1-6 | Written |
| 0 | ||||
| 0 | ||||
| 0 | ||||
| 0 |
Details of re-assessment (where required by referral or deferral)
| Original form of assessment | Form of re-assessment | ILOs re-assessed | Timescale for re-assessment |
|---|---|---|---|
| TCA (1 hour) (40%) | TCA (1 hour open book assessment) (40%) | 1,4 | August reassessment period |
| Practical exam (2 hours) (60%) | Practical exam (2 hour lab based open book practical exam) (60%) | 1-6 | August reassessment period |
Indicative learning resources - Basic reading
The following books are a useful resource for this course:
- Albright S. and Winston W.L. (2016). Business Analytics: Data Analysis & Decision Making (6th Ed). Boston, MA: Cengage.
- Downey, A. (2012). Think Python (2nd Edition). O'Reilly
- Lemahieu, W., Broucke, S., & Baesens, B. (2018). Principles of Database Management: The Practical Guide to Storing, Managing and Analyzing Big and Small Data. Cambridge University Press.
- Page, S. E. (2018). The model thinker: What you need to know to make data work for you. Hachette UK.
- Witten, I., Frank, E., Hall, M., & J Pal, C. (2017). Data Mining Practical Machine Learning Tools and Techniques (4th Edition). Elsevier
Indicative learning resources - Other resources
A more comprehensive bibliography will be available to students taking this course.
| Credit value | 15 |
|---|---|
| Module ECTS | 7.5 |
| Module pre-requisites | None |
| Module co-requisites | None |
| NQF level (module) | 6 |
| Available as distance learning? | No |
| Origin date | 10/05/2021 |
| Last revision date | 15/07/2021 |
