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DRPS : Course Catalogue : Business School : Business Studies

Undergraduate Course: Predictive Analytics for Business (BUST10145)

Course Outline
SchoolBusiness School CollegeCollege of Arts, Humanities and Social Sciences
Credit level (Normal year taken)SCQF Level 10 (Year 3 Undergraduate) AvailabilityAvailable to all students
SCQF Credits20 ECTS Credits10
SummaryThe course covers predictive analytics techniques for cross sectional and panel data to respond to the job market needs of quantitative skills. The methods studied are illustrated with empirical examples.
Course description This course aims at training students in the field of predictive analytics to respond to the job market needs using econometric techniques. To be more specific, this course covers five types of models: basic linear model, linear models accounting for endogeneity, panel data, models with limited dependent variables and duration models. It also covers practical issues in predictive analytics and how to address them.

The course is organised around the following four main teaching blocks:
- Block 1: Linear regression models for cross sectional data with and without endogeneity, and applications in business, finance and economics.
- Block 2: Regression models for panel data and applications in business, finance and economics.
- Block 3: Probit and logit models for discrete variables with applications in business, finance and
- Block 4: Duration models with applications in business, finance and economics.

Teaching will take the form of weekly 2-hour class lectures and weekly 1-hour computer lab sessions. Students will learn how to use state-of-the-art predictive analytics tools in the context of practical problems faced by business managers. Some of the material covered in lectures and discussion sessions will be research-led and based on recent publications from the academic literature.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: Business Research Methods I: Introduction to Data Analysis (BUST08033) OR Business Analytics and Information Systems (BUST08032) OR Statistical Methods for Economics (ECNM08016) OR Statistics (Year 2) (MATH08051)
Prohibited Combinations Other requirements None
Information for Visiting Students
Pre-requisitesVisiting students must have at least 4 Business courses at grade B or above. This course cannot be taken alongside BUST08033 Business Research Methods I: Introduction to Data Analysis; BUST08032 Business Analytics and Information Systems; ECNM08016 Statistical Methods for Economics or MATH08051 Statistics (Year 2). We will only consider University/College level courses.
High Demand Course? Yes
Course Delivery Information
Academic year 2023/24, Available to all students (SV1) Quota:  None
Course Start Semester 2
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 200 ( Lecture Hours 20, Seminar/Tutorial Hours 10, Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 166 )
Assessment (Further Info) Written Exam 70 %, Coursework 30 %, Practical Exam 0 %
Additional Information (Assessment) Essay Assignment (30%): In this project students will have to apply the models presented during the lectures on data using the statistical language R explained during the tutorials and provide an interpretation of the main results. The submission of the essay is due during the semester. The minimum length is 2,500 words; the maximum length is 3,500 words.

Written Exam (70%): Students should attempt four out of five questions. There will be at least one question from each of the 4 topics discussed during the lectures.

It is important for students to enjoy a practical component in which they employ the material that is used during the practical sessions. Furthermore, predictive analytics typically takes place in a data-driven environment which requires project-based skills. This is reflected in the essay component. Nevertheless, the interpretation and characteristics of predictive analytics methodologies should be fully understood and serve as the main learning goal for this course. This is tested during the written exam.
Feedback Feedback on the essays will be provided to let the students readjust their learning experience accordingly.

Generic exam feedback is also provided.
Exam Information
Exam Diet Paper Name Hours & Minutes
Main Exam Diet S2 (April/May)2:00
Learning Outcomes
On completion of this course, the student will be able to:
  1. Discuss the concept and methods of data analytics using the proper terminology .
  2. Understand the objectives and main characteristics of each model studied in the course.
  3. Critically assess the results of the predictive analytic models and their implication.
  4. Select the most suitable model based on the characteristics of the data and the problem analysed.
  5. Critically evaluate the limitations of the models used .
Reading List
James, G., Witten, D., Hastie, T., Tibshirani, R. (2017). An Introduction to Statistical Learning
with Applications in R, Springer Texts in Statistics;
Wooldridge, Jeffrey (2010). Econometric Analysis of Cross Section and Panel Data. MIT Press, 2nd ed.;
Verbeek, Marno (2012). A Guide to Modern Econometrics. John Willey and Sons, 4th ed.;
Hosmer, D., Lemeshow, S., May, S. (2008). Applied Survival Analysis: Regression Modeling of Time to Event Data, John Willey and Sons, 2nd Edition;
Faraway, J. J. (2005) Linear Models with R, Taylor & Francis;
Moore, D. F. (2016) Applied Survival Analysis Using R, Springer.
Additional Information
Graduate Attributes and Skills After the completion of this course, students should be able to:
- Perform quantitative analyses in accordance with the type of the data used
- Plan and implement projects involving data analysis
- Interpret the results of the predictive analytics models
- Evaluate the performance of the models used
- Use the statistical package R to implement different types of models
Course organiserDr Raffaella Calabrese
Tel: (0131 6)50 3900
Course secretaryMs Morgan Wilson
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