Undergraduate Course: Predictive Analytics for Business (BUST10145)
||College||College of Humanities and Social Science
|Credit level (Normal year taken)||SCQF Level 10 (Year 3 Undergraduate)
||Availability||Available to all students
|Summary||The 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.
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.
Information for Visiting Students
|Pre-requisites||Business Research Methods I: Introduction to Data Analysis (BUST08033) or Statistical Methods for Economics (ECNM08016) or Statistics (MATH08051) or Business Analytics and Information Systems (BUST08032) equivalents
|High Demand Course?
Course Delivery Information
|Academic year 2018/19, Available to all students (SV1)
|Learning and Teaching activities (Further Info)
Lecture Hours 20,
Seminar/Tutorial Hours 10,
Formative Assessment Hours 2,
Revision Session Hours 2,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|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. The questions will include one of the 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 on the essays will be provided to let the students readjust their learning experience accordingly.
Generic exam feedback is also provided.
||Hours & Minutes
|Main Exam Diet S2 (April/May)||2:00|
On completion of this course, the student will be able to:
- Discuss the concept and methods of data analytics using the proper terminology .
- Understand the objectives and main characteristics of each model studied in the course.
- Critically assess the results of the predictive analytic models and their implication.
- Select the most suitable model based on the characteristics of the data and the problem analysed.
- Critically evaluate the limitations of the models used .
|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.
|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 organiser||Dr Raffaella Calabrese
Tel: (0131 6)50 3900
|Course secretary||Ms Patricia Ward-Scaltsas
Tel: (0131 6)50 3823