Academic year
2021/2022 Syllabus of previous years
Official course title
Course code
EM1410 (AF:339154 AR:180099)
On campus classes
ECTS credits
Degree level
Master's Degree Programme (DM270)
Educational sector code
1st Term
Course year
Go to Moodle page
The course aims at improving the competences on forecasting modelling and the usage of of forecasting models in business applications.
A deeper understanding of some basic forecasting methods used in Business Analytics and their applications. In particular the various course activities will enable the student to attain the following objectives:

1. (knowledge and understanding)
- Gain knowledge in the mathematical and statistical structures of analytical and forecasting models presented in the course
2. (applying knowledge and understanding)
- Apply autonomously, correctly and critically the analytical and forecasting models presented in the course
3. (making judgements)
- Make a judgement about which methods to use in different applied scenarios knowing the advantages and disadvantages of different methods
4. (communication abilities)
- Being able to explain both in a technical and in a non-technical manner the workings and the results of the analytical and forecasting models presented in the course
- Being able to create compelling visualisations of both raw data and model outputs from analytical and forecasting models
There is no formal pre-requisite, but the course will rely on concepts and methods seen in the first year courses of the Master in Data Analytics for Business and Society (such as Statistical learning for data science, Data analytics and artificial intelligence, Managerial decision making and modelling)
The course introduces the concept of Business Analytics and discuss several modelling approaches such as:

- forecasting and the basics of time series data analysis (seasonality and trends, moving averages, exponential smoothing)
- quantile regression
- hiearchical/panel models for structured data
- simulation and montecarlo analysis
- multivariate data analysis
- decision making

All topics will be introduced using the R software with a strong focus on reproducible research and visualisation of raw data and model outputs.
Slides e material made available on Moodle.
For the different components of the course different textbooks will be used, including:

James G, Witten D, Hastie T, Tibshirani R (2015). An Introduction to Statistical Learning. 6th version. Springer. Webpage
Rob J Hyndman and George Athanasopoulos, Forecasting: Principles and Practice (3rd ed)
Andrew Gelman and Jennifer Hill Data Analysis Using Regression and Multilevel/Hierarchical Models, Cambridge
Lingxin Hao and Daniel Q. Naiman, Quantile Regression, Sage
Camm et al, Essentials of Business Analytics, Cengage Learning
Yihui Xie, J. J. Allaire, Garrett Grolemund, R Markdown: The Definitive Guide -
The exam will take place in the IT lab and is composed of parts: a written part and an R-based part. Both parts will be made of two exercises which aim to evaluate the teorethical and practical understanding of the different models discussed in the course.
The course consists of a combination of conventional theoretical classes focused on description of methods and practice sessions describing the implementation and application of the methods to real problems. Methods will be implemented with the statistical language R ( ). Students are encouraged to bring their own laptops (no tablets!) and to experiment with the code during the course. 
Definitive programme.
Last update of the programme: 14/07/2021