BUSINESS ANALYTICS

The course presents some tools employed in predictive and prescriptive analysis for business and society applications, with a focus on data analysis methods. Building upon the skills acquired in the fist year of the Master, the course will expand the toolbox of methods which can be employed for forecast/prediction and for decision making. Both theoretical and practical aspects of the analysis methods are discussed.

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). In particular the statistical computing language R will be employed.

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)
- simulation and montecarlo analysis
- decision making
- quantile regression
- hierarchical/panel models for structured data

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 http://www-bcf.usc.edu/~gareth/ISL/
Rob J Hyndman and George Athanasopoulos, Forecasting: Principles and Practice (3rd ed) https://otexts.com/fpp3/
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 - https://bookdown.org/yihui/rmarkdown/

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 exercises which aim to evaluate the theoretical 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 (www.r-project.org ). Students are encouraged to bring their own laptops and to experiment with the code during the course. 

English

written