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 foundational courses of the Master, the course will expand the toolbox of methods which can be employed for forecast/prediction and for decision making. The courses focuses on both theoretical and practical aspects (with the statistical software R) of the methods.

A deeper understanding of some basic prescriptive and predictive 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 prescriptive and predictive methods presented in the course
2. (applying knowledge and understanding)
- Apply autonomously, correctly and critically the prescriptive and predictive methods 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 prescriptive and predictive methods models presented in the course
- Being able to create compelling visualisations of both raw data and model outputs from prescriptive and predictive approaches

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

All topics will be introduced using the R software with a strong focus on reproducible research and visualisation of raw data and analytical 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/
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 lasts 90 minutes, takes place in the IT lab and is composed of two parts: a written part, worth 16 points, and an R-based part, worth 18 points. Both parts will be made of exercises which aim to evaluate
1. the theoretical knowledge of the course topics,
2. the ability to apply them for solving real data problems.
3. the ability to use R and interpret its output to solve real data problems.
4. the ability to use the R software to present analytical results.

The final grade is based on the level demonstrated by the students via the written exam on these items.

written

A. Scores in the 18-22 range will be awarded in the presence of:
- basic understanding of the theoretical concepts of the methods presented in the course;
- limited ability to interpret and present the result of an analysis;
- limited ability to translate the results of an analysis into common language and vice versa.
B. scores in the 23-26 range will be awarded in the presence of:
- more than basic understanding of the theoretical concepts of the methods presented in the course;
- fair ability to interpret and present the results of an analysis;
- fair ability to translate the results of an analysis into common language and vice versa.
C. scores in the 27-30 range will be awarded in the presence of:
- good or very good understanding of the theoretical concepts of the methods presented in the course;
- good or very good ability to interpret and present the result of an analysis;
- good or very good ability to translate the results of an analysis into common language and vice versa.
Honours (lode) will be awarded to students who demonstrate a particular ability to solve all the questions posed with care and attention to detail

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.