95638 - Operational Analytics

Learning outcomes

Operational analytics, a specific type of business analytics, is focused on the analysis of business processes to the end of creating competitive advantage by means of operational data analysis and of the application of analytical algorithms. The course concentrates on the algorithmic side, specifically presenting predictive analytical techniques, forecasting future data on the basis of available time series, and prescriptive analytical techniques, defining optimized usage of available resources. Real world cases will be studied and used as testbed for self-developed systems.

Course contents

The course is part of a data science curriculum and provides some tools for predicting short/medium-term management data (predictive analytics) and optimizing scarce resource allocation processes based on predicted data (prescriptive analytics). Elements of predictive analytics and heuristic optimization will be presented and integrated.
For the predictive part, the course proposes methodologies and techniques for analyzing, modeling and predicting univariate time series, with hints of multivariate ones.
It will be shown, compatibly with the available time, that forecast data can be elements of mathematical models of management processes in which to optimize the allocation of scarce resources.
The proposed tools are meant to be used in real business application cases, actual case studies will be shown compatibly with time, and/or demanded to final projects.
The scientific content of the course relates to the knowledge needed to develop an operational analysis module on data obtained from a business information system. Specifically, the following will be presented
- brief summary of stochastic models, random variables. probability distributions
- predictive models: statistical (ARMA, ARIMA, SARIMA), neural (MLP, LSTM, perhaps SVR if time permits) and machine learning/decision tree models (ensemble, random forest, boosting)

 - performance indicators, descriptive statistics, statistical significance tests.

- introductory integer programming models 
- hints to meta/math-heuristic solving techniques
The technological contents will be functional to the practical implementation of the mentioned module, which will be done standalone in python, although other environments and architectures are acceptable.
A full solution will be set up in the classroom and completed independently by each student, and may constitute the project for the exam.

Readings/Bibliography

Lecture slides.

Teaching methods

The course will be offered in English, unless each and everyone of you asks me otherwise.

I will teach in the lab as much as I will be allowed to, in order to ensure hands-on experience of the proposed methods. Anyway, students are encouraged to bring their own laptop for online testing of the proposed elements. 

The algorithms will be coded, following the students' choice, in c# or python. Excel will be used for quick and dirty data analysis.

“"In view of the type of activities and teaching methods adopted, the attendance of this training activity requires the prior participation of all students in Modules 1 and 2 of safety training in the workplace, in e-learning mode”.

Assessment methods

The exam consists of the presentation of an individual project that each student will complete, related to the elements introduced during the course.

For students familiar with programming, the project will include both scientific and technological skills, which will be verified through the presentation. The project consists of a unified computer solution that includes most of the elements introduced during the course. The specific topic will be suggested by the candidates. Very complex solutions may be developed in groups of up to two students, subject to my explicit approval. The proposed solution must be able to run on the machines in the labs, and thus also on my course server.

Generally, unless otherwise agreed, the project will be a python solution. No jupyter notebooks are accepted, and only pandas, numpy, matplotlib and the libraries used in the transparencies can be included as additional libraries, unless otherwise is explicitly agreed upon.

Students with limited previous programming eperience will be able to present complex and economically significant case studies in detail, to which the same algorithms seen in lecture will be applied..

Active participation in lectures will entitle students to specific bonuses.

Teaching tools

Lecture slides will be downloadable before the corresponding lecture.

Office hours

See the website of Vittorio Maniezzo