20829 - DATA ANALYTICS FOR SUSTAINABILITY

In recent years, the data-driven revolution has progressed rapidly. Through increased connectivity and digitalization, private users and companies are generating an unprecedented amount of data which is changing the way we think about the economy. Data and analytics can help businesses understand the cost, impact, and performance of their initiatives while anticipating future requirements and adapting to evolving market conditions. Big data analytics provide decision support, traceability and predictive capabilities that can help transform organisational practices. The aim of the course is to provide students with a first introduction to the topics of data analytics and is divided into two parts. In the first part, decision making and descriptive analytics methods are discussed, which allow students to analyse data producing relevant outputs and interpreting it. In the second part, classical and advanced methods of data analytics for multivariate observations will be introduced, with emphasis to recent methods at the very frontier between statistics and machine learning.

• Decision analysis: structuring a business problem with models and data.
• Principles of Machine Learning: the structure of a Data Science Problem
• Predictive models for a continuous response: linear regression.
• Predictive models for a categorical response: logistic regression.
• Explorative Methods for Multivariate Data: data exploration, principal component analysis and clustering
• Machine Learning Approaches: Ridge and Lasso Regression, Decision Trees and Random Forests

• Recognize appropriate models to structure business and decision problems.
• Identify the correct methodology for solving business and management problems through data science.
• Discern between alternative machine tasks and alternative models.

• Organize information to build a quantitative model in line with the input posed.
• Translate a decision problem into a corresponding quantitative model.
• Use the software R and Silver Decisions in order to determine solutions to a problem.
• Interpret solutions derived from implementing the chosen model in order to obtain managerial insights.
• Analyze models with tools that come from the statistical theory to make inference robust.

• Face-to-face lectures

Teaching and learning activities for this course are divided into face-to-face lectures during which management problems are explained and solution models through quantitative methods are proposed and discussed. Students are assisted in:

• Identifying the decision analysis, statistical or machine learning model, whose principles and properties are described.
• Implementation through dedicated software.
• The solution to the problem.
• Interpreting the solution.
• Analysis of the variability of solutions on the basis of input parameters.

In particular, SilverDecisions and R are used in the classroom. Practice sessions are held during the course, in which students complete activities and go through exercises with their laptops, aimed at the described procedure (identifying a model, implementing data, solutions and sensitivity analysis). These exercises are used as self-assessment of learning of the above-mentioned aspects.  

All the parts of the exam aims to verify:

• The ability to identify a model in line with the hypothesys theories and data assigned.
• The ability to implement the model with the appropriate software.
• The ability to interpret the software’s output.
• The ability to assess the sensitivity of the solutions compared to the input parameters.

  The exam will consist of three parts, namely: an individual assignment, a written exam, and a group project.  

  1) Assignments: Students will be required to solve two individual assignments, with problems covering the entire syllabus, to be carried out individually with the use of the software, within a given time window specified by the teachers.  

  2) Written Exam: students will have to solve questions and short exercises, without the use of the software. The written exam will be solved individually during an in-presence session scheduled in one of the official dates established by the School.   

   

• The assignments will count for 4/5 of the final grade, the final exam for 1/5.

• Slides and notes prepared by the course teachers 

• James G., Witten D., Hastie T., Tibshirani R.: An introduction to statistical learning, with application to R. Springer, New York 2013. https://www.statlearning.com 

• Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: data mining, inference and prediction., Springer-Verlag, New York 2009