Obiettivi formativi
Provide students with practical skills to work with and summarize datasets, aiming to provide business insights.
Prerequisiti
A good knowledge of descriptive statistics, inference, and basic Python programming.
Risultati di apprendimento attesi
The students will have a great ability to use statistics to provide business insights. Also, the continuous assessment program will enhance the student's skills in Python.
Contenuti Del Corso
Stats brief recap;
Inference brief recap;
Python brief recap;
Regression models recap;
Linear and Non Linear. regression models;
Cluster Analysis;
Principal Component Analysis;
Basic of Machine Learning.
Testi Di Riferimento
Course material.
Metodologie Didattiche
Theory + Practical classes.
Modalità di verifica dell'apprendimento
Group continuous assessments (fortnightly frequency) + In class Group Continuous Assessment + Final Theory Exam (written in class)
Criteri per l’assegnazione dell’elaborato finale
TBD
Settimana 1
Statistics and Inference Recap;
Settimana 2
Python recap; first assessment.
Settimana 3
Linear Regression Models
Settimana 4
Linear Regression Models
Settimana 5
Generalized Linear Regression Model.
Settimana 6
Generalized Linear Regression Model.
Assessment
Settimana 7
Panel Regression
Settimana 8
Panel Regression
Assessment
Settimana 9
Intro to Time Series
Settimana 10
Intro to Time Series
Settimana 11
Intro to Machine Learning
Settimana 12
Course Recap, Assessment