Big data Analitics

Recommended Prerequisites

NA

Teaching Methods

The class integrates theoretical lectures with practical based on Python to learn how to implement and analyze the most appropriate models according to the available data.
A tutor will help students weekly in acquiring all the necessary theoretical and practical knowledge.
All the material used during the lectures (slides. script. data) will be available on Kiro-UNIPV platform.

Learning Outcomes

The aim of this course is to study and apply the most relevant statistical models in the analysis of complex data set.
Students will acquire basic skills related to the choice of the most suitable machine learning and/or statistical learning models.
Students will be able to implement the chosen models through the Python software.
Students will understand how to build in a statistical sound way summary measures of well-being.

Work Placement(s)

No

Syllabus

Inferential statistics; Testing hypotheses; Statistical estimation;
Ridge and Lasso regression;
Support Vector Machines;
Decision Tree, Bagging and Boosting.
Principal Component Analysis.

Assessment Methods

Assessment
Case study analysis: 50.0%
Exam: 50.0%

Bibliography

1) Coelho and Richert, Building Machine Learning Systems with Python, Second edition, Packt Publishing
2) Introduction to Python for Econometrics, Statistics and Data Analysis Kevin Sheppard, pdf version available
3) Witten, D. (2013). An Introduction to Statistical Learning: with Applications in R. undefined. Retrieved from https://www.semanticscholar.org/paper/An-Introduction-to-Statistical-Learning%3A-with-in-RWitten/ b5e5a7eee59dd740897c0c3d1ada96c2e2a7e0a7