B0925 - DATA SCIENCE APPLICATIONS

Learning outcomes

By the end of the course the student will develop advanced expertise in analysing complex real-world data. In particular the student will be able to: - identify and apply appropriate statistical techniques to real application problems; - implement the various stages of advanced statistical analysis; - work in a team to develop a data analysis project; -present results of analyses in a short talk and/or poster and demonstrate effective communication skills.

Course contents

Introduction to survival analysis:

1. Survival data and censoring
2. The hazard and survival functions
3. Nonparametric survival curve estimation
4. Nonparametric comparison of survival distributions
5. Cox proportional hazards model
6. Parametric models

Case studies

Readings/Bibliography

Suggested readings:

• Hosmer, D.W., Lemeshow, S., May, S. (2008). Applied Survival Analysis: Regression Modeling of Time to Event Data. John Wiley & Sons.

• Lee, E.T., Wang, J.W. (2013). Statistical Methods for Survival Data Analysis, John Wiley & Sons.

• Moore, D.F. (2016). Applied Survival Analysis Using R. Springer.

Teaching methods

Lectures, computer laboratory sessions, group work.

In consideration of the type of activity and teaching methods adopted, attending this course requires students to have preemptively undergone Modules 1 and 2 about security when in rooms and laboratories for studying, through the e-learning platform (https://elearning-sicurezza.unibo.it/).

Assessment methods

The final exam aims at evaluating the acquired capability to:

• identify and apply appropriate statistical techniques to real application problems;
• implement the various stages of advanced statistical analysis;
• work in a team to develop a data analysis project;
• present results of analyses in a short talk and demonstrate effective communication skills.

Students are asked to elaborate a statistical report on a dataset; collective works are accepted. The deliverable will be presented and discussed during the oral exam.

The evaluation is expressed as eligibility (binary outcome).

Teaching tools

• Slides
  
• Datasets
• R scripts

Office hours

See the website of Marco Berrettini