Antonio Di Narzo, PhD, is professor of Foundations of Statistical Analysis and Machine Learning at DSTI. He earned his MSc and PhD in Statistics at the University of Bologna. He used to work as a postdoctoral fellow in Biostatistics at the SIB Swiss Institute of Bioinformatics in Lausanne, and at Icahn School of Medicine at Mount Sinai in New-York. Antonio’s research has been published in many journals.
Foundations of Statistical Analysis & Machine Learning (Part 1) – Descriptive Statistics
Students will be introduced to the basic principles of data reduction. For example, they will know how to read and understand epidemiological data from different countries. An essential part of data science is the principled digestion of large volumes of data – descriptive statistics lay the foundations to perform this task.
Foundations of Statistical Analysis & Machine Learning (Part 1) – Probability Theory
Students will have a framework to measure, control and deal with uncertainty. For example, the student will be able to take better informed medical decision about themselves from the outcome of clinical tests. An important part of data science is understanding uncertainty in the information we have and in the conclusions we draw from our work. Having a solid framework to deal with uncertainty will therefore make the student a better professional.
Survival Analysis Using R
Students will take away the concept of censored duration data and the existence of well founded tools to analyse it. Students will work on applications range from drug testing in clinical trials, marketing research on contracts duration, analysis of unemployment claims, and maximizing the chance of adoption from a pet’s rescue shelter! The student will have one more tool in his or her tool belt to analyse and understand censored duration data.