PRE-CORSI 2022-23

PREPARATORY COURSES 2022-2023

Two useful pre-courses reserved to all admitted students to the Master Degree in Data Science 2022-23 will be given in the week 12-16- September 2022, in presence, at the Sapienza premises.

  • Crash course in Probability
  • Crash course in Python

In person attendance is highly recommended in order to take full advantage of the course value and to start living university community and building friendships and personal support networks (courses will be online only for students coming from aborad and having not the VISA yet).

 

 

Registration is required  by filling the form available here:

https://forms.gle/2wABwLEFWivKQHBZA

 
 
Teaching mode, location and tentative schedule:
 

Crash course on Python

This is a hands-on introductory course to the python programming language, meant to give you a head start into the data science master degree.

We will interactively cover the basics of coding (data types, data structures, control flows, and OOP) starting from the very basics.

 

This is a tentative list of topics:

 

  • Lecture 1: a basic introduction to Python and data structures
  • Lecture 2: exception handling, file I/O, and standard (built-in)
  • Lecture 3: introduction to object-oriented programming
  • Lecture 4: git and third-party libraries

 

In order to join the course it's mandatory to  pay the 10 euros tax for verification of pre-requisites. You can attend the preparatory course even if you have not yet received an answer to your prerequisite verification request. In case you are lacking credits/knowledge in the specific areas, attendance to the pre-courses is encouraged.
 
All admitted students will be contacted via e-mail by September 10th with all details on location and schedule
 
The course is optional and won't give you any credit or grade, however we highly recommend attendance to students that have little coding experience, 
 
 
Crash course in Probability
 

Brief course description:

 

The goal of this crash course is to provide the student with the basic of probability theory, including an hint of the "data science" perspective: i.e. data modelling. It will cover both theory and exercises, assuming no prior knowledge of the topic.

This is a tentative list of topics:
 

  • Lecture 1: Why we need a Probabilistic Model. Building blocks: definition of probability, independence, conditioning.
  • Lecture 2: What is a Probabilistic Model. Definition and characterisation of a Random Variable. Basic summaries: expected value, variance.
  • Lecture 3: Main  univariate (1D) probabity models and how to Use them. Discrete (Binomial, Poisson) and Continuous (Gaussian, Exponential) random variables.
  • Lecture 4: Beyond 1D. Characterising multidimensional random variables: joint, marginal and conditional distribution. Dependence and association: Correlation, Covariance. Wrap up what we left out the previous days.

There will be no written lectures notes, but you can find all the topics we covered in the following book: A modern introduction to Probability and Statistics - Sparse stuff from Chapters 1 to 10.

 

Other suggested Textbooks:

Agresti, Franklin and Klingenberg (2017), Statistics: The Art and Science of Learning From Data, Pearson 

 

In order to join the course it's mandatory to pay the 10 euros tax for verification of pre-requisites. You can attend the preparatory course even if you have not yet received an answer to your prerequisite verification request. In case you are lacking credits/knowledge in the specific areas, attendance to the pre-courses is encouraged. 

 

 

All admitted students will be contacted via e-mail by September 10th with all details on location and schedule