Data Science I – Introductory course

Instructor Sandro Fiore
Modality In presence
Duration 6 hours
Period Annual
Dates 02/05/2022 at 17:00-19:00
04/05/2022 at 17:00-19:00
09/05/2022 at 19:00-21:00

Course objectives

Data science is the study of extracting value from data. It combines insights, techniques, and tools from several disciplines including, among others, computer science, statistics and applied mathematics. The course is intended to provide students with fundamentals of Data Science presenting both theoretical aspects and some practical examples.

The course targets students from all disciplines and consists of six hours. At the end of the course, students will have a basic knowledge and understanding of Data Science fundamentals.

Entry requirements

This subject is aimed at students with little or no programming experience. The course is mostly theoretical, though basic Python code will be presented to support the analysis of some real case studies with concrete examples.

Content summary

The course will include the following topics:

  1. Course introduction
  2. Data Science fundamentals
    • Data Science introduction, current landscape and the role of Data Scientist
    • Statistical inference, exploratory data analysis and the Data Science process
    • Programming languages and environments for Data Science
  3. First tools for looking at data (1D datasets)
    • Descriptive statistics
    • Visualization methods
  4. Looking at relationships (2D datasets)
    • Scanner plots
    • Correlation
    • Prediction
  5. Data Science Toolboxes
  6. Some practical examples

Teaching methods

The course is structured with theoretical and practical parts. Lessons and exercises will be integrated: for each concept we will see theory and practice.
A few exercises will be assigned to students, carried out at home and discussed in class. All information will be available on the website of the course.

A final test will be used to assess students’ knowledge and understanding of the course topics.


  1. Probability and statistics for Computer Science, by David Forsyth, Publisher: Springer.
  2. Doing Data Science, by Cathy O’Neil, Rachel Schutt, Publisher(s): O’Reilly Media, Inc. ISBN: 9781449358655