Data Science II – Advanced course

Instructor Sandro Fiore
Duration 12 hours
Period First semester

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 advanced knowledge and practical skills on Data Science; it provides an in-depth view of Data Science tools applied to a set of real-world case studies. The course targets students from all disciplines and consists of 12 hours. At the end of the course students will be equipped with a comprehensive knowledge of Data Science tools that will allow them to work on a variety of data-driven problems in any discipline.


This subject is aimed at students with (at least a basic) programming experience. The course includes theoretical parts jointly with more extensive practical ones developed through a set of hands-on/exercises based on Jupyter Notebooks.

(i) Data Science I (highly recommended)
(ii) basic knowledge of the Python programming language.

Content summary

  • Course introduction
  • Toolboxes for Data Scientists
    • Python and Python Libraries for Data Scientists
    • NumPy, Scipy, SCIKIT-Learn, PANDAS
    • Data Science Ecosystem
    • Integrated Development Environments (Jupyter)
    • Jupyter Notebooks
  • Python fundamentals for Data Scientists
    • Reading, selecting, filtering, sorting, manipulating, ranking, plotting data
  • Descriptive Statistics
  • Statistical Inference
  • Regression Analysis
  • Real-world case studies

Teaching methods

The course is structured with theoretical and (mostly) practical sessions. Theory and exercises will be integrated, so for each concept we will see both. Jupyter Notebooks will be mainly used for the hands-on part.

Homeworks will be also assigned to students and discussed in class.
All information will be available on the website of the course.


A final Data Science project will be used to assess students’ knowledge and practical understanding of the course topics.


[1] Introduction to Data Science, A Python Approach to Concepts, Techniques and Applications, by L. Igual, S. Seguí, Publisher Springer,
[2] Doing Data Science, by Cathy O’Neil, Rachel Schutt, Publisher(s): O’Reilly Media, Inc. ISBN: 9781449358655.