Data science lab: process and methods (2019/2020)

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General information

CFI: 8
Professor: Elena Baralis
Teaching assistants:
Tania Cerquitelli (Lessons), Andrea Pasini (Python classes)
Giuseppe Attanasio, Flavio Giobergia (Laboratory sessions)


  • 10-10-2019. The text of the exercises for the Python lectures will be made available in our GitHub repository (check the Python/Material section).
  • 29-09-2019. The first Python lesson will be on 04 October 2019. We suggest you to bring your own PC with Python3 and Jupyter installed.
    In the “Python” section you can find instruction for installing the necessary software.

Learning material


Data science

This section will contain the slides of the data science course.

  • Course introduction (pdf)
  • Introduction to data science (pdf)
  • Data preprocessing (pdf)
  • Association rules (pdf)
  • Clustering (pdf)
  • Classification (pdf)
  • Regression analysis (pdf)
  • Time series analysis (pdf)
  • Data exploration, Feature Engineering, Data visualization (pdf)
  • Use case: Modelling energy efficiency of buildings based on open-data (pdf)
  • Use case: Predictive maintenance (pdf)
  • Use case: Semi-Supervised clustering of geological pores (pdf)



This section will contain the slides and material of the Python classes.


  • GitHub repository. Here we will publish text and solutions of the exercises solved during Python lectures.
  • GitHub tutorial (pdf)
  • Python installation tutorial (pdf)


  • Introduction to Python (pdf)
  • Python programming (pdf)
  • Numpy (pdf)
  • Matplotlib (pdf)
  • Scikit-learn: clustering (pdf)
  • Scikit-learn: classification (pdf)
  • Scikit-learn: regression (pdf)
  • Scikit-learn: preprocessing (pdf)
  • Pandas (pdf)


Laboratory material

This section will contain all the material for carrying out laboratories.

  • Laboratory 1 (9-10 October 19): pdf
    Solutions: pdf, html
  • Laboratory 2 (16-17 October 19): pdf
    Solutions: pdf, html
  • Laboratory 3 (23-24 October 19): pdf
    Solutions: pdf, html
  • Laboratory 4 (30-31 October 19): pdf
    Solutions: pdf, html
  • Laboratory 5 (6-7 November 19): pdf
    Submission platform: link
    Solutions: pdf, html
    Reference report: pdf
  • Laboratory 6 (13-14 November 19): pdf
  • Laboratory 7 (20-21 November 19): pdf
    Kaggle competition: link
  • Laboratory 8 (27-28 November 19): pdf
  • Laboratory 9 (4-5 December 19): pdf
    Submission platform: link
  • Laboratory 10 (11-12 December 19): pdf

Here you can find information about the submission platform.

  • A short guide to laboratory submissions: pdf
  • A short guide to our Kaggle competitions: pdf