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

General information

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

Exam rules for July and September 2020 sessions (COVID-19 emergency)

Exam rules

Project and report rules

Submission platform


Project of the September session

The project will begin on August 20, 2020 (20:00 CEST), and will end on September 20, 2020 (20:00 CEST).

  • Assignment: pdf


Project of the July session

The project for the July session will begin on June 17, 2020, and will end on July 17, 2020. The structure of the project will not change significantly (ranked submissions + report). The project rules will be published on June 17, 2020.

Important. Thanks to the novel technologies supported by the Politecnico di Torino, we are evaluating the possibility to use Exam/Exercise to redact the report and upload the final version of your software. All the submission guidelines will be available soon on the course website. By that time, you will be notified by email.

  • Assignment: pdf


Exam rules: pdf

Project of the winter session

  • Submission platform: link
  • Assignment: pdf
  • Competition results: pdf

Final grades:


  • 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)


  • Machine Learning and Data Fusion in three implementations. CELI. (pdf)
    • Speaker: Francesco Tarasconi (
  • Developement of an actionable prediction model for predicting the occupancy of parking spaces. Consoft Sistemi. (pdf-1, pdf-2)
    • Speaker: Charu Kapila
    • Contacts:
      Daniele Tomatis (Head of the Business Intelligence Business Unit):
      Serena Ambrosini (Head of research and development):



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)

Exam exercises

Exercises for the written exam


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: 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
    Solutions: pdf, html
  • Laboratory 7 (20-21 November 19): pdf
    Kaggle competition: link
    Solutions: pdf, html
  • Laboratory 8 (27-28 November 19): pdf
    Solutions: pdf, html
  • Laboratory 9 (4-5 December 19): pdf
    Submission platform: link
    Solutions: pdf
  • Laboratory 10 (11-12 December 19): pdf
    Solutions: pdf, html

Here you can find information about the submission platform.

      • Guide to submit on the final competition: pdf
      • A short guide to our Kaggle competitions: pdf

 Parent page 


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