Data science lab: process and methods (2019/2020)
General information
SSD: ING-INF/05
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)
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
Exam rules: pdf
Project of the winter session
Final grades:
- Results (27/01/2020): pdf
- Results (10/02/2020): pdf (updated)
- Results (16/06/2020): pdf (updated 27/07/2020)
- Results (02/09/2020): pdf (updated 04/10/2020)
Announcements
- 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)
Seminars
- Machine Learning and Data Fusion in three implementations. CELI. (pdf)
- Speaker: Francesco Tarasconi (francesco.tarasconi@celi.it)
- 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): daniele.tomatis@consoft.it
Serena Ambrosini (Head of research and development): serena.ambrosini@consoft.it
Python
This section will contain the slides and material of the Python classes.
Material
- GitHub repository. Here we will publish text and solutions of the exercises solved during Python lectures.
- GitHub tutorial (pdf)
- Python installation tutorial (pdf)
Slides
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.