Difference between revisions of "Data Analysis for Smart Agriculture"

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(Detailed course schedule)
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The following are last-minute news you should be aware of ;-)
 
The following are last-minute news you should be aware of ;-)
 +
* 03/11/2022: Last set of slides (clustering) have been published
 
  * 19/10/2022: Groups have been sent to students + added a second date for project evaluation
 
  * 19/10/2022: Groups have been sent to students + added a second date for project evaluation
 
  * 11/10/2022: Change in the time of 14/10/2022 lecture!!
 
  * 11/10/2022: Change in the time of 14/10/2022 lecture!!

Revision as of 23:35, 3 November 2022


The following are last-minute news you should be aware of ;-)

* 03/11/2022: Last set of slides (clustering) have been published 
* 19/10/2022: Groups have been sent to students + added a second date for project evaluation
* 11/10/2022: Change in the time of 14/10/2022 lecture!!
* 09/10/2022: New slides published and links to datasets
* 30/09/2022: Detailed final calendar published ... minor changes are expected 
* 22/09/2022: on Friday 23/09/2022 there will be no lecture
* 12/09/2022: the course starts today!!!

Course Aim & Organization

Teachers

The course is composed of a blending of lectures and labs by the course teacher and a teaching assistant.

Course Program

Detailed course schedule

Lecture are:

* On Monday, in room 25.1.1, starts at 16:30 ends at 18:00
* On Friday, in room 25.1.4, starts at 14:30 ends at 16:00

A detailed schedule follows:

Date Day Time Room Teacher Topic
12/09/2022 Monday 17:30-19:00 25.1.1 Matteo Matteucci Course Intro
16/09/2022 Friday 14:30-16:00 25.1.4 Filippo Renga Smart Agrifood Scenario
19/09/2022 Monday 16:30-18:00 25.1.1 Filippo Renga Principles of Data Strategy
23/09/2022 Friday 14:30-16:00 -- -- -- No Lecture --
26/09/2022 Monday 16:30-18:00 25.1.1 Filippo Renga Lab on Data Strategy
30/09/2022 Friday 14:30-16:00 25.1.4 Matteo Matteucci Intro to data analysis
03/10/2022 Monday 16:30-18:00 25.1.1 Riccardo Bertoglio Python and NumPy
07/10/2022 Friday 14:30-16:00 25.1.4 Riccardo Bertoglio Pandas
10/10/2022 Monday 16:30-18:00 25.1.1 Matteo Matteucci Data sources and regression intro
14/10/2022 Friday 13:30-15:00 25.1.4 Matteo Matteucci Regression
17/10/2022 Monday 16:30-18:00 25.1.1 Riccardo Bertoglio Regression lab
21/10/2022 Friday 14:30-16:00 25.1.4 Filippo Renga Data Valorization
24/10/2022 Monday 16:30-18:00 25.1.1 Matteo Matteucci Classification
28/10/2022 Friday 14:30-16:00 25.1.4 Riccardo Bertoglio Classification lab
31/10/2022 Monday 16:30-18:00 25.1.1 -- Holidays -- -- Holidays --
04/11/2022 Friday 14:30-16:00 25.1.4 Matteo Matteucci Clustering
07/11/2022 Monday 16:30-18:00 25.1.1 -- No lecture -- -- No lecture --
11/11/2022 Friday 14:30-16:00 25.1.4 Riccardo Bertoglio Clustering Lab
14/11/2022 Monday 16:30-18:00 25.1.1 Matteo Matteucci + Filippo Renga + Riccardo Bertoglio Project brainstorming
18/11/2022 Friday 14:30-16:00 25.1.4 Matteo Matteucci Project clinic
21/11/2022 Monday 16:30-18:00 25.1.1 Filippo Renga Project Clinic
25/11/2022 Friday 14:30-16:00 25.1.4 Filippo Renga Project clinic
28/11/2022 Monday 16:30-18:00 25.1.1 Matteo Matteucci Project clinic
02/12/2022 Friday 14:30-16:00 25.1.4 Riccardo Bertoglio Project clinic
05/12/2022 Monday 16:30-18:00 25.1.1 Riccardo Bertoglio Project clinic
09/12/2022 Friday 14:30-16:00 25.1.4 -- Holidays -- -- Holidays --
12/12/2022 Monday 16:30-18:00 25.1.1 Matteo Matteucci + Filippo Renga Project Check
16/12/2022 Friday 14:30-16:00 25.1.4 Matteo Matteucci + Riccardo Bertoglio Project clinic
19/12/2022 Monday 16:30-18:00 25.1.1 Matteo Matteucci + Filippo Renga Project evaluation
23/12/2022 Friday 14:30-16:00 25.1.4 -- No Lecture -- -- No Lecture --
11/01/2023 Wednesday 15:00-18:00 25.1.4 Matteo Matteucci + Filippo Renga Project evaluation

Course Evaluation

The course is intended to be a hands-on practical project course, students are expected to face a project "in group" ... more details will follow soon

Course Slides

Here the slides used during classes

You can find here the Material for practicals!

Some data sources

The following are some links to data sources which might become handy when working on the projects