Courses
Responsible Conduct in Research and Innovation
| Duration (hours) | Location | Enrolment deadline | Start date | Schedule | Details | Enrolment | Further information |
|---|---|---|---|---|---|---|---|
| 25 | C6-E106 Campus Nord | - | 22-09-23 | Friday, 11:00-13:00 | 13 2-hours sessions plus recommended readings | - | Syllabus |
Search Inside Yourself
| Duration (hours) | Location | Enrolment deadline | Start date | Schedule | Details | Enrolment | Further information |
|---|---|---|---|---|---|---|---|
| 16 | Sala Àgora Campus Nord UPC / Online | 26-10-23 | 07-11-23 | On-site: Tuesday 16:00-19:00 Online: Tuesday 16:30-19:30 |
Free course, with a deposit of 50 € which will be returned if the student attends at least an 80% of the sessions. | Search Inside Yourself (SIY) | Servei d'Esports de la UPC |
Course organized jointly with the UPC Innovation and Community Office.
Search Inside Yourself (3rd edition)
| Duration | Location | Enrolment deadline | Start date | Schedule | Details | Enrolment | Further information |
|---|---|---|---|---|---|---|---|
| 15 hours | Sala Àgora, Servei d'Esports UPC Barcelona | 31-10-24 | 12-11-24 | 12-11-24 19-11-24 14-01-25 09:00–17:30 |
Free course with a 50 € deposit, which will be returned after attending 100% of its sessions. The course's language is level B2 English | Form | Syllabus |
Introduction to Bayesian Data Analysis
| Duration | Location | Enrolment deadline | Start date | Schedule | Details | Enrolment | Further information |
|---|---|---|---|---|---|---|---|
| 15 hours | Campus Nord D3004 |
21-10-2024 | 05-11-2024 |
05-11-2024 10:00-13:00 |
The course is in person and taught in English. It is limited to 25 attendees, it requires basic notions of statistics. | Form | Syllabus |
Statistics and data science for PhD research. Module 2: Bayesian Modelling
| Duration (hours) | Location | Enrolment deadline | Start date | Schedule | Details | Enrolment | Further information |
|---|---|---|---|---|---|---|---|
| 15 | Campus Nord A2104 Online |
15-04-2024 | 22-04-2024 | 22-04-2024, 25-04-2024, 29-04-2024, 06-05-2024, 09-05-2024, 15:00-18:00 |
All the sessions will be in-person except for the last one (09-05-2024), which will be carried out through Google Meet. | Form | Syllabus |
Statistics and data science for PhD research. Module 2: Bayesian Modelling (2nd edition)
| Duration | Location | Enrolment deadline | Start date | Schedule | Details | Enrolment | Further information |
|---|---|---|---|---|---|---|---|
| 15 hours | Campus Nord D6004 | 24-05-24 | 04-06-24 | 04-06-24 06-06-24 11-06-24 13-06-24 20-06-24 10:00-13:00 |
All sessions will be done in person | Limited to those who were placed on the waiting list for the first edition, who have been contacted through email | Syllabus |
Technology-based Innovation Project (1st edition)
| Duration (hours) | Location | Enrolment deadline | Start date | Schedule | Details | Enrolment | Further information |
|---|---|---|---|---|---|---|---|
| 15 | Campus Nord UPC | 26-01-24 | 31-01-24 | 31-01-24 7-02-24 28-02-24 6-03-24 20-03-24 10:00-13:00 |
Must attend at least 80% of the sessions and turn in a final project. The minimum number of students will be 8 and the maximum 20. |
Form | Syllabus |
Technology-based Innovation Project (2nd edition)
| Duration (hours) | Location | Enrolment deadline | Start date | Schedule | Details | Enrolment | Further information |
|---|---|---|---|---|---|---|---|
| 15 | TBD | 31-01-2025 | 14-02-2025 |
14-02-2025 10:00-13:00 |
Must attend at least 80% of the sessions and turn in a final project. The Research Plan must have been presented and assessed. |
Form | Syllabus |
Scientific Writing: Putting Why? before How?
| Duration | Location | Enrolment deadline | Start date | Schedule | Details | Enrolment | Further information |
|---|---|---|---|---|---|---|---|
| 12 hours |
CNord A4 205 |
12-11-24 | 20-11-24 | 20-11-24 21-11-24 09:30-17:30 |
Exclusive for doctoral students planning to publish an article soon. The course will be carried out in person, taught in English and is limited to 25 attendees. | Form | Syllabus |
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