Practicum

See also: pet project

There are nine practice sessions in this course (PS01-PS09), and the instructions are contained in Python notebooks you have to complete.

Please ask in the course forum or to your practice instructor (“profesor/a de prácticas”) any questions you may have, and if you cannot attend a session, please contact your practice instructor by e-mail.

Please, do not ask the instructors to install software in your computer. If you run into software installation problems, ask in the course forum. The practice instructors absolutely do not have the bandwidth for this.

The text below is valid for PS02, PS03, and PS04. It probably will stay the same, but we may have to do some adjustments from PS05 onwards.

Before the practice session

Prepare for the session

Each notebook will be posted here a day before your session:

• Clone this repository and pull the changes.
• Open the notebook
• Run the imports cell to make sure you have all the requirements.
  • Python 3.8 or higher with the packages in requirements.txt
  • Jupyter or equivalent to run local Python notebooks.
• Run the cell where the dataset is loaded to make sure it loads well in your machine.
• Look at the data to become familiar with what the dataset is about.
• Read the rest of the notebook to understand what the task is about.
• You may bring a little of work done, but not more than the first 20% (according to the percentages next to section titles in the notebook), please.

During the practice session

Bring your laptop to the practice sessions.

You may use AI, but remember to mark clearly in the code #AI whenever you copy-pasted a block of code from AI, and #AI (edited) whenever you copy-pasted a block of code from AI, but then changed it.

You must attend the session, and pre-deliver at the end of the session. Your pre-delivery should contain progress proportional to the duration of the session, two hours.

#	Notebook	Date 101	Date 102	Date 103	Attendance
1	PS01 Null values	30/09	30/09	30/09	Mandatory
2	PS02 Data preparation	06/10	06/10	06/10	Mandatory
3	PS03 Near-duplicates	08/10	08/10	08/10	Mandatory
4	PS04 Association rules	19/10	19/10	19/10	Mandatory
5	Pet project planning	20/10	20/10	20/10	Optional
6	PS05 Content-based recommender system	27/10	27/10	28/10	Mandatory
7	PS06 Item-based similarity recsys	03/11	03/11	04/11	Mandatory
8	PS06 Wrap-up	05/11	05/11	05/11	Optional
9	PS07 Outlier analysis	10/11	10/11	11/11	Mandatory
10	PS08 Data streams	17/11	17/11	18/11	Mandatory
11	PS09 Time series forecasting	24/11	24/11	25/11	Mandatory
12	Practices exam	30/11	30/11	30/11	Mandatory

After the practice session

24h after the end of your session: you must deliver your notebook.

Your delivery should be a continuation of your pre-delivery, and will be graded normally.

No late deliveries are accepted.