Practicum

Software requirements

You will need:

• Python 3.8 or higher.
  • You can use the Anaconda package manager.
• Several Python packages
  • matplotlib
  • seaborn
  • numpy
  • scikit-learn
  • apyori
  • nltk
    • You will also need to run in your Python interpreter: import nlkt and nltk.download('punkt')
• Jupyter Notebooks.
  • Follow these instructions.

Please, if you run into problems installing this software, ask in the course forum. Please do not ask the practice instructors, they absolutely do not have the bandwidth for this.

Practice sessions

Bring your laptop to the practice sessions, and clone this repository.

There are ninve practice sessions in this course, the handouts are Python notebooks. Each session starts with psNN and describe the activities that the students must perform during the practice session.

Some parts are not visible in the preview shown on the GitHub website, so you need to download the notebook to see the instructions.

At the end of each handout there is a description of what you should deliver. Please ask in the course forum or to your practice instructor (“profesor/a de prácticas”) any questions you may have.

See also: personal project

#	Notebook	Date 101	Date 102	Date 103	Attendance	Deadline
1	PS01 Data Preparation	30/09	30/09	30/09	Mandatory	End of session
2	PS02 Data Cleaning	06/10	06/10	06/10	Mandatory	End of session
3	PS03 Near-duplicates	08/10	08/10	08/10	Mandatory	End of session
4	PS04 Association rules	19/10	19/10	19/10	Mandatory	End of session
5	Personal project plan	20/10	20/10	20/10	Optional
6	PS05 Content-based recommender system	TBA	TBA	TBA	Mandatory	End of session
7	PS06 Item-based similarity recsys	TBA	TBA	TBA	Mandatory	End of session
8	PS06 Wrap-up	TBA	TBA	TBA	Optional
9	PS07 Outlier analysis	TBA	TBA	TBA	Mandatory	End of session
10	PS08 Data streams	TBA	TBA	TBA	Mandatory	End of session
11	PS09 Time series forecasting	TBA	TBA	TBA	Mandatory	End of session
12	Practices exam	TBA	TBA	TBA	Mandatory	N/A