/data-mining-course

An undergraduate course on data mining.

Primary LanguageJupyter NotebookGNU General Public License v3.0GPL-3.0

Data Mining Course

Instructor: Carlos Castillo

These are materials for a twelve-weeks undergraduate course on Data Mining, and include two-hour lectures and two-hour practice sessions every week. They were developed for third year students of the bachelor degree on Mathematical Engineering on Data Science at Universitat Pompeu Fabra, Barcelona.

  • "It is special to get a glimpse of what it is to be a data scientist for real. Something bad is the work required for the practicum, which is a bit too much" -- a student from the 2021 edition
  • "I could say this is the best subject I've had during the studies" -- a student from the 2021 edition
  • "I found this subject especially interesting and useful for our future as data scientists. The practices were reflecting the theory concepts and were very guided which I really appreciated. Also, good point that the datasets were so updated." -- a student from the 2020 edition
  • "The learning methodology is excelent and students are given quality materials for learning. As a negative point, the workload is excessive" -- a student from the 2020 edition
  • "A lot of work in the practices, but it was helpful" -- a student from the 2020 edition
  • "Exciting and really helpful for our future as data scientists" -- a student from the 2019 edition
  • "A lot of work. However, the theory and practice materials are excellent" -- a student from the 2019 edition

Contents of this repository

🚧 These materials should not be considered final until the end of the course.

  • πŸ“ˆ Theory: slides and planning for the theory part.
  • πŸ’» Practicum: activities for practical sessions.
  • πŸ“ Datasets: to be used during practical sessions.
  • πŸ“ Exams from previous and current year.

Material specific to UPF students:

Acknowledgments

I am thankful to Aris Gionis and Aris Anagnostopoulos for their comments on an earlier version of these materials. Thanks to Miguel Angel CordobΓ©s for his work on several of the practical sessions, and to Fedor Vitiugin for his corrections and improvements on the practical sessions.

All course materials are available under a Creative Commons license unless specified otherwise.