Timeplan

This page contains a schedule of the teaching sessions. Most of the teaching will take place between July 31 and August 11, 2023, with additional supervision on August 15. Each day will generally contain one or two teaching sessions. Most sessions will follow the daily schedule below:

  • Morning session (9-12):
    • 9-10 Live lecture,
    • 10-12 Notebook with integrated lecturing videos on technical aspects of the session + exercises
  • Lunch break (12-13):
  • Afternoon session (13-16):
    • 13-14 Live lecture,
    • 14-16 Notebook with integrated lecturing videos on technical aspects of the session + exercises
  • Office hour, optional: 16-17 (booking system through Google Sheets, see link in the schedule below)

The complete schedule of sessions is listed below with title, lecturer and links to material. Unless otherwise specified, the teaching will use the hours specified above. See practical information for where lectures and exercise classes take place.

Materials will be added as the course progresses.

Date Time Title Teachers Material
——- Preparation ——-
Jun 28 Assignment 0 posted nb
Jul 27 12:00 Assignment 0 hand-in abs
——— Week 1 ———
Jul 31 9-11 1a. Course welcome + intro to SDS FC pdf
11-12 1b. Meet group FC/TAs
13-16 2. Data Structuring 1 FC & TAs le , nb, nb_sol
Aug 01 9-12 3. Data Structuring 2 FC & TAs le, nb, nb_sol
13-16 4. Plotting FC & TAs le , nb, nb_sol
16-17 Office Hour FC gs
23:59 Assignment 1 posted – nb
Aug 02 9-12 5. Strings, Queries and APIs FC & TAs le , nb, nb_sol
13-16 6. Scraping 1 FC & TAs le, nb, nb_sol
Aug 03 9-12 7. Scraping 2 FC & TAs le, nb, nb_sol
12:59 Fill in supervision sheet – gs, pdf
13-15 TA help + Supervision * TAs gs, pdf
Aug 04 9-12 Exam talk + TA help + Supervision FC & TAs le, gs
13-15 TA help + Supervision * TAs gs
Aug 04 23:59 Assignment 1 hand-in - abs
——— Week 2 ———
Aug 07 9-12 8. Scraping 3 FC & TAs le, nb, nb_sol
13-15 9. Data Ethics FC pdf
15-16 Office Hour FC gs
Aug 08 9-12 10. ML Introduction FC & TAs le, nb, nb_sol
13-16 11. Regression and Regularization FC & TAs le, nb, nb_sol
23:59 Assignment 2 posted – nb
Aug 09 9-12 12. Model Selection and Cross-validation FC & TAs le, nb, nb_sol
13-16 13. Performance Metrics, Non-linear ML, and Perspectives FC & TAs le
Aug 10 9-12 14. Text as Data FC & TAs le, nb, nb_sol
13-15 TA help * TAs All previous material
15-16 Office Hour FC gs
Aug 11 9-12 Exam talk + TA help + Supervision FC & TAs nb
13-15 TA help + supervision * TAs gs
23:59 Assignment 2 hand-in abs
——— Week 3 ———
Aug 15 10-12 Supervision 3 * TAs gs
Aug 15 20:00 Exam project description due pdf, abs
Aug 23 10:00 Hand-in Exam on Digital Exam

* : optional participation

Main lecturer

Teaching assistants (TAs)

  • Mischa Bennedsen (MB)
  • Jonathan Isaksen (JI)
  • Marcus Pedersen (MP)

Download material using Github Desktop

  • Download Github Desktop and make a Github account.

  • Open Github Desktop and go to File -> Open Repository

  • Go to the URL-tab and choose the course Github page (isdsucph/isds2023), and where you want to save it on your own computer.

  • Now you can access the files on you own computer.

  • If there have been updates to the ISDS-repository on GitHub, you can update it (known as pull, sync or fetch) in GitHub Desktop by choosing ISDS as your Current repository and press Fetch origin.

  • NB! When you make changes to your Notebooks, make a copy of the file, with a now name. Else you risk overwriting your changes, the next time you update the repository.

Legend

  • {le, nb, sol}: Respectively refer to lecture slides, exercises / assignment and solution to exercises in Jupyter Notebook format
  • pdf: Slides in PDF format or just PDF file
  • gs: Google Sheets link
  • abs: Link to Absalon