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Cursus: INFOMDSS
INFOMDSS
Data science and society
Cursus informatie
CursuscodeINFOMDSS
Studiepunten (EC)7,5
Cursusdoelen

This is the starting and obligatory course for the Business Informatics (MBI) program. As such, its primary objective is to inspire and introduce you to the domain of Applied Data Science.

At the end of this course, you will be able to:
  1. Understand the role of data science and its societal impact
  2. Recognize the knowledge discovery processes in applied data science
  3. Identify trends and developments in big data technologies
  4. Apply selected big data technologies to solve real-world problems
  5. Analyze unstructured data using natural language processing techniques
  6. Understand the need for self-service data science
Assessment
Throughout the course, you are given a number of individual (mostly quite small) assignments. The answers to the assignments are to be submitted to the appropriate channel in our DSS 2020 Teams group before the stated deadline (mostly one week after release).
There will be no deadline extensions, so be sure to submit appropriately. These assignments will be assessed but not graded: you either pass or fail.

When you have failed 20 % or more of the total number of assignments, you will have failed the course due to the course effort criterion.
However, if you did pass at least 65% of the assignments, you will be given the opportunity to do the repair assignment (which is a relatively big assignment).

To help you complete the assignments, this class is also supported by the DataCamp learning platform for Python, SQL and more, through a combination of short expert videos and hands-on-the-keyboard exercises.

The final grade will be determined based on the following course components:
[A] Mid-term exam
[B] End-term exam
[C] Optional bonus (or penalty) for extraordinary (or poor) participation/performance

Grade = [A]*0.50 + [B]*0.50 + [C]

Note that the minimum grade of each of these exams is a 5.0. If for one of the exams your grade is between a 4.0 and a 5.5, you can repair that specific exam during the “second chance” session.
Note that it is not possible to repair both exams. You need to have a final grade of 6.0 or higher to PASS the course.

All course materials are examined, including all lecture slides, assignments and weekly readings.

In order to qualify for the repair test, all grade components need to be 5.0 or higher, and you also need to have passed at least 65% of the assignments.

Inhoud

Course form
Lectures, tutorials, quizzes, Q&A sessions, assignments.

Literature
We provide PDFs for most if not all required literature. Additional reading will be made known.

The required readings include:

  • Igual, L., & Seguí, S. (2017)."Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications". Switzerland: Springer. [url]
  • Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., & Wirth, R. (2000) "CRISP-DM 1.0: Step-by-step data mining guide" SPSS inc, 16. [Sections 1,2] [url]
  • Hutter, F., Kotthoff, L., & Vanschoren, J. (2019). "Automated Machine Learning - Methods, Systems, Challenges". Springer. [Chapter 1 (required); Chapter 8 (additional)] [url]
  • Clark, A., Fox, C., & Lappin, S. (Eds.). (2013). "The handbook of computational linguistics and natural language processing". John Wiley & Sons. [Chapter 1 (required); Chapters 4,9 (additional)] [url]
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