Department of Information and Computing Sciences

Departement Informatica Onderwijs
Bachelor Informatica Informatiekunde Kunstmatige intelligentie Master Computing Science Game&Media Technology Artifical Intelligence Business Informatics

Onderwijs Informatica en Informatiekunde

Vak-informatie Informatica en Informatiekunde

Data science and society

Website:website containing additional information
Course code:INFOMDSS
Credits:7.5 ECTS
Period:period 1 (week 36 through 45, i.e., 4-9-2017 through 10-11-2017; retake week 1)
Participants:up till now 47 subscriptions
Schedule:Official schedule representation can be found in Osiris
Teachers:Dit is een oud rooster!
lab session          Zhengru Shen
lecture   Tue 15.15-17.0037-44 UNNIK-211 Marco Spruit
Matthieu Brinkhuis
Thu 11.00-12.4536 RUPPERT-A
tutorial group 1 Tue 13.15-15.0037-44 UNNIK-209 studentassistent SR
group 2 Tue 13.15-15.0037-44 UNNIK-222 studentassistent DK
Contents:At the end of this course, you will be able to:
  1. Understand the role of data science and its societal impact
  2. Recognise 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
Literature:See the Literature page on the course website for the latest references.
Course form:There will be 6 contact hours per week. One workshop 2-hour slot to practice with big data tools (Hadoop and Spark with R and Python), and two lecture 2-hour slots for both regular and guest lectures, to respectively investigate big data technologies and their societal impact.

The following assignments are among the key parts of the course:

  • Book review: Explore data science and its societal impact
  • Mid-term data analysis assignment
  • Final data analysis assignment
Exam form:The graded deliverables generate the final course grade as follows:
[A] Book review
[B] Mid-term assignment
[C] Final assignment
[D] Written, mostly multiple choice, exam
[E] Optional bonus for extraordinary participation/performance

Grade = [A]*0.10 + [B]*0.25 + [C]*0.30 + [D]*0.35 + [E]

Minimum effort to qualify for 2nd chance exam:To qualify for the second chance exam, all grading components need to be at least 4.0, and components A-C need to have been submitted within the allotted time.
Description:This is the introductory course for the Applied Data Science profile and the Applied Data Science postgraduate MSc programme. As such, it's primary objective is to inspire and introduce you to the emerging domain of Applied Data Science.