Department of Information and Computing Sciences

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

Onderwijs Informatica en Informatiekunde

Vak-informatie Informatica en Informatiekunde

Data science and society

Website:website met extra informatie
Studiepunten:7.5 ECTS
Periode:periode 3 (week 6 t/m 15, d.w.z. 6-2-2017 t/m 13-4-2017; herkansing week 27)
Deelnemers:tot nu toe 0 inschrijvingen
Rooster:De officiële roosters staan in MyTimetable
Docenten:Dit is een oud rooster!
college          Marco Spruit
Matthieu Brinkhuis
Inhoud: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
  • Spruit,M., & Jagesar,R. (2016). Power to the People! Meta-algorithmic modelling in applied data science. In Fred,A. et al. (Ed.), Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (pp. 400–406). KDIR 2016, November 11-13, 2016, Porto, Portugal: ScitePress.
  • Pritzker, P., and May, W. (2015). NIST Big Data interoperability Framework (NBDIF): Volume 1: Definitions. NIST Special Publication 1500-1. Final Version 1. National Institute of Standards and Technology.
  • Lazer, D., Kennedy, R., King, G., & Vespignani, A. (2014). The parable of Google Flu: traps in big data analysis. Science, 343(6176), 1203-1205.
  • Spruit,M., & Boer,T. de (2014). Business Intelligence as a Service: A Vendor’s Approach . International Journal of Business Intelligence Research, 5(4), 26–43.
  • Domingos, P. (2015). The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. Basic Books. ISBN: 0465065708 (ISBN13: 9780465065707).
Werkvorm:There will be 6 contact hours per week. On Tuesdays from 11:00-12:45, regular lectures will be given, as on Thursdays from 13:15-15:00. The Thursday lectures are followed by workshop sessions where we will practice with big data tools (esp. Hadoop) and collaboratively investigate their societal impact.

In the first weeks, the lectures will focus more on the fundamentals of applied data science, whereas in the second half we will be introduced into the work of various UU/UMCU researchers related to applied data science.

The following assignments are scheduled:

  • Explore data science and its societal impact
  • Survey the market landscape
  • Study selected scientific literature
  • Practice with big data tools
Toetsvorm:The graded deliverables generate the final course grade as follows:
[A] Book review
[B] Market research
[C] Project pitch event
[D] Written, mostly multiple choice, exam
[E] Optional bonus for extraordinary participation/performance

Grade = [A]*0.15 + [B]*0.15 + [C]*0.30 + [D]*0.40 + [E]

Inspanningsverplichting voor aanvullende toets:To qualify for the second chance exam, all grading components need to be at least 4.0.
Beschrijving:This is the introductory course for the Applied Data Science profile. As such, it's primary objective is to inspire and introduce you to the emerging domain of Applied Data Science.

The course balances between Big Ideas and their feasibility due to the Big Diversity of its qualifying students. On the one hand it aims to trigger your enthousiasm for applied data science, and to inspire you to aim for societal impact through data science. On the other hand, it needs to provide you with a core set of information science essentials to properly understand big data technologies, while leveraging your diversity as an opportunity to create an inspiring course.