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Course module: NWI-IMC044
NWI-IMC044
Research Seminar Data Science
Course infoSchedule
Course moduleNWI-IMC044
Credits (ECTS)6
CategoryMA (Master)
Language of instructionEnglish
Offered byRadboud University; Faculty of Science; Informatica en Informatiekunde;
Lecturer(s)
PreviousNext 4
Lecturer (study guide:y results:y LMS:y)
dr. ir. T. Claassen
Other course modules lecturer
Lecturer (study guide:y results:y LMS:y)
dr. ir. F. Hasibi
Other course modules lecturer
Lecturer (study guide:y results:y LMS:y)
prof. dr. ir. D. Hiemstra
Other course modules lecturer
Lecturer (study guide:y results:y LMS:y)
dr. A.J. Hommersom
Other course modules lecturer
Contactperson for the course
prof. dr. E. Marchiori
Other course modules lecturer
Academic year2019
Period
KW3-KW4  (03/02/2020 to 30/08/2020)
Starting block
KW3
Course mode
full-time
Remarks-
Registration using OSIRISYes
Course open to students from other facultiesNo
Pre-registrationNo
Waiting listNo
Placement procedure-
Aims
  • Learn how to evaluate research papers
  • Learn what makes papers good
  • Learn about how papers are refereed and published
  • Obtain a broad overview of important recent developments in data science research
Content
The Research Seminar Data Science is intended to provide students with the opportunity to develop the skill of critically reading and evaluating research papers in the broad area of data science. The course is a required component of the Data Science specialisation. Attendance is compulsory. Every student in the class will present and/or review two papers. The paper to be presented is a recent paper published in a top data science conference or journal. The paper to review has been submitted to some top conference and may have major impact in the future.
Level

Presumed foreknowledge
Bachelor in computer science, artificial intelligence, or a related discipline. Preferably you have already taken a couple of courses in the Data Science specialisation.
Test information
There are three assessed components, of weight 20%, 40%, and 40%, respectively. 1) The collected set of your short summaries of the papers presented by others (i.e., excluding the papers you present yourself). Half to one page each. 2) Your review and presentation of the classical paper, based on your presentation and the discussion which followed. 3) Your review of the paper recently submitted to one of the data science conferences. Reports for 2) and 3) are around 5-6 pages each.
Specifics
If possible, we will replace the presentation and report of a recent paper by an actual review of a paper currently submitted to a conference or journal. This option will depend on the availability of such papers as well as the number of students taking the course. Furthermore, we will also try to attend actual talks by renowned data scientists and then replace one of the short reviews on the papers presented by your fellow students by a review on such a talk.
Additional comments
If possible, we will replace the presentation and report of a recent paper by an actual review of a paper currently submitted to a conference or journal. This option will depend on the availability of such papers as well as the number of students taking the course. Furthermore, we will also try to attend actual talks by renowned data scientists and then replace one of the short reviews on the papers presented by your fellow students by a review on such a talk.
Topics
The scope of the selected papers will be very broad and may involve material a long way from the topics of your interest. However the real purpose is to study the reading and writing of research in data science.
Test information
There are three assessed components, of weight 20%, 40%, and 40%, respectively.
1) The collected set of your short summaries of the papers presented by others (i.e., excluding the papers you present yourself). Half to one page each.
2) Your review and presentation of the classical paper, based on your presentation and the discussion which followed.
3) Your review of the paper recently submitted to one of the data science conferences. Reports for 2) and 3) are around 5-6 pages each.
Prerequisites
Bachelor in computer science, artificial intelligence, or a related discipline. Preferably you have already taken a couple of courses in the Data Science specialisation.
Required materials
Articles
A collection of papers on how to write, present and review papers in computer science will be made available during the course.
Instructional modes
Course
Attendance MandatoryYes

General
You present and guide the discussion on two papers on which you write a longer review. You further write short summaries on the papers presented by others.

Exam Q4

Lecture
Attendance MandatoryYes

Presentation
Attendance MandatoryYes

Remark
N
Project
Attendance MandatoryYes

Resit Exam Q4

Zelfstudie
Attendance MandatoryYes

Tests
Final grade
Test weight10
Test typeExam
OpportunitiesBlock KW4, Block KW4

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