Official course description:
Full info last published 15/11-20

First Year Project

Course info
Language:
English
ECTS points:
15
Course code:
BSFIYEP1KU
Participants max:
100
Offered to guest students:
no
Offered to exchange students:
no
Offered as a single subject:
no
Programme
Level:
Bachelor
Programme:
BSc in Data Science
Staff
Course manager
Associate Professor
Teacher
Associate Professor
Teacher
Associate Professor
Teacher
Associate Professor
Teaching Assistant
Teaching Assistant (TA)
Teaching Assistant
Teaching Assistant (TA)
Teaching Assistant
Teaching Assistant (TA)
Teaching Assistant
Teaching Assistant (TA)
Course semester
Semester
Forår 2021
Start
1 February 2021
End
14 May 2021
Exam
Exam type
ordinær
Internal/External
intern censur
Grade Scale
7-trinsskala
Exam Language
GB
Abstract
This course aims to familiarize students with the pipeline for Data Science projects: From a domain-specific context and associated data we need to identify and formulate a domain-specific research question and translate it into a technical problem, which can then be addressed with techniques within Data Science. After performing the relevant data analysis, the results should be communicated in the context of the domain.
Description

The course consists of a series of full-fledged Data Science mini-projects from start to finish, including the initial memo, technical translation of the problem, some methodology decisions, implementation, evaluation, and translation of the results back into non-technical language. Through this course students will gain experience with online collaboration using platforms such as GitHub and Overleaf.

Formal prerequisites

This course combines knowledge from the first-semester courses Introduction to Data Science and Programming, Linear Algebra and Optimization, and Data science in Research, Business and Society with knowledge that will be acquired during the second semester from the two concurrent courses.

The course is only open for students enrolled in BSc in Data Science.
Intended learning outcomes

After the course, the student should be able to:

  • Identify and delimit a problem in Data Science within a given domain-specific context
  • Discuss the relevant options for an appropriate scientific methodology to address the problem; this covers considerations on the data-analytical approach and on the implementational approach
  • Carry out the full analysis according to the selected methodology
  • Communicate their work to both experts and non-experts; this should cover the entire pipeline from problem formulation to analysis methods and their results
Learning activities

The course comprises four group projects. Each project is associated with a lecture series on the topics of the project, exercise sessions, and project supervision.  

Course literature

The course literature is published in the course page in LearnIT.

Student Activity Budget
Estimated distribution of learning activities for the typical student
  • Preparation for lectures and exercises: 15%
  • Lectures: 10%
  • Exercises: 15%
  • Assignments: 5%
  • Project work, supervision included: 50%
  • Exam with preparation: 5%
Ordinary exam
Exam type:
X: Experimental form, Internal (7-point scale)
Exam variation:
X: Experimental form
Exam description:
F

reexam
Exam type:
B: Oral exam, Internal (7-point scale)
Exam variation:
B1I: Oral exam with time for preparation. In-house.

Time and date
Ordinary Exam - submission Fri, 26 Feb 2021, 08:00 - 14:00
Ordinary Exam - submission Fri, 19 Mar 2021, 08:00 - 14:00
Ordinary Exam - submission Fri, 23 Apr 2021, 08:00 - 14:00
Ordinary Exam - submission Thu, 3 Jun 2021, 08:00 - 14:00
Ordinary Exam Thu, 10 Jun 2021, 09:00 - 21:00
Ordinary Exam Fri, 11 Jun 2021, 09:00 - 21:00
Reexam - submission Wed, 14 Jul 2021, 08:00 - 14:00
Reexam Tue, 24 Aug 2021, 09:00 - 21:00
Reexam Wed, 25 Aug 2021, 09:00 - 21:00