Official course description:

Full info last published 15/05-24
Course info
Language:
English
ECTS points:
7.5
Course code:
BSSODSE1KU
Participants max:
70
Offered to guest students:
yes
Offered to exchange students:
yes
Offered as a single subject:
yes
Price for EU/EEA citizens (Single Subject):
10625 DKK
Programme
Level:
Bachelor
Programme:
BSc in Data Science
Staff
Course manager
Associate Professor
Course semester
Semester
Efterår 2024
Start
26 August 2024
End
24 January 2025
Exam
Exam type
ordinær
Internal/External
ekstern censur
Grade Scale
7-trinsskala
Exam Language
GB
Abstract
The course is an introduction to software engineering and software development for data science. The goal of the course is to allow students to join professional software engineering teams that include data scientist and data analysts.
Description

NB: Course content is subject to change

In this course, students will discover the intricacies of engineering real-world systems comprising data science and machine learning components.

Through lectures, hands-on exploration, and a final small project, they will learn how to design software tailored for production and how to work in a team using Scrum. They will be exposed to concepts, principles, technologies, and tools spanning from data engineering to software monitoring.

At the end of this course, students will be equipped with the foundational knowledge and practical skills necessary to smoothly integrate into professional software engineering teams as competent data scientists.
Formal prerequisites

  • Working knowledge of an imperative programming language (e.g., Python).
  • Understanding of/appreciation for common problems in designing and developing software.

The course is mandatory for BDS students admitted 2021 and later. 

Intended learning outcomes

After the course, the student should be able to:

  • Select and apply suitable software engineering practices and tools.
  • Model and implement a small project using the techniques and technologies demonstrated throughout the course.
  • Explain the concepts of engineering systems that include data science and machine learning components.
  • Describe the Scrum process.
  • Reflect on the implications of design decisions made while engineering data science and machine learning components for real-world system.
Learning activities

The course comprises of week sessions organized as follows:

  • 2-hour lecture
  • 2-hour exercise session
Assignments will be given each week and submission requested, generally, within a week.

Mandatory activities

To access the exam, students will have to satisfy the mandatory activities requirements.

Mandatory activities might be subject to modifications prior to the course start. The final list will be communicated during the first lecture, following is a likely list:

  • Participate to the Scrum simulation
  • Submit a percentage of the weekly activities (e.g., 8 out of 10)
  • Participate to the project related events
The course will include a small size project that groups of students will be challenged with. From the second half of the course onwards, groups will have to participate to activities related to the project including a few reviews and a final project presentation. Additional details regarding the project will be communicated during the first lecture.

The student will receive the grade NA (not approved) at the ordinary exam, if the mandatory activities are not approved and the student will use an exam attempt.

Course literature

The course does not include a mandatory reading list for the course in the form of books. Throughout the course, students will be directed to relevant reading material that, combined, will form the course literature. Information on such reading will be provided in due time.

Student Activity Budget
Estimated distribution of learning activities for the typical student
  • Preparation for lectures and exercises: 10%
  • Lectures: 15%
  • Exercises: 15%
  • Assignments: 20%
  • Project work, supervision included: 30%
  • Exam with preparation: 10%
Ordinary exam
Exam type:
D: Submission of written work with following oral, External (7-point scale)
Exam variation:
D2G: Submission for groups with following oral exam supplemented by the submission. Shared responsibility for the report.
Exam duration per student for the oral exam:
15 minutes


reexam
Exam type:
D: Submission of written work with following oral, External (7-point scale)
Exam variation:
D2G: Submission for groups with following oral exam supplemented by the submission. Shared responsibility for the report.
Group submission:
Group
Exam duration per student for the oral exam:
15 minutes

Time and date
Ordinary Exam - submission Thu, 19 Dec 2024, 08:00 - 14:00
Ordinary Exam Wed, 15 Jan 2025, 09:00 - 21:00
Ordinary Exam Thu, 16 Jan 2025, 09:00 - 21:00
Ordinary Exam Fri, 17 Jan 2025, 09:00 - 21:00