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Scientific Computing and Data Science

Title
Scientific Computing and Data Science
Semester
F2023
Master programme in
Mathematical Bioscience / Physics and Scientific Modelling
Type of activity

Course

Teaching language
English
Study regulation

Read about the Master Programme and find the Study Regulations at ruc.dk

Læs mere om uddannelsen og find din studieordning på ruc.dk

REGISTRATION AND STUDY ADMINISTRATIVE
Registration

Sign up for study activities at stads selvbetjening within the announced registration period, as you can see on the Studyadministration homepage.

When signing up for study activities, please be aware of potential conflicts between study activities or exam dates.

The planning of activities at Roskilde University is based on the recommended study programs which do not overlap. However, if you choose optional courses and/or study plans that goes beyond the recommended study programs, an overlap of lectures or exam dates may occur depending on which courses you choose.

Number of participants
ECTS
10
Responsible for the activity
Thomas Schrøder (tbs@ruc.dk)
Head of study
Kristine Niss (kniss@ruc.dk)
Teachers
Study administration
INM Studieadministration (inm-studieadministration@ruc.dk)
Exam code(s)
U60190
ACADEMIC CONTENT
Overall objective

To give the student experience in choosing and applying the methods of Scientific Computing and Data Science to new problems and to give the student an overview of methods associated with: - Scientific Computing, i.e., the use of computers and applied math to generate data from models by numerical methods and/or simulation. - Data Science, i.e., the use of computers, models, and applied math to gain insight from data.

Detailed description of content

The aim of the course is to give the student experience in choosing and applying methods of Scientific Computing and Data Science to (for the student) new problems and to give the student an overview of methods associated with the two subjects:

  • Scientific Computing, i.e., the use of computers and applied math to generate data from models by numerical methods and/or simulation. In the first mini-project students will work in groups to implement the so-called molecular-dynamics method in Python. This includes the relevant testing and interpretation of results.

  • Data Science, i.e., the use of computers, models, and applied math to gain insight from data. In the second mini-project, students will work in groups to apply data science methods to scientific data of their own choice, and report their conclusions in a convincing way.

In the third and final mini-projct students groups can choose between the two subjects (Scientific Computing and Data Science) or a combination thereof. If a single subject is chosen, the mini-project must include a discussion of how the other subject could be involved.

Course material and Reading list

To be specified on moodle

Overall plan and expected work effort

10 ECTS course

  • Reading course material and problem solving at home: 70 hrs

  • Discussion and problem solving in class: 30 hrs

  • Working on mini-projects at home: 100 hrs

  • Working on mini-projects in class: 60 hrs

  • Exam preparation: 9 hrs

  • Exam: 1 hours

- Total 270 hrs

Format
Evaluation and feedback

The course includes formative evaluation based on dialogue between the students and the teacher(s).

Students are expected to provide constructive critique, feedback and viewpoints during the course if it is needed for the course to have better quality. Every other year at the end of the course, there will also be an evaluation through a questionnaire in SurveyXact. The Study Board will handle all evaluations along with any comments from the course responsible teacher.

Furthermore, students can, in accordance with RUCs ‘feel free to state your views’ strategy through their representatives at the study board, send evaluations, comments or insights form the course to the study board during or after the course.

Programme

Theme 1: Scientific Computing. First mini-project: Molecular Dynamics

Theme 2: Data Science. Second mini-project: Application of data science methods to scientific data

Theme 3: Third mini-project: Scientific computing and/or data science

ASSESSMENT
Overall learning outcomes

After completing the course the students will be able to

  • demonstrate an overview of methods in Scientific Computing and Data Science.

  • choose methods in Scientific Computing and Data Science relevant for a given problem.

  • independently learn about methods in Scientific Computing and Data Science on an advanced level.

  • apply methods in Scientific Computing and Data Science to a new problem. This includes the relevant programming, testing, and interpretation of results.

Form of examination

Individual oral exam based on a portfolio.

The character limit of the portfolio is 1,200-120,000 characters, including spaces. Examples of written products are exercise responses, talking points for presentations, written feedback, reflections, written assignments. The preparation of the products may be subject to time limits.
The character limits include the cover, table of contents, bibliography, figures and other illustrations, but exclude any appendices.

Time allowed for exam including time used for assessment: 30 minutes.
The assessment is an assessment of the oral examination. The written product(s) is not part of the assessment.

Permitted support and preparation materials for the oral exam: All.

Assessment: 7-point grading scale.
Moderation: Internal co-assessor
Form of Re-examination
Samme som ordinær eksamen
Type of examination in special cases
Examination and assessment criteria

The students produce a portefolio consisting of 3 mini-projects. All 3 can be handed-in for review by the teacher.

At the exam the student makes a presentation of the third mini-project. The presentation may be interrupted by clarifying questions and the presentation will be followed by a discussion and questioning with in the curriculum of the course.

Students will be assessed by their ability to:

  • apply methods in Scientific Computing and Data Science to a new problem. This includes the relevant programming, testing, and interpretation of results.

  • argue for choosing specific methods in Scientific Computing and Data Science relevant for a given problem.

  • demonstrate that they have independently learnt about methods in Scientific Computing and Data Science on an advanced level.

The assessment of the oral exam is based on the student’s ability to meet the criteria mentioned above and their ability to

  • clearly present and communicate the scientific content of the portfolio

  • engage in a scientific dialogue and discussion with the assessor and co assessor

Furthermore, whether the performance meets all formal requirements in regard to both for the written og oral exam

Exam code(s)
Exam code(s) : U60190
Last changed 17/05/2022

lecture list:

Show lessons for Subclass: 1 Find calendar (1) PDF for print (1)

Tuesday 07-02-2023 08:15 - 07-02-2023 12:00 in week 06
Scientific Computing and Data Science (MathBio)

Thursday 09-02-2023 12:15 - 09-02-2023 14:00 in week 06
Scientific Computing and Data Science (MathBio) - please note room in 12.1 due to RUC Open House

Tuesday 14-02-2023 08:15 - 14-02-2023 12:00 in week 07
Scientific Computing and Data Science (MathBio)

Thursday 16-02-2023 12:15 - 16-02-2023 14:00 in week 07
Scientific Computing and Data Science (MathBio)

Tuesday 21-02-2023 08:15 - 21-02-2023 12:00 in week 08
Scientific Computing and Data Science (MathBio)

Thursday 23-02-2023 12:15 - 23-02-2023 14:00 in week 08
Scientific Computing and Data Science (MathBio)

Tuesday 28-02-2023 08:15 - 28-02-2023 12:00 in week 09
Scientific Computing and Data Science (MathBio)

Thursday 02-03-2023 12:15 - 02-03-2023 14:00 in week 09
Scientific Computing and Data Science (MathBio)

Tuesday 07-03-2023 08:15 - 07-03-2023 12:00 in week 10
Scientific Computing and Data Science (MathBio)

Thursday 09-03-2023 14:15 - 09-03-2023 16:00 in week 10
Scientific Computing and Data Science (MathBio)

Tuesday 14-03-2023 08:15 - 14-03-2023 12:00 in week 11
Scientific Computing and Data Science (MathBio)

Thursday 16-03-2023 14:15 - 16-03-2023 16:00 in week 11
Scientific Computing and Data Science (MathBio)

Tuesday 21-03-2023 08:15 - 21-03-2023 12:00 in week 12
Scientific Computing and Data Science (MathBio)

Thursday 23-03-2023 14:15 - 23-03-2023 16:00 in week 12
Scientific Computing and Data Science (MathBio)

Tuesday 28-03-2023 10:15 - 28-03-2023 12:00 in week 13
Scientific Computing and Data Science (MathBio)

Tuesday 04-04-2023 10:15 - 04-04-2023 12:00 in week 14
Scientific Computing and Data Science (MathBio)

Tuesday 11-04-2023 10:15 - 11-04-2023 12:00 in week 15
Scientific Computing and Data Science (MathBio)

Friday 14-04-2023 08:15 - 14-04-2023 12:00 in week 15
Scientific Computing and Data Science (MathBio)

Tuesday 18-04-2023 10:15 - 18-04-2023 12:00 in week 16
Scientific Computing and Data Science (MathBio)

Friday 21-04-2023 08:15 - 21-04-2023 12:00 in week 16
Scientific Computing and Data Science (MathBio)

Tuesday 25-04-2023 10:15 - 25-04-2023 12:00 in week 17
Scientific Computing and Data Science (MathBio)

Friday 28-04-2023 08:15 - 28-04-2023 12:00 in week 17
Scientific Computing and Data Science (MathBio)

Tuesday 02-05-2023 10:15 - 02-05-2023 12:00 in week 18
Scientific Computing and Data Science (MathBio)

Thursday 04-05-2023 12:15 - 04-05-2023 16:00 in week 18
Scientific Computing and Data Science (MathBio)

Tuesday 09-05-2023 10:15 - 09-05-2023 12:00 in week 19
Scientific Computing and Data Science (MathBio)

Friday 12-05-2023 08:15 - 12-05-2023 12:00 in week 19
Scientific Computing and Data Science (MathBio)

Tuesday 16-05-2023 10:15 - 16-05-2023 12:00 in week 20
Scientific Computing and Data Science (MathBio)

Friday 19-05-2023 08:15 - 19-05-2023 12:00 in week 20
Scientific Computing and Data Science (MathBio)

Tuesday 23-05-2023 10:15 - 23-05-2023 12:00 in week 21
Scientific Computing and Data Science (MathBio)

Friday 26-05-2023 08:15 - 26-05-2023 12:00 in week 21
Scientific Computing and Data Science (MathBio)

Tuesday 30-05-2023 08:15 - 30-05-2023 12:00 in week 22
Scientific Computing and Data Science (MathBio)

Monday 12-06-2023 10:00 - 12-06-2023 10:00 in week 24
Scientific Computing and Data Science - Hand-in of portfolio (MathBio)

Thursday 15-06-2023 08:15 - 15-06-2023 16:00 in week 24
Scientific Computing and Data Science - Exam (MathBio)

Friday 30-06-2023 10:00 - 30-06-2023 10:00 in week 26
Scientific Computing and Data Science - Hand-in of portfolio (reexam) (MathBio)

Thursday 03-08-2023 08:15 - 03-08-2023 16:00 in week 31
Scientific Computing and Data Science - Reexam (MathBio)