NDAK14007U Applied Programming (APP)

Volume 2024/2025
Education

MSc Programme in Bioinformatics

MSc Programme in Physics

MSc Programme in Statistics

MSc Programme in Mathematics-Economics

Content

The purpose of the course is to introduce the programming language C/C++, key programming concepts in a scientific context, and guidelines for documentation. The course will enable the student to develop the C/C++ code needed to process large amounts of scientific data that cannot be handled in interpreted languages such as MATLAB, Python, Maple, or R.

The teaching will be based on examples from linear algebra.

Contents:
C/C++, problem modelling, control and data structures, encapsulation, the standard template library (STL), calls to external libraries and functions in C/C++ from, examples of interfacing  to e.g.  Python / R / MATLAB,  the use of classes and libraries including an overview of libraries for scientific programming, and introduction to object-oriented programming.

Learning Outcome

Knowledge of

  • Several programming paradigms; translated versus interpreted languages
  • Imperative control structures and basic data structures
  • Encapsulation of data states by means of structures and objects
  • Basic computer architecture and hardware limitations
  • Unit-tests

 

Skills in

  • Writing small programs in C/C++
  • Using templates / STL
  • Compiling and using external/3rd. party libraries
  • Implementing native C/C++ in a library which can be called from an interpreted language
  • Finding, reading and using documentation for C/C++ libraries
  • Using tools and structured approaches to locate and correct errors

 

Competences in

  • Translating a scientific problem into an executable program using (C/C++) to solve the problem
  • Participating in software development involving a shared codebase
Literature

The first part of the course will rely on a book.

The second part will rely on online resources.

See Absalon for the specifics when the course is set up.


 

The course requires Linear Algebra including programming experience in one of the following languages: Python / R / MATLAB / Maple.

Academic qualifications equivalent to a BSc degree is recommended.
We use flipped classroom teaching with focus on solving practical exercises which is a methodology with only a few short lectures. Exercises classes will primarily be held online and lectures can also occur online.

The course is based on assignments, which are solved at home and in class. We encourage all students to show up at the exercise classes. All assignments are a part of the evaluation, and the teachers will be present during all exercise classes.

Lectures will be given when general issues arise with respect to understanding. We will NOT give lectures covering the entire syllabus, and it is the students' responsibility to read the reading materials. However, the teachers are available to explain any topic in the syllabus at the students' request during exercise classes. We have a priori selected a few difficult topics which will be covered during exercises through short lectures.
The course is intended for all non-computer science students at the Faculty of Science, including but not limited to Physics, Math, Chemistry, Biology etc , and serves as an introduction to programming in C/C++.
  • Category
  • Hours
  • Lectures
  • 4
  • Preparation
  • 38
  • Practical exercises
  • 84
  • Exam
  • 80
  • Total
  • 206
Written
Collective
Continuous feedback during the course of the semester
Credit
7,5 ECTS
Type of assessment
Continuous assessment
Type of assessment details
Continuous assessment based on 4-6 written, individual assignments.

All assignments have to be passed in order to pass the entire course.
Aid
All aids allowed

The use of Large Language Models (LLM)/Large Multimodal Models (LMM) – such as ChatGPT and GPT-4 – is permitted.

Marking scale
passed/not passed
Censorship form
No external censorship
Several internal examiners
Re-exam

A 20 minutes oral examination without preparation, covering the entire course syllabus.

 

Criteria for exam assesment

See Learning Outcome.