STKD6710 Introduction to Programming II Emneplan

This course is focusing on the development of basic programming techniques, analytical thinking, comprehension of code and problem-solving skills achieved through a programming-based approach. It also focuses on developing programming skills relevant for personal and professional use. It provides theoretical and practical exposure to different programming technologies and programming concepts such as object-oriented programming.

None.

No additional requirements to the general requirements for the Summer School.

After completing this course the student should have the following learning outcome:

Knowledge

On successful completion of this course the student has:

• basic understanding of the operation and capabilities of computers
• ability to use algorithmic problem-solving
• knowledge of the methods used to debug programs
• basic knowledge of automating processes using computers
• understanding of writing basic programs using modern programming languages

Skills

On successful completion of this course the student is:

• able to format and write basic code
• able to identify and remediate bugs
• able to apply problem solving principles to the development computer programs
• able to solve and design solutions to (simple) programming problems
• proficient at efficiently translate solutions into computer programs

General Competence On successful completion of this course the student is:

• proficient in planning and implementing a project plan for software development
• able to explain problem-solving principles
• able to recognise the place programming has within a professional domain

This is a blended learning course, with four weeks of attendance-based teaching followed by eight weeks of part-time online learning. The four-week attendance-based teaching module contains individual programming exercises and a group-programming project. This project will be evaluated based on a group oral presentation at the end of the first four weeks. This evaluation provides a basis for the following independent online study, which culminates in a submission of an individual project.

None.

Examination system:

1. An oral group-presentation of a project and a code repository. Each group may consist of 2-3 students. The presentation and code repository counts for 50% of the final grade.
2. An individual portfolio consisting of a 4,000 to 8,000 words report and a code repository. This project counts for 50% of the final grade.

Each partial exam must be assed to E or better for the course as a whole to be given a passing grade.

The oral presentation cannot be appealed.

All support materials are allowed for both the oral presentation and the individual portfolio.

Students taking the course must have a thorough knowledge of advanced calculus, including ordinary and partial differential equations. The student should also be familiar with linear algebra and Fourier and Laplace transform theory. In terms of programming, the candidate should have some experience in implementing numerical methods, including schemes for solving partial differential equations.

The candidate should also have a certain knowledge of mathematical analysis, modern physics or physiology – depending on specialization.

The course will be offered once a year, provided 3 or more students sign up for the course. If less than 3 students sign up for a course, the course will be cancelled for that year.

1. Two examiners will be used, one of which can be external.
2. One examiner will be used for the final examination

External examiner is used regularly

Students who complete the course are expected to have the following learning outcomes, defined in terms of knowledge, skills and general competence:

Knowledge

On successful completion of the course, the student:

• knows how mathematical models can be derived from facts and first principles.
• has a repertoire of methods to solve and/or analyse both ordinary differential equation (ODE) systems and certain partial differential equations (PDEs).
• is able to apply analytical and/or numerical solution methods for PDEs to models of heat transfer, wave propagation and diffusion-convection and discuss the relevance of these models to real-world phenomena.
• is able to construct and develop relevant models and discuss the validity of the models.

Skills

On successful completion of the course, the student can:

• can determine steady states of ODE systems and use linear approximation to elucidate the stability properties of these states.
• can solve and/or analyse selected PDE models.
• is able to implement and use some numerical methods for solving relevant PDEs.
• can devise the solution of certain composite quantitative problems.
• can disseminate results and findings in an accessible manner – both orally and in writing.

General competence

• is aware of the usefulness and limitations of mathematical modelling as well as of pitfalls frequently encountered in modelling and simulation.
• is able to discuss properties of a system using the equations of the mathematical model that describes the system.
• can explain and use numerical methods, know their strengths and weaknesses and interpret results of numerical simulations.