1. Bachelor of Science (at least 180 higher education credits) in computer science or related subjects such as mathematics, informatics, telecommunications, electrical engineering, physics.
2. Knowledge equivalent to English 6 at Swedish upper secondary level.
3. At least 15 credits in programming.
4. At least 7.5 credits in mathematics.

The course is part of the programme Computer Science: Applied Data Science, master’s programme, and can be included in the master's degree in computer science (120 credits).

The course contains the following elements:

The course conveys perspectives on software development techniques and joint project work, as well as current advances in software development. Some techniques and methods that are addressed are:

• Data-driven innovation and data-driven decision making in research and development of artifacts
• A-B testing, data collection techniques
• Scrum and Kanban Software development framework that implements Agile and Lean methods
• Distributed software development
• Test-driven development
• DevOps/DataOps/MLOps principles and practice
• Challenges to achieving high-performance groups for software development
• Data-driven innovation and ML workflow lifecycle: how collected user data can be introduced to the software development cycle, during the design, implementation, evaluation and maintenance phases.

Knowledge and understanding
For a passing grade the student shall be able to:

• Demonstrate an understanding of cultural and social challenges in assembling, leading and participating in high-performance groups for software development
• Describe the characteristics of different software development techniques

Competence and abilities
For a passing grade the student shall be able to:

• Identify how activities can be reviewed and improved using collected user data
• Prepare and choose strategies and methods for implementing effective software development projects, as well as deploying and maintaining ML systems in production reliably and efficiently
• Practice the use of academic language to present and reflect, in written as well as verbal form

Evaluation abilities and approach
For a passing grade the student shall be able to:

• Make assessments in the field of computer science, taking into consideration relevant practical, scientific, societal and ethical aspects
• Identify his or her need for further knowledge and take responsibility for his or her own continued development in the field

Lectures, seminars.

Requirements for pass: the course is assessed through

• a pre-study report in a group presented verbally and in writing (3.5 credits, UA), and
• an individual written reflection (4 credits, UA).

An A-E passing grade requires that all parts have been completed and passed.

The final grade is based on the individual written reflection.

• Kohavi, Ron. Trustworthy Online Controlled Experiments A Practical Guide to A/B Testing, 2020
• Ståhl, Daniel and Mårtensson, Torvald. Continuous Practices: A Strategic Approach to Accelerating the Software Production System, 2018
• A collection of scientific articles.

The University provides students who are taking or have completed a course with the opportunity to share their experiences of and opinions about the course in the form of a course evaluation that is arranged by the University. The University compiles the course evaluations and notifies the results and any decisions regarding actions brought about by the course evaluations. The results shall be kept available for the students. (HF 1:14).

When a course is no longer given, or the contents have been radically changed, the student has the right to re-take the examination, which will be given twice during a one year period, according to the syllabus which was valid at the time of registration.

If a student has a Learning support decision, the examiner has the right to provide the student with an adapted test, or to allow the student to take the exam in a different format.

The syllabus is a translation of a Swedish source text.