Web programming, 6 credits

Webbprogrammering, 6 hp


Main field of study

Computer Science and Engineering

Course level

Second cycle

Course type

Single subject and programme course


Sahand Sadjadee

Course coordinator

Sahand Sadjadee

Director of studies or equivalent

Jalal Maleki
ECV = Elective / Compulsory / Voluntary
Course offered for Semester Weeks Timetable module Language Campus ECV
F7MSL Statistics and Machine Learning, Master´s Programme - First and main admission round 2 (Spring 2021) 202103-202112 3 English Linköping, Valla E
F7MSL Statistics and Machine Learning, Master´s Programme - Second admission round (open only for Swedish/EU students) 2 (Spring 2021) 202103-202112 3 English Linköping, Valla E

Main field of study

Computer Science and Engineering

Course level

Second cycle

Advancement level


Course offered for

  • Master's Programme in Statistics and Machine Learning

Entry requirements

A bachelor’s degree in one of the following subjects: statistics, mathematics, applied mathematics, computer science, engineering, or equivalent. Completed courses in calculus, linear algebra, statistics and programming are required. The course in programming should include object-oriented programming.
Documented knowledge of English equivalent to Engelska B/Engelska 6 

Intended learning outcomes

After completion of the course the student should on an advanced level be able to
- describe the techniques used in web programming 
- describe the content management system and its use
- use technologies such as HTML, CSS, Javascript, Python, Flask, SQL and JSON in applications that involve interactive web content. 
- develop applications for both client and server environments 
- give an account of issues related to web services, creating such services and using existing ones.

Course content

The course covers the following areas: 
- Overview of WWW, HTML, Javascript and other client-server techniques. 
- Programming languages Python, Flask, SQL, Websockets, JSON and other server-side technologies 

Teaching and working methods

The course will consist of lectures and dator laboratory exercises. Homework and independent study are a necessary complement to the course. Language of instruction: English. 


Project work and laboratory work. Detailed information about the examination can be found in the course’s study guide.

If the LiU coordinator for students with disabilities has granted a student the right to an adapted examination for a written examination in an examination hall, the student has the right to it. If the coordinator has instead recommended for the student an adapted examination or alternative form of examination, the examiner may grant this if the examiner assesses that it is possible, based on consideration of the course objectives.

Students failing an exam covering either the entire course or part of the course twice are entitled to have a new examiner appointed for the reexamination.

Students who have passed an examination may not retake it in order to improve their grades.



Other information

Planning and implementation of a course must take its starting point in the wording of the syllabus. The course evaluation included in each course must therefore take up the question how well the course agrees with the syllabus. 

The course is carried out in such a way that both men´s and women´s experience and knowledge is made visible and developed.


Institutionen för datavetenskap
Code Name Scope Grading scale
LAB1 Project and laboratory work 6 credits EC
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