Introduction to Python, 3 credits

Introduktion till Python, 3 hp

732A70

Main field of study

Computer Science

Course level

Second cycle

Course type

Programme course

Examiner

Gabriel Ducrocq

Course coordinator

Gabriel Ducrocq

Director of studies or equivalent

Jolanta Pielaszkiewicz

Contact

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 2024) 202403-202412 4 English Linköping, Valla C
F7MSL Statistics and Machine Learning, Master´s Programme - Second admission round (open only for Swedish/EU students) 2 (Spring 2024) 202403-202412 4 English Linköping, Valla C

Main field of study

Computer Science

Course level

Second cycle

Advancement level

A1N

Course offered for

  • Master's Programme in Statistics and Machine Learning

Entry requirements

  • 180 ECTS credits passed including 90 ECTS credits in one of the following subjects:
    • statistics
    • mathematics
    • applied mathematics
    • computer science
    • engineering
  • Passed courses in:
    • calculus
    • linear algebra
    • statistics
    • programming
  • English corresponding to the level of English in Swedish upper secondary education (Engelska 6)
    Exemption from Swedish
  • At least 6 ECTS credits passed from semester 1 Master's Programme in Statistics and Machine Learning, or the equivalent

Intended learning outcomes

After completion of the course the student should at an advanced level be able to: 
- Write a computer code for scientific computing using basic Python language elements
- Use simple and advanced data structures for problem solving
- Apply tools available in some commonly used Python packages
- Correct mistakes in own codes by means of debugging tools

Course content

- Python basics: programming environment and documentation, program flow, variables, comments, numerical operators, loops, conditional statements.
- Data structures: simple data types, tuples, lists, dictionaries, sets,  iterators and generators.
- Functions and functional programming, anonymous lambda functions, comprehensions.
- Classes and object oriented programming, objects and message passing
- The standard library and essential third-party packages for graphics, scientific computing and data manipulation.
- Debugging.

Teaching and working methods

The teaching comprises lectures and computer exercises. Homework and independent study are a necessary complement to the course.

Examination

Written reports on computer exercises. Detailed information about the examination can be found in the course’s study guide. 

If special circumstances prevail, and if it is possible with consideration of the nature of the compulsory component, the examiner may decide to replace the compulsory component with another equivalent component.

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 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.

An examiner may also decide that an adapted examination or alternative form of examination if the examiner assessed that special circumstances prevail, and the examiner assesses that it is possible while maintaining the objectives of the course.

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.

Grades

ECTS, EC

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 conducted in such a way that there are equal opportunities with regard to sex, transgender identity or expression, ethnicity, religion or other belief, disability, sexual orientation and age.

If special circumstances prevail, the vice-chancellor may in a special decision specify the preconditions for temporary deviations from this course syllabus, and delegate the right to take such decisions.

Department

Institutionen för datavetenskap
Code Name Scope Grading scale
LAB2 Laboration 3 credits EC
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