Syllabus for Introduction to Programming in Python and R for Bioscience

Introduktionskurs i programmering i Python och R inom biovetenskap

  • 7.5 credits
  • Course code: 3FB221
  • Education cycle: First cycle
  • Main field(s) of study and in-depth level: Pharmaceutical Sciences G2F
  • Grading system: Fail (U), Pass (G), Pass with distinction (VG)
  • Established: 2016-05-26
  • Established by: The Educational Board of Pharmacy
  • Revised: 2018-05-24
  • Revised by: The Educational Board of Pharmacy
  • Applies from: week 27, 2018
  • Entry requirements: Admitted to the Master programme in Drug Modelling and participated in earlier courses in the program
    or
    120 credits within science and technology or pharmacy
  • Responsible department: Department of Pharmaceutical Biosciences

Learning outcomes

On the completion of the course, the student shall be able to:

  • Explain and use basic concepts in programming
  • Construct and execute basic programs in Python and R
  • Design and implement basic algorithms in Python and R
  • Use external libraries with Python and R-packages
  • Use R for statistical calculations
  • Graphically visualise data and results of statistical calculations

Content

This course consists of two parts. During the first two weeks of the course focus is on Python programming and during the remaining three weeks focus is on R programming.
The Python part of the course will give a general introduction to programming, and students will learn and practice introductory programming concepts using the Python programming language. Focus lies on how to think computationally and students will learn and practice to write small programs to tackle problems. The course will also contain a section on how to use code written by other programmers in your own Python programs.
In the R part of the course the tools needed for data analysis, in particular for large dataset will be taught. The student will learn how to take a large dataset break up into manageable pieces and use a range of qualitative and quantitative tools to summarise it and learn what it has to tell. The importance of how to communicate the findings will be an emphasis. Each section of the course is motivated by a particular dataset, and the student will gain experience working with a wide variety of data sources varying in size and quality. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, and organising and commenting R code. Topics in statistical data analysis and optimisation will provide working examples.

Instruction

Teaching consists of lectures, computer labs and project work.
The project work is an obligatory task to be solved and reported individually. 
Attendance is required at the course introduction.

Assessment

Written examination takes place at the end of the course. For a passing grade the student must pass the written examination (0,5 credits), pass on the project work in Python (3 credits) and R (4 credits) as well as pass on all parts of the course that are obligatory. A chance to finalise a failed compulsory part can be arranged only at the next course occasion and only in case of a vacancy. Students who have failed the first examination are allowed five re-examinations.

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the University´s disability coordinator.
 

Other directives

This course substitutes and corresponds to 3FB617.

Reading list

The reading list is missing. For further information, please contact the responsible department.