General provisions

This course substitutes and corresponds to 3FB617.

Entry requirements

Admitted to the Master programme in Drug Modelling

Learning outcomes

After having completed the course, the students should be knowledgeable in the principles of programming in R for the purpose of data management, visualisation of data (plotting) and basic statistical calculations as well as programming in Python. Specifically, the students should be able to:

• Explain and use basic concepts in programming
• Construct and execute basic programs in Python
• Design and implement basic algorithms in Python
• Use external libraries with Python
• Construct and execute basic programs in R using elementary programming techniques, e.g. import/export of data from file or Internet, assign and manipulate data structures, create user-defined functions, loops, condition statements and debugging.
• Use R for statistical calculations
• Implement and describe Monte Carlo techniques as well as perform simulation studies with analysis and evaluation of result
• Graphically visualise data and results of statistical calculations
• Use external R-packages in statistics and data mining

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.

Both the computer labs and project work are tasks to be solved and reported individually. For computer labs and project work we encourage collaborations. Students are encouraged to discuss data, code and problems with each other, but the tasks are handed in individually.

Many projects will explicitly encourage you to use resources on the Internet, but it is unacceptable to copy verbatim from outside sources. Reference the source in a comment in your code.

Attendance is required at the course introduction and computer labs.

Assessment

Examination takes place at the end of the course. For a passing grade the student must pass the examination (examination code) and all parts of the course that are obligatory (examination code). 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.