Introduction to Julia for natural sciences
The course format will include morning lectures followed by practical exercises. The interactive development environment of Jupyter (www.jupyter.org) will be used throughout the course. In addition, we will use Julia’s REPL and the Visual Studio editor. Basic Julia syntax will be introduced and students will gradually build core skills required to efficiently handle various types of datasets. The students will also be introduced to the DataFrames package and practice data manipulation and aggregation techniques in example datasets. Finally, the students will gain experience in producing informative graphs using the Plots package or similar.
Syllabus and other information
Syllabus
P000084 Introduction to Julia for natural sciences, 2.0 Credits
Subjects
Education cycle
Postgraduate levelGrading scale
Language
EnglishPrior knowledge
Admitted to a PhD or residency program in biology, medicine, nursing, veterinary medicine, animal science, food science, nutrition or similar topics. No prior programming experience is required.Objectives
After completing this course, the students should be able to:
- Write and understand basic Julia code
- Write basic functions in Julia
- Perform basic dataset analysis using the DataFrames package of Julia
- Deploy simple machine learning models in Julia
- Create static and interactive plots
Content
The course format will include morning lectures followed by practical exercises. The interactive development environment of Jupyter (www.jupyter.org) will be used throughout the course. In addition, we will use Julia’s REPL and the Visual Studio editor. Basic Julia syntax will be introduced and students will gradually build core skills required to efficiently handle various types of datasets. The students will also be introduced to the DataFrames package and practice data manipulation and aggregation techniques in example datasets. Finally, the students will gain experience in producing informative graphs using the Plots package or similar.
Additional information
The course will take the form of distance learning using Zoom or a similar platform. The course consists of five full-day meetings comprised of lectures and computer exercises. In addition, the students are expected to do individual work before the start of the course and in between meetings.Responsible department
Department of Animal Biosciences