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University degree in medicine, dentistry, nursing, or Master's degree in other fields and/or postgraduate research fellows (PhD students and research-year students).
Medical professionals and other specialists now have the opportunity to gain insight into physiological mechanisms, both normal and pathological, using data driven analysis strategies that not long ago were only applicable to those with access to supercomputing services. With the proliferation of quantitative research tools, and the simultaneous commoditisation of computational power, comes a need for all researchers to understand the foundations on which scientific computing is based. Such an understanding is the basis for learning a programming language, and enables publication of documented and reproducible data analyses. The aim of the present course is to lay the conceptual foundation with which the student may begin to apply data-driven research practices, and to support her advencement to more specialised topics such as C119: "Stata and Datadocumentation" and C171: "Introduction to MATLAB in Neuroimaging".
//: # (Define according to Bloom's Taxonomy (or similar), cf. ILOs.pdf) //: # ( * NB: storage, memory, CPU, (GPU), input devices, output devices, network) //: # ( * NB: could be of relevance if talking about large files processed on a remote server)
Successful completion of the course will enable the student to:
- Summarize how the main components of a computer relate to, and constrain, the act of "computing".
- Describe the basic organisation of a file system, and navigate it using commands in a "terminal".
- Contrast textual and binary files in terms of their contents and find information in both using tools that can be automated.
- Contrast local and non-local computing resources and file systems, and formulate use cases for both.
- Use variables in a programming language (python) and perform simple operations (manipulations) on the information (data) they contain.
- Write a program to extract, collate and preprocess "raw" data for further processing (statistics, visualisation, etc.).
Teaching is in the form of "interactive lectures", where students follow instructions to complete specific learning tasks (hands-on), after introduction by the lecturers.
- Day 1: The anatomy of a computer and data
- Day 2: The anatomy and building blocks of a program
- Day 3: Programming as a means to gain insight into data
No prior knowledge on the topics covered is required. Participants must bring their own laptop with a hard drive (no Chromebooks or tablet-/surface-type devices are supported). Any reasonably modern (< 5-year-old) Windows, OS X or Linux operating system will do.
English
Will be provided before the beginning of course.
Mandatory assignment (pass/fail, internal examination)
To be determined by HEALTH
Mads Jensen (mads@cfin.au.dk)
- Mads Jensen (CFIN, AU)
- Christopher Bailey (CFIN, AU; cjb@cfin.au.dk)
- Teaching assistants: from CFIN PhD student pool
To be determined. If a large enough locale is made available, perhaps just a large lecture theatre, we are prepared to open this up to tens of participants, perhaps even up to about 50!
Three days of teaching (Mon, Wed, Fri), possible starting dates:
- Mon 4 September (week 36)
- Mon 11 September (week 37)
Suitable venue will be determined after enrolment. Auditorium is fine, though flat teaching room slightly preferred.