Programming for neuroinformatics

General data

Course ID:	1100-2BN27
Erasmus code / ISCED:	11.001 Kod klasyfikacyjny przedmiotu składa się z trzech do pięciu cyfr, przy czym trzy pierwsze oznaczają klasyfikację dziedziny wg. Listy kodów dziedzin obowiązującej w programie Socrates/Erasmus, czwarta (dotąd na ogół 0) – ewentualne uszczegółowienie informacji o dyscyplinie, piąta – stopień zaawansowania przedmiotu ustalony na podstawie roku studiów, dla którego przedmiot jest przeznaczony. / (0540) Mathematics and statistics, not further defined The ISCED (International Standard Classification of Education) code has been designed by UNESCO.
Course title:	Programming for neuroinformatics
Name in Polish:	Programowanie dla neuroinformatyków
Organizational unit:	Faculty of Physics
Course groups:
Course homepage:	http://brain.fuw.edu.pl/edu/TI:Programowanie_II
ECTS credit allocation (and other scores):	(not available) Basic information on ECTS credits allocation principles: the annual hourly workload of the student’s work required to achieve the expected learning outcomes for a given stage is 1500-1800h, corresponding to 60 ECTS; the student’s weekly hourly workload is 45 h; 1 ECTS point corresponds to 25-30 hours of student work needed to achieve the assumed learning outcomes; weekly student workload necessary to achieve the assumed learning outcomes allows to obtain 1.5 ECTS; work required to pass the course, which has been assigned 3 ECTS, constitutes 10% of the semester student load. view allocation of credits
Language:	Polish
Prerequisites (description):	Ability to write simple Python programs and construct simple algorithms.
Mode:	Classroom
Short description:	A continuation of basics of Python course. Introduction to Object Oriented Programming and parsing of different data storage formats.
Full description:	Course program: 1. Advanced Python Syntax. 2. Advanced Functions. 3. Advanced Matplotlib + Numpy. 4. Advanced Input and Output. 5. Object Oriented Programming: private variables, property, *exercises. 6. Decorators. 7. Introduction to Graphs. 8. Basics of Matlab. 9. Trees. 10. Recursion 11. Basics of XML and XML parsing. 12. Basics of computational complexity theory.
Bibliography:	Course material can be found http://brain.fuw.edu.pl/edu/TI:Programowanie_II
Learning outcomes:	Students should be able to write their own scripts and programs performing advanced data analysis and be able to use, change and understand programs, that were written by someone else.
Assessment methods and assessment criteria:	2 tests.
Practical placement:	None.

This course is not currently offered.

Course descriptions are protected by copyright.
Copyright by University of Warsaw.