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Advanced Python for cognitive scientists (2nd part of the semester)

General data

Course ID:	2500-EN-COG-OB1Z-5
Erasmus code / ISCED:	14.4 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. / (0313) Psychology The ISCED (International Standard Classification of Education) code has been designed by UNESCO.
Course title:	Advanced Python for cognitive scientists (2nd part of the semester)
Name in Polish:	Advanced Python for cognitive scientists
Organizational unit:	Faculty of Psychology
Course groups:	(in Polish) Cognitive Science
ECTS credit allocation (and other scores):	4.00 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.
Language:	English
Type of course:	obligatory courses
Prerequisites (description):	“Introduction to programming in Python” class or equivalent.
Mode:	Classroom
Short description:	The goal of the course is to build fluency in using Python programming language as a tool for scientific computing, data manipulation and visualization. We will introduce libraries which constitute a core of Python ecosystem for data analysis: numpy, scipy, pandas, matplotlib. After covering the basics, students will have the opportunity to hone their skills by working through a number of applications of the introduced tools in data analysis. Simultaneously, they will be improving their programming style and learning about good programming practices. Previous experience with Python is necessary.
Assessment methods and assessment criteria:

Classes in period "Winter semester 2023/24" (past)

Time span:	2023-10-01 - 2024-01-28	Selected timetable range: weekly course term Navigate to timetable MO TU CWW W TH CWW FR
Type of class:	Inter-active lecture, 45 hours more information
Coordinators:	(unknown)
Group instructors:	Marcin Leśniak
Students list:	(inaccessible to you)
Examination:	Course - Grading Inter-active lecture - Grading
Full description:	This course is designed as a continuation of an introductory course of Python programming. It is assumed that students know the basics of language syntax and are able to write simple programs on their own. In this class they will expand their knowledge of the language, get to know popular Python libraries, and learn practical applications of their skills. In addition to imperative style of programming, already known to students, concepts of high-level array programming (based on numpy and pandas libraries) are introduced. The focus is on scientific computing and exploratory data analysis. Libraries covered include numpy, scipy, pandas, matplotlib. Students learn important aspects of data literacy: data preprocessing, data manipulation, data visualization. These practical skills are prerequisites for delving deeper into issues of computational modeling and data science.
Bibliography:	Recommended (not obligatory) readings: 1. Sheppard, K. (2016). Introduction to Python for Econometrics, Statistics and Numerical Analysis: Third Edition https://www.kevinsheppard.com/files/teaching/python/notes/python_introduction_2019.pdf 2. Rougier, N.P. (2017). From Python to Numpy http://www.labri.fr/perso/nrougier/from-python-to-numpy/ 3. McKinney, W. (2017). Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython: Second Edition

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