Advanced Python for cognitive scientists 2500-EN-CS-PM-03
This course is designed as a continuation of an introductory course on 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 the imperative style of programming, already known to students, concepts of high-level array programming (based on numpy and pandas libraries) are introduced. The general principles of inner-workings of Python are discussed for students to have better understanding of the language limitations and optimization techniques, as well as to have a broader programming skills palette.
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, manipulation, visualization. These practical skills are prerequisites for delving deeper into issues of computational modeling and data science.
Other applications of Python beside data analysis may also be explored according to students’ research interests and needs.
Learning activities:
The class will be conducted in a computer laboratory. It will consist of programming exercises interspersed with short lectures, demonstrations and discussions.
Learning outcomes
Student:
- knows and understands popular Python libraries for data analysis and concepts of exploratory data analysis and data visualization (K_W07)
- is able to choose and utilize viable Python libraries and methods to work with specific types and collections of data (K_U03, K_U07, K_U12)
- is able to browse and read technical documentation, search for new relevant libraries and tools and experiment with different approaches (K_K02)
- is aware of possibilities and limitations regarding the use of generative AI in programming (K_W06)
Assessment criteria
a) Assessment methods In-class tests & exercises.
b) Components of the final grade and their weights
i) Laboratory exercises (20%) At least two times during the semester students will be given simple problems to solve individually (or in specified cases - in pairs) during class. They will be graded on the spot by the instructor.
ii) Short in-class tests (20%) At least two times during the semester students will have to take a short quiz during class, which will cover recent classes’ topics. The attendance in-person is mandatory for the score to count.
iii) Final test (60%) The final test will be conducted during the last meeting. It may consist of a mix of single-choice, multiple-choice or open questions.
c) Grading scale
i) over 50%: 3
ii) over 60%: 3+
iii) over 70%: 4
iv) over 80%: 4+
v) over 90%: 5
d) Requirements for retaking the assessment
The final test may be retaken.
If due to absence students miss graded exercises or short tests in class, they may be given an additional homework assignment or different short test during the next meeting.
e) Exams in the exam session Not applicable
Attendance rules:
Two unexcused absences are allowed in the semester. Further unexcused absences may result in lowering the grade.
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: