Advanced Python for cognitive scientists 2500-EN-COG-OB1Z-5
Ćwiczenia wykładowe (CWW) Semestr zimowy 2019/20

Literature:

• Kevin Sheppard (2016). Introduction to Python for Econometrics, Statistics and Numerical Analysis: Third Edition https://www.kevinsheppard.com/images/b/b3/Python_introduction-2016.pdf

• Nicolas P. Rougier (2017). From Python to Numpy

http://www.labri.fr/perso/nrougier/from-python-to-numpy/

• Wes McKinney (2017). Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython: Second Edition

Laboratory exercises (40%) At least two times during the semester students will be given simple problems to solve individually during class. They will be graded on the spot by the instructor.

Homework assignments (60%) At least three times during the semester students will be assigned short problems to solve at home. These assignments may have more open format. Time to solve each assignment should be 1-2 weeks.

Two unexcused absences are allowed in the semester. Further unexcused absences may result in lowering the grade. If due to absences students miss graded exercises in class, they may be given an additional homework assignment.

1. Programming languages and paradigms. Review of Python syntax.

2. Advanced iteration and data structures (modules: collections, itertools).

3. Writing beautiful code. Stylistic exercises. PEP8 standard.

4. File loading, text processing, regular expressions.

5. Array processing in numpy. Vectorizing operations.

6. Advanced numpy indexing, broadcasting.

7. Basics of scientific computing with numpy and scipy. Implementing gradient descent algorithm.

8. Cellular automata. Game of life. Image processing with convolution matrices.

9. Shelling’s segregation mode: implementing simple agent-based simulation.

10. Data munging with pandas I. Data frames, grouping, aggregation.

11. Data munging with pandas II. Time series.

12. Data visualizations.

13. Exploratory data analysis example.

14. Optimizing performance. Pytorch and numba.