Advanced Python for cognitive scientists
Informacje ogólne
Kod przedmiotu: | 2500-EN-COG-OB1Z-5 |
Kod Erasmus / ISCED: |
14.4
|
Nazwa przedmiotu: | Advanced Python for cognitive scientists |
Jednostka: | Wydział Psychologii |
Grupy: |
Cognitive Science |
Punkty ECTS i inne: |
4.00
LUB
5.00
(zmienne w czasie)
|
Język prowadzenia: | angielski |
Rodzaj przedmiotu: | obowiązkowe |
Założenia (opisowo): | (tylko po angielsku) “Introduction to programming in Python” class or equivalent. |
Tryb prowadzenia: | w sali |
Skrócony opis: |
(tylko po angielsku) 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. |
Metody i kryteria oceniania: |
Zajęcia w cyklu "Semestr zimowy 2023/24" (zakończony)
Okres: | 2023-10-01 - 2024-01-28 |
Przejdź do planu
PN WT CWW
ŚR CZ CWW
PT |
Typ zajęć: |
Ćwiczenia wykładowe, 45 godzin
|
|
Koordynatorzy: | (brak danych) | |
Prowadzący grup: | Marcin Leśniak | |
Lista studentów: | (nie masz dostępu) | |
Zaliczenie: |
Przedmiot -
Zaliczenie na ocenę
Ćwiczenia wykładowe - Zaliczenie na ocenę |
|
Pełny opis: |
(tylko po angielsku) 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. |
|
Literatura: |
(tylko po angielsku) 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 |
Zajęcia w cyklu "Semestr zimowy 2024/25" (w trakcie)
Okres: | 2024-10-01 - 2025-01-26 |
Przejdź do planu
PN WT ŚR CZ CWW
PT CWW
|
Typ zajęć: |
Ćwiczenia wykładowe, 45 godzin
|
|
Koordynatorzy: | (brak danych) | |
Prowadzący grup: | Marcin Leśniak | |
Lista studentów: | (nie masz dostępu) | |
Zaliczenie: |
Przedmiot -
Zaliczenie na ocenę
Ćwiczenia wykładowe - Zaliczenie na ocenę |
|
Pełny opis: |
(tylko po angielsku) 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. |
|
Literatura: |
(tylko po angielsku) 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 |
Właścicielem praw autorskich jest Uniwersytet Warszawski, Wydział Fizyki.