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Advanced Python for cognitive scientists

Informacje ogólne

Kod przedmiotu: 2500-EN-COG-OB1Z-5 Kod Erasmus / ISCED: 14.4 / (0313) Psychologia
Nazwa przedmiotu: Advanced Python for cognitive scientists
Jednostka: Wydział Psychologii
Grupy: Cognitive Science
Punkty ECTS i inne: 4.00
Język prowadzenia: angielski
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 2019/20" (zakończony)

Okres: 2019-10-01 - 2020-01-27
Wybrany podział planu:


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Typ zajęć: Ćwiczenia wykładowe, 45 godzin więcej informacji
Koordynatorzy: (brak danych)
Prowadzący grup: Jarosław Paszek, Szymon Talaga
Lista studentów: (nie masz dostępu)
Zaliczenie: 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.

Learning outcomes Student knows and understands:

- popular Python libraries for data analysis (K_W04)

- concepts of exploratory data analysis and data visualization (K_W08)

Student is able to:

- perform basic data analysis, build data preprocessing pipeline, program

Literatura: (tylko po angielsku)

Kevin Sheppard (2016). Introduction to Python for Econometrics,

Statistics and Numerical Analysis: Third Edition

https://www.kevinsheppard.com/images/b/b3/Python_introducti

on-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

Opisy przedmiotów w USOS i USOSweb są chronione prawem autorskim.
Właścicielem praw autorskich jest Uniwersytet Warszawski.