Poslední úprava: Mgr. Jan Šíla, M.Sc. (06.02.2023)
The course is taught in person and we expect students to come to the class to attend the lectures and seminars.
The aim of the course is to provide hands-on experience in programming in Python with a special emphasis on data manipulation and processing.
Students will get the basics of Pandas, Numpy or Matplotlib and also collect web data with API requests and BeatifiulSoup. The students will also be guided through modern social-coding and open-source technologies such as GitHub, Jupyter and Open Data.
The students will gain experience using the data from the IES website and subject evaluation protocols.
The course would make use of the DataCamp online sources ( https://www.datacamp.com ) to provide the students with reliable yet simple resources for learning Python programming.
Poslední úprava: Mgr. Jan Šíla, M.Sc. (06.02.2023)
The course is taught in person and we expect students to come to the class to attend the lectures and seminars.
The aim of the course is to provide hands-on experience in programming in Python with a special emphasis on data manipulation and processing.
Students will get the basics of Pandas, Numpy or Matplotlib and also collect web data with API requests and BeatifiulSoup. The students will also be guided through modern social-coding and open-source technologies such as GitHub, Jupyter and Open Data.
The students will gain experience using the data from the IES website and subject evaluation protocols.
The course would make use of the DataCamp online sources ( https://www.datacamp.com ) to provide the students with reliable yet simple resources for learning Python programming.
Cíl předmětu -
Poslední úprava: SCHNELLEROVA (24.10.2019)
Please switch to the english version.
Poslední úprava: Mgr. Martin Hronec (06.02.2020)
After passing the course, the students will be able to execute a software-based, data-oriented project in Python, specifically download the data from APIs or directly from the web, pre-process it, analyze it and visualize it. Further, they will be able to do it in a repeatable, standard software-development quality manner using version control.
Literatura -
Poslední úprava: Mgr. Michaela Čuprová (02.02.2020)
Book
Wes McKinney: Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython, O'Reilly, 2012
Grading scale (according to Dean's Provision 17/2018):
A: above 90 (not inclusive)
B: between 80 (not inclusive) and 90 (inclusive)
C: between 70 (not inclusive) and 80 (inclusive)
D: between 60 (not inclusive) and 70 (inclusive)
E: between 50 (not inclusive) and 60 (inclusive)
F: below 50 (inclusive)
Sylabus -
Poslední úprava: PhDr. Petr Bednařík, Ph.D. (15.02.2020)
Previous experience with general coding is assumed - The course is designed for students that have at least some basic coding experience. It does not need to be very advanced, but they should be aware of concepts such as for loop, if and else, variable or function.
No knowledge of Python is required for entering the course.
Previous experience with general coding is assumed - The course is designed for students that have at least some basic coding experience. It does not need to be very advanced, but they should be aware of concepts such as for loop, if and else, variable or function.
No knowledge of Python is required for entering the course.
Week
Date
L/S
Topic
Lecturer
Deadline
1
2.10.
S
Seminar 0: Setup (Jupyter, VScode, Git, OS basics)
Martin
1
3.1
L
Python basics
Martin
2
10.10.
L
Python basics II
Jan
3
16.10.
S
Seminar 1: Basics
Alena
HW 1
3
17.10.
L
Numpy
Jan
4
24.10.
L
Pandas I
Martin
5
30.10.
S
Seminar 2: Numpy & pandas
Alena
HW 2
5
31.10.
L
Pandas II + Matplotlib
Martin
6
7.11.
L
Data formats, APIs
Jan
7
13.11.
S
Seminar 3: Data formats & APIs
Alena
HW 3
7
14.11.
L
Algorithmic problem solving
Jan
8
21.11.
-
MIDTERM
Alena, Jan & Martin
9
27.11.
S
MIDTERM solution
Alena
9
28.11.
L
Data science
Martin
Project proposal
10
5.12.
L
How to code (avoiding spaghetti code)
Martin
Topic approved
11
11.12.
S
Seminar 5: Data science case-study
Alena
11
12.12.
L
Databases
Jan
12
19.12.
L
Guest lecture (TBA) + Python Beer
Alena, Jan & Martin
2.1.
-
-
WiP: Project consultations
Alena, Jan & Martin
9.1.
-
-
WiP: Project consultations
Alena, Jan & Martin
Vstupní požadavky -
Poslední úprava: SCHNELLEROVA (24.10.2019)
Please switch to the english version.
Poslední úprava: Mgr. Martin Hronec (06.02.2020)
Previous experience with general coding is assumed - The course is designed for students that have at least some basic coding experience. It does not need to be very advanced, but they should be aware of concepts such as for loop, if and else, variable or function.
No knowledge of Python is required for entering the course.
Požadavky k zápisu -
Poslední úprava: SCHNELLEROVA (24.10.2019)
Please switch to the english version.
Poslední úprava: Mgr. Martin Hronec (04.10.2022)
The course is primarily for master and advanced bachelor students.
