SubjectsSubjects(version: 970)
Course, academic year 2024/2025
   Login via CAS
--:--
 
 Search ... Teachers Departments Classes Classification View by plans Setup
 
 
 
 Details
 
 
 
 
Data Processing in Python - JEM207
Title: Data Processing in Python
Czech title: Data Processing in Python
Guaranteed by: Institute of Economic Studies (23-IES)
Faculty: Faculty of Social Sciences
Actual: from 2024
Semester: both
E-Credits: 5
Examination process: written
Hours per week, examination: 2/2, Ex [HT]
Capacity: winter:30 / unlimited (60)
summer:unknown / unknown (60)
Min. number of students: unlimited
4EU+: no
Virtual mobility / capacity: no
State of the course: taught
Language: English
Teaching methods: full-time
Additional information: https://github.com/iesfsv/Data-Processing-in-Python
Note: course can be enrolled in outside the study plan
enabled for web enrollment
priority enrollment if the course is part of the study plan
you can enroll for the course in winter and in summer semester
Guarantor: Mgr. Luboš Hanus, Ph.D.
Teacher(s): Mgr. Luboš Hanus, Ph.D.
Mgr. Vilém Krejcar
Class: Courses for incoming students
Annotation -
There is no ONLINE version for SS 2024/2025

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 collect web data with API requests. 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.
Last update: Hanus Luboš, Mgr., Ph.D. (18.02.2025)
Aim of the course -

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.

Last update: Hronec Martin, Mgr., Ph.D. (06.02.2020)
Literature -
Teaching methods -

Please see the course GitHub repository (Data-Processing-in-Python).

Last update: Krejcar Vilém, Mgr. (01.10.2024)
Requirements to the exam -

The final grade consists of four parts:

  • Final project (60%)
  • Midterm (25%)
  • Final project work-in-progress presentation (10%)
  • Homework assignments (5%) - leetcode.com

Important Note: To pass the course, students must achieve at least 50% of the points from both the work-in-progress presentation and homework assignments.

For more details about the course, please visit the GitHub repository: Data Processing in Python at IES.

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)
Last update: Krejcar Vilém, Mgr. (01.10.2024)
Syllabus -

Here’s your schedule with the semester beginning on February 17th:

WEEKDATEL/STOPICLECTURERDEADLINE
1 17/2 S Seminar 0: Setup (Jupyter, VScode, Git, OS basics) Luboš
1 18/2 L Python basics Luboš
2 25/2 L Python basics II Luboš
2 26/2 S Seminar 1: Basics Vilém
3 3/3 L Numpy Luboš HW 1
4 10/3 L Pandas I Luboš
4 11/3 S Seminar 2: Numpy & pandas Vilém HW 2
5 17/3 L Pandas II + Matplotlib Luboš
5 18/3 S Seminar 3: Data formats & APIs Vilém -
6 24/3 L Data formats, APIs Luboš HW 3
7 31/3 - MIDTERM Luboš & Vilém
7 1/4 S Seminar 4: Midterm solution Vilém
8 7/4 L Algorithmic problem solving Luboš
9 14/4 L Data science Luboš
10 21/4 L How to code (avoiding spaghetti code) Luboš
10 22/4 S Seminar 5: Data science case-study Vilém Project proposal approval
11 28/4 L Mixed topics: pkg, tests, docs, sql Luboš
12 5/5 L Guest Lecture: TBA, Beer with the guest and class-members after the lecture at TBA, 8:15pm Luboš & Vilém
13 12/5 -
14 19/5 -
15 26/5-28/5 - WiP: Project consultations Luboš & Vilém
16 2/6-6/6 - WiP: Project consultations Luboš & Vilém
17 9/6 -
18 16/6 - Deadline Final Project - submit Luboš & Vilém

Let me know if you need any other adjustments! ����

Last update: Hanus Luboš, Mgr., Ph.D. (02.02.2025)
Entry requirements -

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.

Last update: Hronec Martin, Mgr., Ph.D. (06.02.2020)
Registration requirements -

The course is primarily for master and advanced bachelor students.

Last update: Hronec Martin, Mgr., Ph.D. (04.10.2022)
 
Charles University | Information system of Charles University | http://www.cuni.cz/UKEN-329.html