Data Processing in Python - JEM207

• Anglický název: Data Processing in Python
• Český název: Data Processing in Python
• Zajišťuje: Institut ekonomických studií (23-IES)
• Fakulta: Fakulta sociálních věd
• Platnost: od 2024
• Semestr: oba
• E-Kredity: 5
• Způsob provedení zkoušky: písemná
• Rozsah, examinace: 2/2, Zk [HT]
• Počet míst: zimní:30 / neomezen (60); letní:neurčen / neurčen (60)
• Minimální obsazenost: neomezen
• 4EU+: ne
• Virtuální mobilita / počet míst pro virtuální mobilitu: ne
• Stav předmětu: vyučován
• Jazyk výuky: angličtina
• Způsob výuky: prezenční
• Další informace: https://github.com/iesfsv/Data-Processing-in-Python
• Poznámka: předmět je možno zapsat mimo plán; povolen pro zápis po webu; při zápisu přednost, je-li ve stud. plánu; předmět lze zapsat v ZS i LS
• Garant: Mgr. Luboš Hanus, Ph.D.
• Vyučující: Mgr. Luboš Hanus, Ph.D.; Mgr. Vilém Krejcar
• Třída: Courses for incoming students

Anotace

čeština

There is no ONLINE version for SS 2024/2025.

Please switch to the english version.

Poslední úprava: Hanus Luboš, Mgr., Ph.D. (18.02.2025)

angličtina

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.

Poslední úprava: Hanus Luboš, Mgr., Ph.D. (18.02.2025)

Cíl předmětu

čeština

Please switch to the english version.

Poslední úprava: SCHNELLEROVA (24.10.2019)

angličtina

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.

Poslední úprava: Hronec Martin, Mgr., Ph.D. (06.02.2020)

Literatura

čeština

Please switch to the english version.

Poslední úprava: Krejcar Vilém, Mgr. (01.10.2024)

angličtina

Book

Wes McKinney: Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython, O'Reilly, 2012

https://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/1449319793

 

Recommended DataCamp Courses

General Python

Introduction to Python, Intermediate Python for Data Science

 

Pandas

pandas Foundations, Manipulating DataFrames with pandas, Merging DataFrames with pandas, Cleaning Data in Python

 

Web Data Formats

Importing Data in Python (Part 1), Importing Data in Python (Part 2), Web Scraping with Python

 

Data Visualizations

Introduction to Data Visualization, Interactive Data Visualization in Bokeh

 

SQL

Introduction to SQL for Data Science, Introduction to Databases in Python

 

Others

LearnPython, Learn Python on CodeAcademy, Pandas, Practical Introduction to Web Scraping in Python

 

Official Documentation

Python, Pandas, Numpy, requests, Matplotlib

 

 

Poslední úprava: Krejcar Vilém, Mgr. (01.10.2024)

Metody výuky

čeština

Please switch to the english version.

Poslední úprava: Hronec Martin, Mgr., Ph.D. (14.02.2020)

angličtina

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

Poslední úprava: Krejcar Vilém, Mgr. (01.10.2024)

Požadavky ke zkoušce

čeština

Please switch to the english version.

Poslední úprava: SCHNELLEROVA (24.10.2019)

angličtina

The final grade consists of four parts:

• Final project (60%)
• Midterm (25%)
• Final project work-in-progress presentation (10%)
• Homework assignments (5%) - leetcode.com
• HW1 (any easy problem) - 1%;
• HW2 (any easy or medium problem from the "Parentheses") - 2%;
• HW3 (any easy or medium problem from the "Pandas set of problems") - 2%;

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)

Poslední úprava: Krejcar Vilém, Mgr. (01.10.2024)

Sylabus

čeština

Please switch to the english version.

Poslední úprava: Krejcar Vilém, Mgr. (01.10.2024)

angličtina

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

WEEK	DATE	L/S	TOPIC	LECTURER	DEADLINE
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! ����

Poslední úprava: Hanus Luboš, Mgr., Ph.D. (02.02.2025)

Vstupní požadavky

čeština

Please switch to the english version.

Poslední úprava: SCHNELLEROVA (24.10.2019)

angličtina

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.

Poslední úprava: Hronec Martin, Mgr., Ph.D. (06.02.2020)

angličtina

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

čeština

Please switch to the english version.

Poslední úprava: SCHNELLEROVA (24.10.2019)

angličtina

The course is primarily for master and advanced bachelor students.

Poslední úprava: Hronec Martin, Mgr., Ph.D. (04.10.2022)