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 2018 to 2018
• Semester: summer
• E-Credits: 5
• Examination process: summer s.:written
• Hours per week, examination: summer s.:2/0, Ex [HT]
• Capacity: 50 / unknown (50)
• Min. number of students: unlimited
• 4EU+: no
• Virtual mobility / capacity: no
• State of the course: taught
• Language: English
• Teaching methods: full-time
• Teaching methods: full-time
• Additional information: https://github.com/vitekzkytek/PythonDataIES
• 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
• Guarantor: Mgr. Bc. Vít Macháček, Ph.D.; Mgr. Martin Hronec
• Teacher(s): Mgr. Martin Hronec; Mgr. Bc. Vít Macháček, Ph.D.
• Class: Courses for incoming students
• Pre-requisite : JEB110

Annotation

Last update: 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.

Aim of the course

Last update: 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.

Literature

Last update: Mgr. Bc. Vít Macháček, Ph.D. (07.01.2019)

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

BeautifulSoup 

Matplotlib

 

 

Teaching methods

Last update: Mgr. Martin Hronec (14.02.2020)

Please switch to the english version.

Requirements to the exam

Last update: Mgr. Jan Šíla, M.Sc. (05.12.2023)

The final grade consists of four parts:

• Homework assignments (5%)
• Midterm (25%)
• Presentation of work in progress on the final project (10%) - at least 50% required from this part
• Final project (60%) - at least 50% required from this part

more info on the course GitHub( https://github.com/vitekzkytek/PythonDataIES/blob/master/README.md )

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)

Syllabus

Last update: Mgr. Jan Šíla, M.Sc. (23.09.2021)

Date	Topic	who	Project	HW
5/10	Intro, Jupyter, Git (+ GitHub)	Martin
11/10	Seminar (Git)	Martin		HW 1
12/10	Strings, Floats, Lists, Dictionaries, Functions	Vitek		HW 0
19/10	Numpy, Pandas, Matplotlib	Jan		HW 2
25/10	Seminar	Jan
26/10	Object-Oriented Programming	Jan		HW 3
2/11	HTML, XML, JSON, requests, APIs, BeautifulSoup	Jan
8/11	IES Web Scraper	Vitek		HW 4
9/11	Seminar	Vitek
16/11	Advanced Pandas	Vitek		HW 5
22/11	Seminar - MIDTERM	full house
23/11	Introduction to Databases	Jan	Project Topic Proposal	HW 6
30/11	Packaging and Documentation	Martin
6/12	Testing (and decorators)	Martin
7/12	Seminar	Martin	Project Topic Approval
14/12	Guest lecture	TBD
20/12	Project Work 2 (Seminar)	full house	Work-in-progress
21/12	Project Work 2	full house	Work-in-progress
TBA	Project Deadline	full house

Entry requirements

Last update: 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.

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.

Registration requirements

Last update: Mgr. Martin Hronec (04.10.2022)

The course is primarily for master and advanced bachelor students.