Data Processing in Python (JEM207)
The aim of the course is to provide a hands-on experience with the data-manipulation techniques in Python. The special emphasis is put on standard libraries such as Pandas, Numpy or Matplotlib and also collecting web data with requests and BeatifiulSoup. The students will also be guided through the modern social-coding and open-source technologies such as GitHub, Jupyter and Open Data.
The students will gain their experience using the data from the IES website and subject evaluation protocols.
The course would make use of the DataCamp online sources to provide the students with reliable and yet simple resources for learning Python programming.
After passing the course, the students will be able to download the data from APIs or directly from the web, pre-process it, analyze it and visualize it.
Econometrics II. (JEB110) is an explicit prerequisite for bachelor students.
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 ,
No knowledge of Python is required for entering the course.
Recommended DataCamp Courses
Web Data Formats
Passing the course is rewarded with 5 ECTS credits.
The requirement for passing the course are DataCamp assignments (6x5pts) and the final project (70pts).
DataCamp Assignments (30%)
Assignment 1 - Submission on 27/2 (Introduction to Python Course)
- Python Lists
- Python Basics
- Function and Packages
Assignment 2 - Submission on 6/3 (Manipulating DataFrames with pandas)
- Extracting and Transforming Data
- Advanced Indexing
Assignment 3 - Submission on 13/3 (Object-Oriented Programming in Python)
- Getting ready for object-oriented programming
- Deep dive into classes and objects
- Fancy classes, fancy objects
Assignment 4 - Submission on 20/3 (Web Scraping in Python Course)
- Introduction to HTML
- XPaths and Selectors
- CSS Locators, Chaining, and Responses
Assignment 5 - Submission on 5/4
- The Intro to SQL for Data Science (full course)
Assignment 6 - Submission on 10/4
- Concatenating data (Merging DataFrames with pandas)
- Merging data (Merging DataFrames with pandas)
- Rearranging and reshaping data (Manipulating DataFrames with pandas)
- Grouping data (Manipulating DataFrames with pandas)
Final project (70%)
- Students in teams by 2
- The task is to download any data from API or directly from the web. These data should be processed and visualized in the Jupyter Notebook. The project should be submitted as a GitHub repository
- The selection of the data is entirely up to the students.
March 27th: Project Topic First Submission
April 10th: Project Topic Final Submission
May 31st: Project Submission (to be confirmed)
- The project use correctly downloaded data from the public API or website.
- The data were cleaned appropriately
- The data are visualized
- The project is submitted as a public GitHub repository
- All team members collaborated on the GitHub repository (note that the history)
- The code is readable, commented and appropriately structured
- One ready-to-run method for downloading the data.
- Submitted as a jupyter notebook.
- 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)
Jupyter and GitHub intro here
The Jupyter notebook with IES web parser
|20-21/2||Intro + GitHub, Jupyter, DataCamp||Martin|
|27-28/2||Strings, Floats, Lists, Dictionaries, Functions||Vítek||HW 1|
|6-7/3||Pandas, Matplotlib, Numpy||Martin||HW 2|
|13-14/3||Object-Oriented Programming||Martin||HW 3|
|20-21/3||HTML, XML, JSON, requests, APIs, BeautifulSoup||Vítek||HW 4|
|27-28/3||IES Web Scraper||Vítek||Project Topic Proposal|
|3-4/4||Introduction to SQL||Vítek||HW 5|
|10-11/4||Advanced Pandas||Martin||Project Topic Approval||HW 6|
|17-18/4||Project Work 1||Vítek|
|1-2/5||Project Work 2||Vítek|
|8-9/5||Introduction to GeoData (Guest Lecture)||Guest|