Data Processing in Python (JEM207)
Stable link for online attendance: https://meet.google.com/zda-kefi-bsq
Ad hoc lecture 3 - sorry for the issues - https://meet.google.com/ute-ptrh-ycw
|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|
|26/10||Object-Oriented Programming||Martin||HW 3|
|2/11||HTML, XML, JSON, requests, APIs, BeautifulSoup||Jan|
|8/11||IES Web Scraper||Vitek||HW 4|
|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|
|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|
The requirements for passing the course are DataCamp assignments (5pts), the midterm (25pts), work in-progress-presentation (10pts), and the final project - including the final delivery presentation (60pts). At least 50% from the DataCamp assignments and work-in-progress presentation is required for passing the course.
Final project (60%)
- Students in teams by 2
- Deadline: TBA
- 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, with auxiliary scripts consisting of functions and classes definitions as .py files. The project should be submitted as a GitHub repository.
- The selection of the data is up to the students. (Conditional on our approval.)
- Git collaboration as a proof of collaboration of both students.
- More details during the lecture.
Projects' Evaluation critera
- Submitted as a Jupyter notebook in a Git repository. All team members pushed to the repo.
- Code is runnable and replicable (after installation of necessary packages).Exception only due to good reasons (data availablity, etc)
- OOP and code structure
- Analysis and visualization
- Code Readibility + Documentation
See example project from the previous semesters here from last year.
Project work - presentation (10%)
- Presentation of work-in-progress related to the final project.
Midterm exam (25%)
22/11. Live coding (80 minutes), "open browser", no collaboration between the students. More details during the lecture week before
DataCamp Assignments (5%)
3 assignments out of assignments 1-6 submitted on time is required.
*Deadline extended to Oct 17th at 23:59
- Introduction to Python - Python Basics
- Introduction to Python - Python Lists
- Introduction to Python - Functions and packages
Recommended DataCamp Courses
Web Data Formats
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.
Passing the course is rewarded with 5 ECTS credits.