Skip to content

The course site for the Data Processing in Python from IES

Notifications You must be signed in to change notification settings

hlsse/PythonDataIES

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 
 
 
 
 
 
 
 
 

Repository files navigation

Data Processing in Python (JEM207)

The course site for the Data Processing in Python from IES. See information on SIS. The course is taught by Martin Hronec, Jan Šíla and Alena Pavlovova.

Communication

Please direct all questions at Alena Pavlova only.

FAQ - pre semester

  • If you are on waiting list there is nothing we can do to enroll you. We managed to master somehow python, but SIS is something else. We follow the rules. Students usully drop from the course during the first week of the semester so there is a good chance you will be able to register.

  • The course is held in-person and there is by default no online option.

  • If you are junior to last BSc year/ MSc level, please consider your coding skills. If you just started coding (R or anything else), please consider signing up later on. We will still be here (hopefully) next semester as well.

  • If you decide to drop out after the 2-week grace period, note that if you submit homework, you will be awarded "F" mark following the university guidelines. Please, do consider this as well with regards to staying in the course. There might be others waiting for the spot.

Schedule

Week Date L/S Topic Lecturer Deadline
1 2.10. S Seminar 0: Setup (Jupyter, VScode, Git, OS basics) Martin
1 3.1 L Python basics Martin
2 10.10. L Python basics II Jan
3 16.10. S Seminar 1: Basics Alena HW 1
3 17.10. L Numpy Jan
4 24.10. L Pandas I Martin
5 30.10. S Seminar 2: Numpy & pandas Alena HW 2
5 31.10. L Pandas II + Matplotlib Martin
6 7.11. L Data formats, APIs Jan
7 13.11. S Seminar 3: Data formats & APIs Alena HW 3
7 14.11. L Algorithmic problem solving Jan
8 21.11. - MIDTERM Alena, Jan & Martin
9 27.11. S MIDTERM solution Alena
9 28.11. L Data science Martin Project proposal
10 5.12. L How to code (avoiding spaghetti code) Martin Topic approved
11 11.12. S Seminar 5: Data science case-study Alena
11 12.12. L Databases Jan
12 19.12. L Guest lecture (TBA) + Python Beer Alena, Jan & Martin
2.1. - - WiP: Project consultations Alena, Jan & Martin
9.1. - - WiP: Project consultations Alena, Jan & Martin

Course requirements

The requirements for passing the course are homeworks (5pts), the midterm (25pts), work in-progress-presentation (10pts), and the final project - including the final delivery presentation (60pts). At least 50% from the homeworks assignments and work-in-progress presentation is required for passing the course.

Final project (60%)

  • Students in teams by 2
  • Deadline for topic approval: 5th of December 2023
  • Deadline: 9th of February 2024

Projects' Evaluation critera

  • Use of git by both - 5pts
    • meaningful commit messages
  • pythonic code principles - 5 pts
    • code is more often read than written, EAFP
  • runability - 15 pts
    • by far the most important one! Project needs to run from scratch after installing versioned requirements.
      • provide requirements.txt file with specific versions of packages (use pip freeze to get it), and specify your precise Python version.
  • code structure - 15 pts
    • functions (classes), properly named variables
  • README, documentation - 5 pts
  • analysis, visualization - 15 pts
    • highlight key poins of your projet

Project work - presentation (10%)

  • Presentation of work-in-progress related to the final project.
  • Prepare questions, understand the goals of your project

Midterm exam (25%)

Live coding (80 minutes), "open browser", no collaboration between the students. More details during the lecture week before

Homework Assignments (5%)

  • Create leetcode.com account

  • You are expected to submit in a specified Google form

  • Rules:

    • Do not just copy the public solutions or what ChatGPT tells you. We will make an effort to find out and you will be penalized as per academic integrity guidelines. Do not try to get easy points by cheating, it is not the purpose of the HW tasks.
    • Have fun and try to beat the world!
    • Your submission will ideally be accepted by leetcode, but send us your best attempt regardless, you can still get the points. If anything, try to optimize run time, do not worry about memory.
    • You will struggle, but if you solve many of those, your next stop is Google cafeteria as an employee!
    • If you cannot decide, there is a shuffle button which will pick something for you.
  • HW 1 (1 pts):

    • Choose one of the easy problems. Have fun and send us how far you have got!
  • HW 2 (2 pts):

    • One easy or one Medium problem
  • HW 3 (2 pts):

Prerequisities

The course is designed for students who 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 to enter the course.

Credits

Passing the course is rewarded with 5 ECTS credits.

About

The course site for the Data Processing in Python from IES

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Jupyter Notebook 100.0%