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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 Pavlova.

Communication

Please direct all questions at Alena Pavlova only.

Final project

WiP consultations

Please reserve your date and time of the consultation here.

Project - paring

  • If you are looking for a partner use this google sheet with your CUNI account logged in. If you have a partner, delete your info, please, to make it easier for others.

  • See full instructions below schedule

Schedule

Week Date L/S Topic Lecturer Deadline
1 19.2. S Seminar 0: Setup (Jupyter, VScode, Git, OS basics) Martin + Alena
1 20.2. L Python basics Martin
2 27.2. L Python basics II Jan
3 4.3. S Seminar 1: Basics Alena HW 1
3 5.3. L Numpy Jan
4 12.3. L Pandas I Martin
5 18.3. S Seminar 2: Numpy & pandas Alena HW 2
5 19.3. L Pandas II + Matplotlib Martin
6 26.3. L Data formats, APIs Jan
7 2.4. S Seminar 3: Data formats & APIs Alena HW 3
7 8.4. - MIDTERM Alena, Jan & Martin
8 9.4 L Algorithmic problem solving Jan
9 15.4. S MIDTERM solution Alena
9 16.4. L Data science Martin
10 23.4. L How to code (avoiding spaghetti code) Martin Project proposal
11 29.4. S Seminar 5: Data science case-study Alena
11 30.4. L Databases Jan Topic approved
12 7.5. L CarmineOptions + Beer after lecture @ https://pivo-klub.cz/ Marek Hauzr
13 12.-16.5. - WiP: Project consultations Alena, Jan & Martin
14 20.-23.5. - 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
  • [Submit you proposal here](past deadline)
  • Deadline for topic approval: 23rd of April 2024
  • Deadline: 6th of September 2024

Projects' Evaluation criteria

  • Use of git by both - 5pts
    • meaningful commit messages
  • pythonic code principles - 5 pts
    • Provide requirements.txt file of the dependencies with versions (can use pip freeze) so that we can install with pip install -r requirements.txt
    • code is more often read than written, EAFP
  • Runnable code - 15 pts
    • by far the most important one! The 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 points of your project, give it some narrative

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 the week before

Homework Assignments (5%)

  • Create leetcode.com account

  • You are expected to submit in a specified Google form: https://forms.gle/jkoRpZ7yZoQYSYjY7

  • 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.

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The course site for the Data Processing in Python from IES

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