Python for Data Science – NMFP451

Course info and materials.

Practicals: Ondřej Týbl, Filip Bočinec ([surname]@karlin.mff.cuni.cz)

Time: Monday 12:20

Room: K4

Plan

Date	Topic	Homework Assignment	Homework Deadline1	Lecturer
29.09.2025	introduction	click	15.10.2025	Filip
13.10.2025	numpy	click	20.10.2025	Filip
27.10.2025	pandas	[TODO]	03.11.2025	Ondřej
10.11.2025	sql	[TODO]	17.11.2025	Ondřej
24.11.2025	matplotlib	[TODO]	01.12.2025	Ondřej
08.12.2025	scikit-learn	[TODO]	15.12.2025	Filip
05.01.2026	Object-oriented programming	[TODO]	12.01.2026	Ondřej

Materials

• w3schools
• MIT

Requirements

Several homework assignments will be given. You are required to submit a solution for each assignment by the respective deadline. Your code will be evaluated based on readability, efficiency, correctness, and whether it runs successfully.

After each deadline, we check your GitHub repositories and evaluate your solutions as passed, revision or failed. You need all the solutions to be marked as passed by the end of January 2026 to obtain the course credits. If marked as revision, you can resubmit your improved solution to obtain passed (no submissions after the end of January 2026). Your results (under your UKČO) can be found here.

Use of Large Language Models

Large Language Models (LLMs) such as ChatGPT, DeepSeek, or other AI assistants can be powerful tools to support your learning and coding. We do not prohibit their use, but we expect you to use them wisely and responsibly. Please follow these guidelines:

• Understand Before Using: Always make sure you understand the answer or code snippet provided by an LLM. Do not copy-paste blindly; LLMs may produce errors or code that is not optimized.

• Use as a Learning Aid, Not a Shortcut: LLMs are helpful for explanations, examples, and clarifications. Try to solve exercises yourself first; then, if needed, consult an LLM for hints or guidance.

• Verify and Test Everything: Always verify the correctness of code or explanations given by an LLM. Run code locally and check outputs. Make sure it matches the theory and your understanding.

• Develop Critical Thinking: Use LLM outputs as a starting point, not a final answer. Compare multiple sources and reasoning to strengthen your understanding.

• Privacy and Safety: Avoid sharing sensitive personal data or credentials with LLMs.

Setting up everything

In our course, we will learn how to use a whole bunch of tools and technologies that form the foundation of data science.

Category	Solution	Description
Programming Language	Python	The language used to write and execute code.
Integrated Development Environment (IDE)	PyCharm	Editor for writing, debugging, and managing projects (+ many more).
Virtual Environment	venv	An isolated environment to manage project-specific package dependencies.
Script / Notebook	Jupyter Notebook	An interactive file format/environment for running and documenting code.
Version Control System (VCS)	Git	Tracks code changes and manages project history.
VCS Hosting Platform	GitHub	Cloud-based platform for hosting and collaborating on Git repositories.

Below, a detailed instructions on how to set everything up is provided.

Setup Instructions

1) Install PyCharm

• Download the installer here.
• Install PyCharm using default options.

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2) Install Git

• Windows
  Download Git for Windows (use the 64-bit Git for Windows Setup).
  Install with the recommended (default) settings.
  Verify installation by opening Command Prompt and typing:

  git --version

• macOS
  Install Xcode Command Line Tools (which include Git) by running:

  xcode-select --install

  Verify installation by typing:

  git --version

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3) Create a GitHub Account

• Go to GitHub and create an account.
• Fork the course repository by clicking Fork here: python-for-data-science.
  (We strongly recommend keeping the default repository name.)
• A fork enables you to commit your own changes in a separate copy of the repository.

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4) Create a Project in PyCharm

• (Windows) Select Clone Repository.
• (Mac) Select Get from VCS.
• Enter the URL of your forked repository:

  https://github.com/[your-username]/python-for-data-science
  

• Select Clone.

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5) Create a Python Environment

• (Mac) In the bottom-right corner of PyCharm, click on <No Interpreter>, select Add New Interpreter → Add Local Interpreter. Create a new virtual environment with Python 3.11 (if not present, PyCharm will download it). Open the Terminal in PyCharm (icon in the bottom-left corner). Make sure your command line starts with (.venv) → this confirms the virtual environment is active. Install all required packages:

  pip install -r requirements.txt

• (Windows) After project is created, Creating Virtual Environment window should pop up (if not, follow Mac instructions). As base interpreter, choose Python 3.11 and click OK
• A blue progress bar will appear at the bottom of PyCharm when processes such as package installation are running. Please wait until it completes.
• If you need additional packages later, install them via:

  pip install [package]

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6) Submit a Test Homework Solution

• Open assignment1.ipynb in PyCharm
• Click Run to verify everything works.
• Add a new cell that prints your GitHub username.
• Submit your solution with Git → Commit → Commit and Push.
  • Enter a commit message.
  • Click Commit and Push.
  • Now GitHub wants to authenticate you. Select authentitacion via Token, now a new window where you should enter your token will appear. To create this token, go to GitHub website select your account icon in the top-right –> Settings –> Developer Settings –> Personal access tokens –> Tokens (classic) –> Generate new token –> add some description, validity deadline, select repo and Generate token. The token will appear and paste it in your PyCharm.
• Verify your changes at:

  https://github.com/[your-username]/python-for-data-science
  

• Go to the repository settings and make sure that Issues are enabled. This will allow us to make feedback to your solutions directly.
• Tell us your name and GitHub username.
  All future homework solutions will be submitted the same way.

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7) 🎉 Congratulations!

You are ready to start working on the course!

FAQ

1. Problem: When creating virtual environment, Pycharm does not provide any python versions. Solution: Completely erase the project including the folder in PycharmProjects and create it from scratch. If does not help, install desired python version yourself.

Footnotes

1. All homework deadlines are due at 23:59 on the specified date. ↩