COM6018 Data Science with Python

Getting Set Up

Jon Barker

Overview

• Tools
• Python Versions
• Python Release Cycle

Tools

There are a number of tools that we will use in this module. These are:

• Python - the programming language
• Jupyter Notebooks - a way of writing and running Python code
• Git - a version control system
• GitHub - a website for hosting Git repositories
• Visual Studio Code - a text editor (recommended)
• Conda - a package manager for Python
• bash - a command line shell

These tools are all free and open source and all widely used in industry. They will also all run on Windows, Mac and Linux.

Python Versions

There are two versions of Python in common use: Python 2 and Python 3. We will be using Python 3 in this module. Python 2 is no longer supported and should not be used for new projects.

Python Release Cycle

Python 3 is being continually developed and new subversions are released regularly.

We will be using Python 3.12. This is the latest version at the time of writing.

Getting the Materials

The materials for this module are hosted on GitHub.

Visit https://github.com/UOS-COM-6018/COM6018

You can clone the repository using git. Open a terminal and then type:

git clone https://github.com/UOS-COM-6018/COM6018.git

This will have made a directoy called COM6018 in your current directory. You can cd into this directory and then ls to see the contents.

cd COM6018
ls

Contents

The materials are organised as follows:

.
└── materials
    ├── labs       # This will contain the lab materials
    ├── lectures   # These lecturer notes
    ├── solutions  # Solutions to the lab classes
    └── tutorials  # Tutorials covering the lecture content

Lectures and tutorials will generally be released on Sunday. Labs wills be released later in the week. Solutions will be released after the lab class.

Setting up a Python Virtual Environment

We will use uv to manage the Python environment. This ensures that the correct version of Python and all required packages are installed.

The project includes a file called pyproject.toml, which uv reads to determine the dependencies and their versions.

The environment.yml file

[project]
name = "com6018"
version = "2025.0.0"
description = "Materials for the 2025/2026 COM6018 module"
readme = "README.md"
requires-python = ">=3.12.5"
dependencies = [
    "cartopy>=0.25.0",
    "ipykernel>=6.30.1",
    "jupyterlab>=4.4.7",
    "matplotlib>=3.10.6",
    "notebook>=7.4.5",
    "numpy>=2.1.1",
    "pandas>=2.3.2",
    "scikit-learn>=1.7.2",
    "seaborn>=0.13.2",
    "xlrd>=2.0.2",
]

Creating the Python Virtual Environment

To set up the environment, run:

cd COM6018
uv sync

If you haven’t used uv before, you’ll need to install it first. It works on Windows, Linux, and macOS.

Installation instructions:

https://docs.astral.sh/uv/getting-started/installation/

Installing Visual Studio Code

I highly recommend using Visual Studio Code as your text editor. It is free and open source and has excellent support for Python.

https://code.visualstudio.com/download

It also provides support for running the Jupyter Notebooks that we use in the lab classes.

Jupyter Notebooks

Jupyter Notebooks are a way of writing and running Python code. They are very popular in data science and machine learning.

They allow you to integrate notes, Python code and the output of the code in a single document.

They are ideal for exploratory data analysis and for sharing your work with others. They are also great for lab classes.

cd COM6018  # replace COM6018 with the path to your COM6018 directory
source .venv/bin/activate
cd materials/tutorials
jupyter notebook 010_Introducing_Python.ipynb

What Next?

Read the tutorials 010_Introducing_Python.ipynb and 015_Further_Python.ipynb to get started with Python.

Come to the Lab on Friday and complete the 010_python_intro.ipynb notebook.