Setup

Examples

All code in this course is in Python.

Packages

We use the following packages, listed here with the week in which they first appear.

Package	For
numpy	arrays and linear algebra
pandas	data frames
matplotlib	plots
scikit-learn	machine learning methods
scipy	optimization, hierarchical clustering
torch	neural networks
umap-learn	nonlinear dimensionality reduction

We recommend the uv package and project manager.

Installing uv

Follow the instructions at docs.astral.sh/uv/getting-started/installation, or run one of these:

# macOS / Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

# Windows (PowerShell)
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

Setting up the environment

Create a project folder for the course, create a virtual environment with Python 3.12, and install the packages into it.

mkdir bio322 && cd bio322
uv venv --python 3.12
uv pip install numpy pandas matplotlib scikit-learn scipy torch umap-learn jupyter

On Linux/macOS activate it with source .venv/bin/activate, on Windows with .venv\Scripts\activate. Once activated, python and jupyter point to this environment. (With VS Code, see below, you usually don’t need to activate it manually — VS Code finds .venv automatically.)

Editor

We recommend VS Code with the Python and Jupyter extensions installed (search for them in the Extensions panel, Ctrl+Shift+X/Cmd+Shift+X). VS Code then runs both notebooks (.ipynb) and plain scripts (.py), selects the .venv you just created as the interpreter/kernel, and gives you a debugger.

If you prefer a notebook in the browser instead, use Jupyter directly (it was installed above with uv pip install ... jupyter):

jupyter lab

Checking that it works

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import sklearn

print(np.__version__, pd.__version__, sklearn.__version__)