Setup

Examples

All code in this course is in Python.

Packages

We use the following packages.

Package	For
numpy	arrays and linear algebra
pandas	data frames
matplotlib	plots
scikit-learn	machine learning methods
scipy	optimization and other numerical routines
torch	automatic differentiation and model fitting
umap-learn	visualizing high-dimensional data

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
source .venv/bin/activate # Linux/macOS
.venv\Scripts\activate    # Windows

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__)