DATA202 - Introduction to Machine Learning

Fall 2026 · Calvin University

Resources

Running Notebooks

All class materials and practices are .ipynb (Jupyter notebook) files, opened and run in VS Code — either connected to Calvin’s cloud workspace (Coder) or installed on your own machine. Download the file from the course website and run it in one of two ways.

Required VS Code extensions

Whichever option you use, two extensions make .ipynb files work in VS Code:

On a Coder workspace these are pre-installed. Installing VS Code locally, install both from the Extensions panel (Ctrl+Shift+X, search by name) before opening any .ipynb file.

Optional but useful: the GitHub Copilot extension (plus GitHub Copilot Chat) works directly inside notebook cells in VS Code — inline suggestions appear as you type in a code cell, and you can select a cell (or just place your cursor in it) and open Copilot Chat (Ctrl+Alt+I) to ask about or modify just that cell, similar to Colab’s cell-level AI assistant. Calvin students can get Copilot for free through the GitHub Student Developer Pack (see Version Control below) — remember the course’s LLM use policy still applies to anything it suggests.

Option A — Coder (Recommended)

Calvin provides a cloud-based development environment through Coder — nothing to install for the environment itself, only VS Code.

  1. Log in at coder.cs.calvin.edu with your Calvin credentials and start your workspace
  2. Open it one of two ways:
    • In the browser — click the workspace’s VS Code (browser) button; this opens a full VS Code interface, ready to use with no local install at all
    • In VS Code Desktop — install VS Code locally, install the Coder extension (coder.coder-remote) from the Extensions panel, then use it to connect to your workspace; VS Code Desktop then runs against the remote workspace exactly as if the files were local
  3. Download the .ipynb file from the course website
  4. Upload it into your workspace — in the browser VS Code, the Explorer sidebar (the file-tree panel on the left, usually already open; click the top icon in the left activity bar if not) is where files live. Either:
    • Drag the file from your computer straight onto the Explorer panel, or
    • Right-click empty space inside the Explorer panel and choose Upload…, then pick the file from your computer’s file picker

    If a notebook needs extra files too (a dataset or image, for a few notebooks that don’t load data straight from a URL), upload those the same way, into the same folder as the notebook.

  5. Click the uploaded .ipynb file in the Explorer to open it
  6. In the top-right of the notebook, use Select Kernel to pick the Python environment (only needed once per workspace), then run cells with Shift + Enter

Tip: files persist in your workspace between sessions — always save before closing.

Option B — Local Installation

If you prefer to run notebooks entirely on your own machine:

  1. Install Python — either Anaconda (includes Python plus most scientific libraries) or a plain install from python.org
  2. Install VS Code, then the Python and Jupyter extensions (see above)
  3. Install the course’s required packages — see Configuring Your Environment below
  4. Download the .ipynb file from the course website into a local folder
  5. In VS Code, use File → Open Folder on that folder (recommended, so relative paths to datasets/images resolve correctly), then open the .ipynb file from the Explorer sidebar
  6. Use Select Kernel in the top-right of the notebook to choose your Python interpreter, then run cells with Shift + Enter

Jupyter Notebooks — Key Concepts

References:

Configuring Your Environment

On Coder: every package this course uses — pandas, plotly, scikit-learn, and otter-grader — is already installed in your workspace. Nothing to do here; skip to the next section.

On a local install: open a terminal (VS Code’s built-in terminal, Ctrl+`) and run:

pip install pandas plotly scikit-learn otter-grader

pip isn’t always on your PATH, or (rarely, these days) might point at Python 2 instead of Python 3. pip and pip3 almost always do the exact same thing — try them in this order until one works:

pip install pandas plotly scikit-learn otter-grader
pip3 install pandas plotly scikit-learn otter-grader
python -m pip install pandas plotly scikit-learn otter-grader

If neither pip nor pip3 is recognized at all ('pip' is not recognized... / command not found), python -m pip install ... almost always works — it runs pip through whichever python you already use to run your code, rather than relying on pip being on your PATH separately. (If python itself isn’t recognized either, try python3 -m pip install ... instead.)

Verify it worked — run this in the same terminal:

python -c "import pandas, plotly, sklearn, otter; print('All good!')"

If that prints All good! with no errors, you’re set for every notebook and practice this semester.

Autograding with Otter

This course uses otter-grader for automatic feedback on in-class exercises and practices. (Already installed on Coder; see Configuring Your Environment above for local setup.)

When you open a notebook, the first cell initializes the grader:

import otter
grader = otter.Notebook("notebook_name.ipynb")

After completing each task, run the check cell below it:

grader.check("task_name")

A ✅ means your answer passes the tests. If it fails, read the message and try again. For class exercises, this is just for your own feedback — nothing is collected. For practices, the same tests are used when your notebook is graded.

Core Libraries

pandas — Data Manipulation & Analysis

plotly — Visualization

scikit-learn — Modeling & Machine Learning

Version Control

All code is managed with Git and hosted on GitHub.

Key commands:

git add .
git commit -m "description of changes"
git push