Lab 1: Python Environment and Notebook Setup DS

Date: July 6
Time: 1:00-5:00 PM
TA: Jianhao Huang / Shanmu Wang
Goal: Prepare a working Python environment for all data science labs.

Why This Lab Matters

Most data science labs in this course will be distributed as Jupyter notebook skeletons. Before we start modeling and visualization, everyone needs a reliable setup that can:

• open .ipynb notebooks;
• run Python code cells;
• import common data science packages;
• save outputs and figures;
• keep course files organized.

By the end of this lab, you should have one working setup on your laptop or lab computer and be able to run the provided notebook skeleton from start to finish.

Materials

Download the Lab 1 notebook folder from the password-protected Box materials folder:

Open notebook lab materials on Box

Ask Eric for the password.

Recommended Setup

Use one of these setups:

Option	Recommended For	Notes
VS Code + Jupyter extension	Most students	Good editor, file browser, terminal, and notebook support in one app.
JupyterLab in browser	Students who prefer browser notebooks	Simple and close to classic notebook workflows.
Google Colab	Backup only	Useful if local installation fails, but local files and packages are less convenient.

We recommend a local Python environment because later labs may use local datasets, saved figures, and custom helper files.

Part A: Organize Your Course Folder

1:00-1:10 PM - Create a Course Workspace

Create one folder for the course, for example:

COSMOS-Cluster10/
  labs/
  data/
  outputs/
  notebooks/

Suggested rule: one subfolder per lab.

COSMOS-Cluster10/
  labs/
    lab01-python-setup/
    lab02-data-visualization/
    lab04-regression-knn/

Do not put course work only in Downloads. It is easy to lose files there.

Part B: Install Python

1:10-1:35 PM - Choose One Python Distribution

Use one of the following.

Option 1: Miniforge or Miniconda

This is recommended if you already know conda or want a clean environment manager.

After installing, open a terminal and check:

python --version
conda --version

If python does not work on macOS, try:

python3 --version

Option 2: Python.org Installer

Install Python from the official Python installer. Then check:

python --version
python -m pip --version

On macOS, you may need:

python3 --version
python3 -m pip --version

Required Python Version

Use Python 3.10 or newer. Python 3.11 or 3.12 is recommended. If your Python version is older than 3.10, ask a TA before continuing.

Part C: Create the Course Environment

1:35-2:00 PM - Create an Isolated Environment

An environment keeps this course’s packages separate from other projects.

If Using Conda

conda create -n cosmos-ds python=3.11
conda activate cosmos-ds

Install packages:

python -m pip install numpy pandas matplotlib seaborn scipy scikit-learn jupyterlab notebook ipykernel

If Using venv

Create the environment inside your course folder.

macOS/Linux:

python3 -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install numpy pandas matplotlib seaborn scipy scikit-learn jupyterlab notebook ipykernel

Windows PowerShell:

python -m venv .venv
.\.venv\Scripts\Activate.ps1
python -m pip install --upgrade pip
python -m pip install numpy pandas matplotlib seaborn scipy scikit-learn jupyterlab notebook ipykernel

Checkpoint:

python -c "import numpy, pandas, matplotlib, sklearn; print('DS environment ready')"

Part D: Install and Configure an IDE

2:00-2:25 PM - VS Code Setup

If using VS Code:

1. Install VS Code.
2. Install these extensions:
   • Python
   • Jupyter
3. Open your COSMOS-Cluster10 folder in VS Code.
4. Open a terminal inside VS Code.
5. Activate your environment.
6. Select the Python interpreter:
   • press Command+Shift+P on macOS or Ctrl+Shift+P on Windows;
   • search for Python: Select Interpreter;
   • choose cosmos-ds or .venv.

2:25-2:35 PM - Register a Jupyter Kernel

Run:

python -m ipykernel install --user --name cosmos-ds --display-name "Python (COSMOS DS)"

When opening a notebook, select the kernel named:

Python (COSMOS DS)

Part E: Run the Lab 1 Notebook Skeleton

2:35-3:20 PM - Open the Provided Notebook

Download the Lab 1 folder from Box and place it inside:

COSMOS-Cluster10/labs/lab01-python-setup/

The folder should include the Lab 1 notebook skeleton. Expected file name:

lab01_python_setup.ipynb

Open it in VS Code or JupyterLab.

If using JupyterLab, start it from the activated environment:

jupyter lab

Then open the notebook from the file browser.

Notebook Check Cells

Your notebook should include or run equivalent cells:

import sys
print(sys.version)
print(sys.executable)

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

print("numpy:", np.__version__)
print("pandas:", pd.__version__)
print("scikit-learn:", sklearn.__version__)

x = np.linspace(0, 2 * np.pi, 200)
y = np.sin(x)

plt.plot(x, y)
plt.xlabel("x")
plt.ylabel("sin(x)")
plt.title("Environment test plot")
plt.show()

Checkpoint: show the TA your notebook with all cells executed and a visible plot.

Part F: Python Warm-Up

3:20-4:15 PM - NumPy and Dictionaries

Complete the warm-up cells in the skeleton notebook.

Topics:

• lists vs NumPy arrays;
• array indexing and slicing;
• basic array statistics: mean, standard deviation, min, max;
• dictionaries for storing structured records;
• converting a list of dictionaries into a pandas.DataFrame.

Example data structure:

records = [
    {"student": "A", "sensor": "imu", "samples": 1200, "label": "walking"},
    {"student": "B", "sensor": "uwb", "samples": 800, "label": "los"},
    {"student": "C", "sensor": "mmwave", "samples": 500, "label": "gesture"},
]

Required tasks:

1. Create a DataFrame from the records.
2. Add one new record.
3. Compute total samples by sensor type.
4. Make one bar chart.
5. Save the figure into the outputs/ folder.

Part G: Debugging Practice

4:15-4:40 PM - Common Errors

Work through at least three intentionally broken cells in the skeleton notebook.

Examples:

• missing import;
• wrong variable name;
• wrong file path;
• package not installed;
• kernel using the wrong Python environment.

For each error, write:

1. What error message did Python show?
2. What caused the error?
3. How did you fix it?

Part H: Reproducibility Check

4:40-4:55 PM - Restart and Run All

In the notebook menu:

1. Restart the kernel.
2. Run all cells from top to bottom.
3. Confirm there are no hidden dependencies on old variables.
4. Save the notebook.

This is important. A notebook is not complete until it can run from a clean kernel.

Optional Tutorial: Better Terminal Setup for Linux/macOS

This tutorial is optional and is not required for Lab 1 submission. It is useful if you want a nicer terminal for the rest of the course, especially when you are navigating folders, activating Python environments, and using Git.

Goal: install Oh My Zsh, add the Powerlevel10k prompt theme, and enable command syntax highlighting plus autosuggestions.

Step 0 - Check Zsh and Git

Open Terminal and run:

zsh --version
git --version

If zsh is missing on Linux, install it with your package manager. For example, on Ubuntu/Debian:

sudo apt update
sudo apt install zsh git curl

On macOS, Zsh is usually already installed.

Optional: make Zsh your default shell.

chsh -s "$(which zsh)"

Close and reopen Terminal after changing your default shell.

Step 1 - Install Oh My Zsh

Run the official installer:

sh -c "$(curl -fsSL https://raw.githubusercontent.com/ohmyzsh/ohmyzsh/master/tools/install.sh)"

If the installer asks whether to switch to Zsh, choose yes.

Step 2 - Install Powerlevel10k

Clone the theme into the Oh My Zsh custom themes folder:

git clone --depth=1 https://github.com/romkatv/powerlevel10k.git "${ZSH_CUSTOM:-$HOME/.oh-my-zsh/custom}/themes/powerlevel10k"

Open ~/.zshrc in a text editor and change the ZSH_THEME line to:

ZSH_THEME="powerlevel10k/powerlevel10k"

Restart Zsh:

exec zsh

If the Powerlevel10k configuration wizard does not start automatically, run:

p10k configure

For best icons and symbols, install the font recommended by the Powerlevel10k wizard, then select it in your terminal profile.

Step 3 - Add Highlighting and Autosuggestions

Install the two plugins:

git clone https://github.com/zsh-users/zsh-autosuggestions "${ZSH_CUSTOM:-$HOME/.oh-my-zsh/custom}/plugins/zsh-autosuggestions"
git clone https://github.com/zsh-users/zsh-syntax-highlighting.git "${ZSH_CUSTOM:-$HOME/.oh-my-zsh/custom}/plugins/zsh-syntax-highlighting"

Open ~/.zshrc, find the plugins=(...) line, and include these plugins. Keep zsh-syntax-highlighting last.

plugins=(git zsh-autosuggestions zsh-syntax-highlighting)

Restart Zsh:

exec zsh

Quick checks:

• type part of an old command and press the right arrow key to accept the autosuggestion;
• type a valid command such as python --version and notice the command highlighting;
• type a command that does not exist and notice that the highlighting changes.

Optional Tutorial: Git by Example

This tutorial is optional and is not required for Lab 1 submission. It is useful if you want more practice with Git, especially before working on group code, notebooks, or final project materials.

Use Git by example: Interactive guide as a hands-on reference. The guide lets you read short explanations, run example commands, and experiment safely in an interactive environment.

Submit

Submit:

1. Completed lab01_python_setup.ipynb.
2. One screenshot showing the selected kernel.
3. One screenshot or output cell showing package versions.
4. One generated plot saved in outputs/.
5. Short answers to the debugging questions.

Troubleshooting

ModuleNotFoundError

The package is missing from the current environment.

python -m pip install package_name

Then restart the notebook kernel.

Notebook Uses the Wrong Python

Run this in a notebook cell:

import sys
print(sys.executable)

If it does not point to cosmos-ds or .venv, select the correct kernel.

jupyter: command not found

Activate the environment and install Jupyter:

python -m pip install jupyterlab notebook

Then run:

jupyter lab

File Path Problems

Use paths relative to the notebook location when possible.

Recommended:

from pathlib import Path

project_dir = Path.cwd()
data_dir = project_dir / "data"
output_dir = project_dir / "outputs"
output_dir.mkdir(exist_ok=True)

Avoid hard-coded paths like:

/Users/myname/Downloads/random_folder/data.csv

What to Conclude

A strong Lab 1 setup means:

• you can open and run notebooks;
• the notebook uses the intended Python environment;
• common packages import successfully;
• plots display and save correctly;
• your files are organized for the rest of the course.

If your environment still does not work by the end of lab, ask a TA to help you switch to the backup option before the next data science lab.