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Lab 01 — What is Data Science? & Environment Setup

Session: Week 1 · Session 1 · 90 min CLO: CLO-1 (foundation) Difficulty: Beginner

Learning objectives

By the end of this lab you can:

  1. Explain the data science lifecycle in your own words.
  2. Verify your Python, Jupyter, Git, and editor installation.
  3. Create and activate a virtual environment.
  4. Run your first Python + pandas + NumPy code.
  5. Start a course repository on GitHub and clone it locally.

Problem statement

A new hire at a data team needs a working, verifiable environment on day one. Your task: prove that your machine can run the exact software stack this course uses — nothing more, nothing less — and record the proof.

Dataset requirements

None — you generate a tiny dataset inline (a list of numbers).

Step-by-step tasks

  1. Check versions. Open a terminal and run the checks in the starter code below. Record every version you get.
  2. Create a virtual environment named .venv in your course folder and activate it. (If you already did this in the session, record the command.)
  3. Install the course stack with pip install -r requirements.txt (the file is at the repository root). Note how long it takes.
  4. Run the smoke test below inside the environment. It must print versions of pandas and NumPy and the mean of [2, 4, 6, 8].
  5. Create a GitHub repo named data-science-course, clone it locally, and commit a first hello.md file. Push it.
  6. Write a short paragraph in hello.md: name the 5 stages of the data science lifecycle and which course module covers each.

Starter code

# smoke_test.py — run inside your activated virtual environment
import sys
import numpy as np
import pandas as pd

print("Python:", sys.version.split()[0])
print("NumPy:", np.__version__)
print("pandas:", pd.__version__)

numbers = [2, 4, 6, 8]
print("mean of", numbers, "=", np.mean(numbers))

# terminal checks (record all output)
python --version
git --version
jupyter --version

Expected output

Python: 3.x.y
NumPy: 2.x.y
pandas: 2.x.y
mean of [2, 4, 6, 8] = 5.0

(Exact versions depend on your machine — the point is that all three print without errors.)

Questions

  1. List the five stages of the data science lifecycle.
  2. Which stage is this course's Module A about? Module B? Module C?
  3. Why use a virtual environment instead of installing packages globally?
  4. What does git push do, and why do teams use GitHub?
  5. True/False: Jupyter is a programming language.

Challenge task

Write a versions.md file that records your Python, NumPy, pandas, Git, and Jupyter versions as a table, plus the date. Commit and push it. This file is your machine's "baseline" — you will reuse it in Lab 24 (Reproducible Workflows).

Marking rubric

Criteria Max Notes
Versions recorded for all five tools 2 from versions.md table
Virtual environment created & used 2 .venv in repo, smoke test ran in it
Smoke test runs without errors 3 correct output mean = 5.0
GitHub repo cloned & first commit pushed 2 commit message meaningful
Lifecycle paragraph (5 stages + module map) 3 each stage named correctly
Challenge: versions.md committed 3 table + date
Total 15