Setup: Anaconda, Jupyter & VS Code

Why a Proper Setup Matters

A plain Python install is enough to write scripts, but data science work leans on a handful of specialized libraries — NumPy, Pandas, Matplotlib — plus a way to run code interactively, inspecting a chart or a table right after you produce it instead of only seeing text scroll past in a terminal.

That's the gap Anaconda and Jupyter notebooks fill. This lesson gets both installed and working, then shows VS Code as a lighter alternative once you're comfortable.

Installing Anaconda

Anaconda is a free Python distribution built specifically for data science. Instead of installing Python and then separately installing NumPy, Pandas, Matplotlib, Jupyter, and dozens of their dependencies one by one, Anaconda installs all of it — and the conda package manager — in a single setup.

Installation Steps (Windows / macOS / Linux)

What Is a Jupyter Notebook?

A Jupyter notebook is a document — saved with a .ipynb extension — made up of a sequence of cells. Each cell holds either Python code or formatted text (Markdown), and you run cells one at a time, in any order you choose, rather than executing an entire file top to bottom.

Why data scientists live in notebooks

Creating and Running Your First Notebook

# Cell 1 — this runs immediately when you press Shift + Enter
print("Hello from my first Jupyter notebook!")

x = 7
y = 5
x + y  # in a notebook, the last expression's value is displayed automatically

Running that cell prints the greeting, and also displays 12 underneath — even without a print() call. This is one of Jupyter's most useful habits: the value of the last line in a cell is shown automatically, which makes quick inspection fast.

# Cell 2 — x and y are still available; the notebook shares one session
total = x + y
print(f"x + y = {total}")

A quick look at inline plotting

import matplotlib.pyplot as plt

plt.plot([1, 2, 3, 4], [10, 20, 15, 30])
plt.title("A tiny preview chart")
plt.show()  # the chart renders directly below this cell — no separate window

You'll learn Matplotlib properly in Section 3 — for now, just notice that the chart appears right in the notebook, beneath the cell that created it. That instant feedback loop is the whole point of working this way.

Alternative: VS Code with the Jupyter Extension

Once you're comfortable with notebooks, many data scientists move to editing .ipynb files inside VS Code instead of the browser-based Jupyter interface — you get the same cell-based experience, plus VS Code's autocomplete, debugging, and Git integration.

pip install Basics

pip is Python's standard package installer. Even with Anaconda, you'll eventually need a library that isn't bundled by default — pip install is how you get it.

# Install a single package
pip install requests

# Install a specific version
pip install pandas==2.2.0

# Install everything listed in a requirements file
pip install -r requirements.txt

# See what's installed and which versions
pip list

# Check details about one package
pip show numpy

Creating a Virtual Environment

A virtual environment is an isolated, self-contained Python installation for a single project. Without one, every package you install lands in one shared, global Python — and two projects that need different versions of the same library will conflict.

Using conda (recommended alongside Anaconda)

# Create a new environment named "datasci" with Python 3.11
conda create -n datasci python=3.11

# Activate it
conda activate datasci

# Install packages inside the active environment
conda install numpy pandas matplotlib jupyter

# Leave the environment
conda deactivate

Using Python's built-in venv module

# Create a virtual environment in a folder called "venv"
python -m venv venv

# Activate it — Windows
venv\Scripts\activate

# Activate it — macOS / Linux
source venv/bin/activate

# Once active, install as usual
pip install numpy pandas

# Leave the environment
deactivate

Lesson Summary

Let's recap everything you learned in this lesson: