Children dressed in lab coats are studying a python

Python & VS Code Setup (From Zero to a Professional Environment)

Lesson Objectives

By the end of this lesson, you will be able to:

  • Install Python correctly on your system
  • Configure Visual Studio Code as a professional Python IDE
  • Run Python scripts from the terminal
  • Understand how this setup scales to data analysis and machine learning

This lesson is foundational.

A poor environment setup will compromise everything that follows.

1️⃣ What Is Python (and Why We Use It)

Python is a high-level, general-purpose programming language designed to prioritize:

  • readability
  • expressiveness
  • rapid development

Python is widely adopted in:

  • scientific computing
  • statistical analysis
  • machine learning
  • artificial intelligence
  • automation and scripting

In this course, Python is not an end in itself.

It is the computational backbone for advanced analytical workflows.

2️⃣ Installing Python (The Correct Way)

Step 1 — Download Python

Always download Python from the official source:

https://www.python.org

Install Python 3.10 or newer.

Avoid unofficial installers or OS-level package managers unless you know exactly what you are doing.

Step 2 — Installation Options (Critical)

During installation:

✅ Check the option

Add Python to PATH

Do not skip this step

Why this matters:

  • The PATH variable allows your system to locate Python
  • Without it, Python commands will fail silently or unpredictably

Step 3 — Verify Installation

Open a terminal (Command Prompt / PowerShell / Terminal):

python --version

Expected output:

Python 3.12.x

If this command fails, Python is not properly installed.

3️⃣ Installing Visual Studio Code

Why Visual Studio Code?

Visual Studio Code (VS Code) is a lightweight but professional-grade code editor.

We use VS Code because it:

  • scales from beginner to expert
  • integrates seamlessly with Python
  • supports debugging, linting, and testing
  • is widely used in scientific and industrial contexts

Step 1 — Download VS Code

Official website:

https://code.visualstudio.com

Install using default settings.

Step 2 — Install Python Extensions

In VS Code → Extensions panel, install:

  • Python (Microsoft)
  • Pylance

These extensions provide:

  • syntax highlighting
  • intelligent autocomplete
  • inline error detection
  • type inference

This is essential when working with complex analytical code later.

4️⃣ Creating Your First Python Project

Step 1 — Create a Project Folder

Create a folder, for example:

python_course

Open it in VS Code:

File → Open Folder

Step 2 — Create a Python File

Inside the folder, create:

lesson01.py

5️⃣Writing Your First Python Code

Insert the following code:

print("Hello, Python!")

Code Explanation (Line by Line)

  • print() is a built-in Python function
  • "Hello, Python!" is a string literal
  • The function outputs the string to the console

Python is an interpreted language:

the code is executed line by line, without compilation.

6️⃣ Running the Script

Option 1 — Terminal Execution (Recommended)

In the VS Code terminal:

python lesson01.py

Expected output:

Hello, Python!

Option 2 — VS Code Run Button

You may also use the ▶ Run button.

Both approaches are valid, but terminal execution builds better mental models for later work.

7️⃣ Common Beginner Errors (and Why They Matter)

Missing quotation marks

print(Hello)

Error:

NameError: name 'Hello' is not defined

Explanation:

  • Python interprets unquoted text as variable names

Using typographic quotes

print(“Hello”)

Explanation:

  • Python only accepts straight quotes (" or ')

Forgetting to save the file

Always save before running.

8️⃣ How This Setup Scales to Advanced Python

This exact environment will later support:

  • numerical libraries (numpy, scipy)
  • data analysis (pandas)
  • visualization (matplotlib, seaborn)
  • machine learning (scikit-learn)
  • reproducible research pipelines

You will not need to change tools, only add complexity.

FAQ — Frequently Asked Questions

Q: Why not start with Jupyter notebooks?

A: Scripts teach execution flow and structure. Notebooks come later.

Q: Should I install Anaconda?

A: Not yet. Understanding the standard Python ecosystem first is critical.

Q: Is Python suitable for serious scientific work?

A: Yes. Most modern scientific pipelines rely on Python.

Q: Will this work on Windows, macOS, and Linux?

A: Yes. Python is cross-platform.

Exercises

Exercise 1

Print your name.

Exercise 2

Print your age.

Exercise 3

Print two lines of text.

Exercise 4

Print a sentence containing quotes.

Exercise 5

Print a number.

Exercise 6

Print the result of 10 + 5.

Exercise 7

Print a string and a number on the same line.

Exercise 8

Use \\n to print text on two lines.

Exercise 9

Modify the file and run it again.

Exercise 10

Create a new file test.py and run it.

Solutions

Exercise 1

print("Michele")

Exercise 2

print(42)

Exercise 3

print("Line one")
print("Line two")

Exercise 4

print('He said "Python is powerful"')

Exercise 5

print(100)

Exercise 6

print(10 + 5)

Exercise 7

print("Age:", 42)

Exercise 8

print("First line\\nSecond line")

Exercise 9

# Save and re-run the file

Exercise 10

# test.py
print("This is a new file")

Next Lesson Preview

In Lesson 03, we will cover:

  • variables
  • naming conventions
  • Python syntax fundamentals
  • how Python stores and manipulates data

This is where real programming begins.