🚀 Data Science Roadmap 2026

📘 Phase 1: Programming Fundamentals

🐍 Topic 1: Python Basics – Variables & Data Types

Welcome to the Complete Data Science Roadmap! 🎉

Over the coming lessons, we'll learn everything you need to become a job-ready Data Scientist—from Python and SQL to Machine Learning, Deep Learning, Generative AI, and MLOps.

Today, we're starting with the first and most important topic of the roadmap: Python Basics – Variables & Data Types.

Python is the most widely used programming language in Data Science because it is easy to learn, highly readable, and supported by powerful libraries such as NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, and PyTorch.

Before building machine learning models or analyzing data, you must understand how Python stores and manages data. Every Python program begins with variables and data types, making them the foundation of your Data Science journey.

🔹 1. What is Python?

Python is a high-level, interpreted programming language used for:

✅ Data Science

✅ Machine Learning

✅ Artificial Intelligence

✅ Data Analysis

✅ Automation

✅ Web Development

🔹 2. What is a Variable?

A variable is a named container used to store data in memory.

Think of a variable like a labeled box. You store information inside the box, and whenever you need that information later, you simply use the label (variable name).

For example:

name = "Aman"
age = 25
salary = 175000

Here:

• "name" stores a string.

• "age" stores an integer.

• "salary" stores a numeric value.

🔹 3. Rules for Naming Variables

✅ Valid Rules

• Must begin with a letter or underscore ("_")

• Can contain letters, numbers, and underscores

• Variable names are case-sensitive

Examples:

student_name = "Rahul"
marks = 90
age2 = 24

❌ Invalid Examples

2name = "Rahul"
student name = "Rahul"
class = 10

Why?

• Cannot start with a number

• Spaces are not allowed

• "class" is a reserved Python keyword

🔹 4. What are Data Types?

A data type tells Python what kind of value a variable stores.

Python automatically detects the data type when you assign a value.

Data Types in Python:

int: Whole numbers Example: 25

float : Decimal numbers Example: 99.99

str: Text

Example: "Python"

bool: True or False

complex: Complex numbers

Example: 3+4j

🔹 5. Integer (int)

Stores whole numbers.

age = 25
print(age)
print(type(age))

Output:

25
<class 'int'>

🔹 6. Float (float)

Stores decimal numbers.

price = 199.99
print(price)
print(type(price))

Output:

199.99
<class 'float'>

🔹 7. String (str)

Stores text.

name = "Suresh"
print(name)
print(type(name))

Output:

Deepak
<class 'str'>

Strings can be written using either single (' ') or double (" ") quotes.

🔹 8. Boolean (bool)

Boolean values are used for decision-making.

They can store only two values: True or False

is_student = True
print(type(is_student))

Output:

<class 'bool'>

🔹 9. Complex Numbers

Python also supports complex numbers.

number = 3 + 4j
print(type(number))

Output:

<class 'complex'>

Although rarely used in Data Science, they are useful in scientific and mathematical computations.

🔹 10. Checking the Data Type

Use the type() function.

salary = 50000
print(type(salary))

Output:

<class 'int'>

🔹 11. Type Conversion (Casting)

Sometimes you need to convert one data type into another.

String → Integer

age = "25"
print(int(age))

Integer → Float

marks = 95
print(float(marks))

Float → Integer

price = 199.99
print(int(price)) # Output: 199

Integer → String

number = 100
print(str(number))

🔹 12. Multiple Variable Assignment

Assign multiple variables in one line.

x, y, z = 10, 20, 30

Assign the same value to multiple variables.

a = b = c = 100

🔹 13. Dynamic Typing

Python is dynamically typed.

This means a variable can store different data types at different times.

x = 10
x = "Data Science"
print(x) 
# Output: Data Science

🔹 14. Best Practices

✅ Use meaningful variable names.

student_name = "Rahul"
monthly_salary = 50000

Instead of:

a = "Rahul"
b = 50000

Follow the snake_case naming convention.

Examples: customer_name, total_sales, average_salary

🔹 15. Real-World Example

name = "Rohit"
age = 25
salary = 65000.50
is_employee = True

print(name)
print(age)
print(salary)
print(is_employee)

Output:

Rohit
25
65000.5
True

🎯 Key Takeaways

✅ Variables are used to store data.

✅ Python automatically detects data types.

✅ The most common data types are: int, float, str, bool, complex

✅ Use type() to check a variable's data type.

✅ Use meaningful variable names and follow the snake_case naming convention.

Mastering variables and data types is the first step toward becoming a successful Data Scientist. Every machine learning model, data analysis project, and AI application starts with understanding how data is stored and managed in Python.

Double Tap ❤️ For More