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🚀 Data Science Roadmap 2026

📘 Phase 1: Programming Fundamentals

🐍 Topic 6: Python Functions
 
So far, you've learned variables, operators, input/output, conditional statements, and loops. As your programs grow larger, writing the same code repeatedly becomes inefficient. That's where functions come in. 

A function is a reusable block of code that performs a specific task. Functions make your code cleaner, easier to maintain, and reusable. 

Functions are heavily used in Data Science, Machine Learning, and AI because they allow you to organize complex workflows into smaller, manageable pieces.

🔹 1. What is a Function? 
A function is a named block of code that executes only when it is called. 

Instead of writing the same logic multiple times, you write it once inside a function and reuse it whenever needed. 

Example

def greet():
    print("Welcome to Data Science!")

greet()

Output 

Welcome to Data Science!

🔹 2. Why Do We Use Functions? 

Functions help you: 
✅ Avoid writing duplicate code 
✅ Improve code readability 
✅ Make debugging easier 
✅ Reuse code in multiple places 
✅ Build modular applications 

🔹 3. Defining a Function 

Syntax

def function_name():
    # Function body

Example:

def welcome():
    print("Hello, World!")

welcome()

🔹 4. Function Parameters 
Parameters allow you to pass information into a function.

def greet(name):
    print("Hello", name)

greet("Deepak")

Output 

Hello Deepak 

Here, "name" is called a parameter.

🔹 5. Function Arguments 
When calling a function, the values you pass are called arguments.

def square(number):
    print(number * number)

square(5)

Output 

25 

Here: 
• "number" → Parameter
• "5" → Argument

🔹 6. Returning Values 
A function can return a value using the return keyword.

def add(a, b):
    return a + b

result = add(10, 20)
print(result)

Output 

30 

Using return allows the function's result to be stored or used later.

🔹 7. Default Parameters 
You can assign default values to parameters.

def greet(name="Guest"):
    print("Hello", name)

greet()
greet("Rahul")

Output

Hello Guest
Hello Rahul

🔹 8. Multiple Return Values 
A function can return more than one value.

def calculate(a, b):
    return a + b, a * b

sum_value, product = calculate(4, 5)
print(sum_value)
print(product)

Output

9
20

🔹 9. Scope of Variables 
Variables created inside a function are called local variables.

def demo():
    message = "Inside Function"
    print(message)

demo()

Trying to access

message

outside the function will produce an error because it exists only inside the function.

🔹 10. Built-in Functions 
Python provides many ready-to-use functions. 

Examples:

numbers = [5, 2, 8, 1]
print(len(numbers))
print(max(numbers))
print(min(numbers))
print(sum(numbers))

Output

4
8
1
16

🔹 11. Real-World Data Science Example 
Calculate the average marks of students.

def average(marks):
    return sum(marks) / len(marks)

scores = [80, 75, 92, 88]
print(average(scores))

Output 

83.75 

Functions like this are commonly used while cleaning data, calculating statistics, and building machine learning pipelines.

🔹 12. Common Mistakes 
❌ Forgetting to Call the Function

def greet():
    print("Hello")

# Nothing happens because the function isn't called.

Correct:

greet()

❌ Forgetting to Return a Value

def add(a, b):
    a + b

# Correct:
def add(a, b):
    return a + b

🎯 Practice Questions 
1. Write a function to add two numbers.
2. Create a function to calculate the square of a number.
3. Write a function that checks whether a number is even or odd.
4. Create a function to calculate the average of a list.
5. Write a function that returns the largest of three numbers.

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🚀 Data Science Roadmap 2026

📘 Phase 1: Programming Fundamentals

🐍 Topic 7: Python Data Structures (Lists, Tuples, Sets & Dictionaries)

Welcome back! 👋

So far, you've learned variables, operators, input/output, conditional statements, loops, and functions. Now it's time to learn one of the most important topics in Python— Data Structures.

Data structures help us store, organize, and manage data efficiently. In Data Science, almost every dataset you work with will be stored or manipulated using these structures.

Python provides four built-in data structures:

• List

• Tuple

• Set

• Dictionary

Let's understand each one in detail.

🔹 1. List

A List is an ordered, mutable collection that allows duplicate values.

Creating a List

fruits = ["Apple", "Banana", "Mango"]
print(fruits)

Output

['Apple', 'Banana', 'Mango']

Accessing Elements

print(fruits[0])
print(fruits[2])

Output

Apple
Mango

Modifying a List

fruits[1] = "Orange"
print(fruits)

Output

['Apple', 'Orange', 'Mango']

Adding Elements

fruits.append("Grapes")
print(fruits)

Removing Elements

fruits.remove("Orange")
print(fruits)

🔹 2. Tuple

A Tuple is an ordered collection that cannot be modified after creation (immutable).

Creating a Tuple

colors = ("Red", "Green", "Blue")
print(colors)

Accessing Elements

print(colors[1])

Output

Green

Why Use Tuples?

Use tuples when your data should never change.

Examples:

• Months of the year

• Days of the week

• Fixed coordinates

🔹 3. Set

A Set is an unordered collection of unique elements.

Duplicate values are automatically removed.

Creating a Set

numbers = {1, 2, 2, 3, 4, 4, 5}
print(numbers)

Output

{1, 2, 3, 4, 5}

Adding Elements

numbers.add(6)

Removing Elements

numbers.remove(3)

Common Uses

• Remove duplicates

• Membership testing

• Mathematical set operations

🔹 4. Dictionary ⭐

A Dictionary stores data as key-value pairs.

It is one of the most frequently used data structures in Data Science.

Creating a Dictionary

student = {
    "name": "Deepak",
    "age": 24,
    "course": "Data Science"
}
print(student)

Accessing Values

print(student["name"])

Output

Deepak

Adding a New Key

student["city"] = "Mumbai"

Updating a Value

student["age"] = 25

Removing a Key

del student["course"]

🔹 5. Comparison of Data Structures

Feature | List | Tuple | Set | Dictionary

Ordered | ✅ | ✅ | ❌ | ✅

Mutable | ✅ | ❌ | ✅ | ✅

Duplicates Allowed | ✅ | ✅ | ❌ | Keys ❌

Indexed | ✅ | ✅ | ❌ | By Key

🔹 6. Common List Methods

numbers = [10, 20, 30]
numbers.append(40)
numbers.insert(1, 15)
numbers.remove(20)
numbers.sort()
print(numbers)

🔹 7. Common Dictionary Methods

student = {
    "name": "Rahul",
    "age": 23
}
print(student.keys())
print(student.values())
print(student.items())

🔹 8. Real-World Data Science Example

Suppose you have student information.

students = [
    {"name": "Amit", "marks": 90},
    {"name": "Sara", "marks": 85}
]

for student in students:
    print(student["name"], student["marks"])

Output

Amit 90
Sara 85

This is very similar to how records are stored before converting them into a Pandas DataFrame.

🔹 9. Common Mistakes

❌ Trying to Modify a Tuple

colors = ("Red", "Green")
colors[0] = "Blue"

This raises a TypeError because tuples are immutable.

❌ Accessing a Missing Dictionary Key

student = {"name": "John"}
print(student["age"])

This raises a KeyError.

A safer approach:

print(student.get("age"))

🎯 Practice Questions

1. Create a list of five cities and print the third city.

2. Create a tuple containing the days of the week.

3. Remove duplicate numbers from a list using a set.

4. Create a dictionary containing your name, age, and profession.

5. Print all keys and values of a dictionary using a loop.

🎯 Key Takeaways

✅ Lists are ordered and mutable.

✅ Tuples are ordered and immutable.

✅ Sets store only unique values.

✅ Dictionaries store data as key-value pairs.

✅ Dictionaries and Lists are the most commonly used data structures in Data Science.

Mastering these four data structures will make it much easier to work with datasets, APIs, JSON files, and machine learning projects.

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