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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.

Double Tap ❤️ For Part-7

🚀 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.

Double Tap ❤️ For Part-8

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🚀 Data Science Roadmap 2026

📘 Phase 1: Programming Fundamentals

🐍 Topic 8: Python List Comprehensions

Welcome back! 👋

In the previous lesson, you learned about Python's built-in data structures—Lists, Tuples, Sets, and Dictionaries.

Now it's time to learn one of Python's most elegant and frequently used features: List Comprehensions.

List comprehensions provide a concise and readable way to create, filter, and transform lists. They are widely used in Data Science, Machine Learning, data preprocessing, and coding interviews.

🔹 1. What is a List Comprehension?

A list comprehension is a compact way to create a new list by applying an expression to each item in an iterable (such as a list, tuple, or range).

Instead of writing multiple lines with a loop, you can accomplish the same task in a single line.

General Syntax

new_list = [expression for item in iterable]

🔹 2. Creating a List Using a Loop

numbers = []
for i in range(5):
    numbers.append(i)
print(numbers)

Output

[0, 1, 2, 3, 4]

🔹 3. Creating the Same List Using List Comprehension

numbers = [i for i in range(5)]
print(numbers)

Output

[0, 1, 2, 3, 4]  

Notice how the code is shorter and easier to read.

🔹 4. Performing Calculations

Create a list of squares.

squares = [x ** 2 for x in range(1, 6)]
print(squares)

Output

[1, 4, 9, 16, 25]

🔹 5. Using Conditions

You can filter elements while creating a list.

Example: Even Numbers

even_numbers = [x for x in range(1, 11) if x % 2 == 0]
print(even_numbers)

Output

[2, 4, 6, 8, 10]

🔹 6. Converting Strings

Convert all names to uppercase.

names = ["rahul", "deepak", "anita"]
upper_names = [name.upper() for name in names]
print(upper_names)

Output

['RAHUL', 'DEEPAK', 'ANITA']

🔹 7. Using Conditional Expressions

Replace negative numbers with zero.

numbers = [5, -2, 8, -1, 3]
updated = [0 if x < 0 else x for x in numbers]
print(updated)

Output

[5, 0, 8, 0, 3]

🔹 8. Nested List Comprehension

Create a multiplication table.

table = [[i * j for j in range(1, 6)] for i in range(1, 4)]
print(table)

Output

[[1, 2, 3, 4, 5],
 [2, 4, 6, 8, 10],
 [3, 6, 9, 12, 15]]

🔹 9. Real-World Data Science Example

Suppose you have a list of sales amounts.

sales = [1200, 850, 1500, 600, 2000]
high_sales = [sale for sale in sales if sale > 1000]
print(high_sales)

Output

[1200, 1500, 2000] 

This technique is commonly used while cleaning and filtering datasets before analysis.

🔹 10. Benefits of List Comprehensions

✅ Shorter code

✅ Easier to read

✅ Faster than traditional loops in many cases

✅ Widely used in Data Science and Machine Learning

🔹 11. Common Mistakes

❌ Forgetting the Expression

numbers = [for i in range(5)]  # SyntaxError

Correct:

numbers = [i for i in range(5)]

❌ Incorrect Order of "if"

numbers = [if x % 2 == 0 x for x in range(10)]  # SyntaxError

Correct:

numbers = [x for x in range(10) if x % 2 == 0]

🎯 Practice Questions

1. Create a list of numbers from 1 to 20.

2. Create a list containing the squares of numbers from 1 to 10.

3. Create a list containing only odd numbers from 1 to 20.

4. Convert a list of names to lowercase.

5. Replace all negative values in a list with zero using a list comprehension.

🎯 Key Takeaways

✅ List comprehensions provide a concise way to create lists.

✅ They combine loops and expressions into a single line.

✅ You can filter data using "if" conditions.

✅ Conditional expressions allow values to be modified during list creation.

✅ List comprehensions are widely used in data cleaning, feature engineering, and machine learning workflows.

Mastering list comprehensions will help you write cleaner, more Pythonic code and prepare you for technical interviews and real-world Data Science projects.

Double Tap ❤️ For Part-9

🚀 Data Science Roadmap 2026

📘 Phase 1: Programming Fundamentals

🐍 Topic 8: Python List Comprehensions

Welcome back! 👋

In the previous lesson, you learned about Python's built-in data structures—Lists, Tuples, Sets, and Dictionaries.

Now it's time to learn one of Python's most elegant and frequently used features: List Comprehensions.

List comprehensions provide a concise and readable way to create, filter, and transform lists. They are widely used in Data Science, Machine Learning, data preprocessing, and coding interviews.

🔹 1. What is a List Comprehension?

A list comprehension is a compact way to create a new list by applying an expression to each item in an iterable (such as a list, tuple, or range).

Instead of writing multiple lines with a loop, you can accomplish the same task in a single line.

General Syntax

new_list = [expression for item in iterable]

🔹 2. Creating a List Using a Loop

numbers = []
for i in range(5):
    numbers.append(i)
print(numbers)

Output

[0, 1, 2, 3, 4]

🔹 3. Creating the Same List Using List Comprehension

numbers = [i for i in range(5)]
print(numbers)

Output

[0, 1, 2, 3, 4]

Notice how the code is shorter and easier to read.

🔹 4. Performing Calculations

Create a list of squares.

squares = [x ** 2 for x in range(1, 6)]
print(squares)

Output

[1, 4, 9, 16, 25]

🔹 5. Using Conditions

You can filter elements while creating a list.

Example: Even Numbers

even_numbers = [x for x in range(1, 11) if x % 2 == 0]
print(even_numbers)

Output

[2, 4, 6, 8, 10]

🔹 6. Converting Strings

Convert all names to uppercase.

names = ["rahul", "deepak", "anita"]
upper_names = [name.upper() for name in names]
print(upper_names)

Output

['RAHUL', 'DEEPAK', 'ANITA']

🔹 7. Using Conditional Expressions

Replace negative numbers with zero.

numbers = [5, -2, 8, -1, 3]
updated = [0 if x < 0 else x for x in numbers]
print(updated)

Output

[5, 0, 8, 0, 3]

🔹 8. Nested List Comprehension

Create a multiplication table.

table = [[i * j for j in range(1, 6)] for i in range(1, 4)]
print(table)

Output

[[1, 2, 3, 4, 5],
 [2, 4, 6, 8, 10],
 [3, 6, 9, 12, 15]]

🔹 9. Real-World Data Science Example

Suppose you have a list of sales amounts.

sales = [1200, 850, 1500, 600, 2000]
high_sales = [sale for sale in sales if sale > 1000]
print(high_sales)

Output

[1200, 1500, 2000]

This technique is commonly used while cleaning and filtering datasets before analysis.

🔹 10. Benefits of List Comprehensions

✅ Shorter code

✅ Easier to read

✅ Faster than traditional loops in many cases

✅ Widely used in Data Science and Machine Learning

🔹 11. Common Mistakes

❌ Forgetting the Expression

numbers = [for i in range(5)] # SyntaxError

Correct:

numbers = [i for i in range(5)]

❌ Incorrect Order of "if"

numbers = [if x % 2 == 0 x for x in range(10)]

Correct:

numbers = [x for x in range(10) if x % 2 == 0]

🎯 Practice Questions

1. Create a list of numbers from 1 to 20.

2. Create a list containing the squares of numbers from 1 to 10.

3. Create a list containing only odd numbers from 1 to 20.

4. Convert a list of names to lowercase.

5. Replace all negative values in a list with zero using a list comprehension.

🎯 Key Takeaways

✅ List comprehensions provide a concise way to create lists.

✅ They combine loops and expressions into a single line.

✅ You can filter data using "if" conditions.

✅ Conditional expressions allow values to be modified during list creation.

✅ List comprehensions are widely used in data cleaning, feature engineering, and machine learning workflows.

Mastering list comprehensions will help you write cleaner, more Pythonic code and prepare you for technical interviews and real-world Data Science projects.

Double Tap ❤️ For Part-9

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🚀 Data Science Roadmap 2026

📘 Phase 1: Programming Fundamentals

🐍 Topic 9: Python Lambda Functions, map(), filter(), and reduce()

Welcome back! 👋

So far, you've learned Python basics, loops, functions, data structures, and list comprehensions. In this lesson, you'll learn functional programming concepts in Python using Lambda Functions, map(), filter(), and reduce().

These are widely used in Data Science for transforming, filtering, and processing large datasets efficiently.

🔹 1. What is a Lambda Function?

A Lambda Function is a small anonymous function that can have any number of arguments but only one expression.

Unlike normal functions, lambda functions don't require a name.

Syntax

lambda arguments: expression

Example

square = lambda x: x * x  
print(square(5))  

Output: 25

This is equivalent to:

def square(x):
    return x * x

🔹 2. Why Use Lambda Functions?

Lambda functions are useful when:

✅ You need a simple function only once.

✅ You want shorter, cleaner code.

✅ You're using functions like map(), filter(), or sorted().

🔹 3. Lambda with Multiple Arguments

add = lambda a, b: a + b  
print(add(10, 20)) 

Output: 30

🔹 4. The map() Function

The map() function applies a function to every item in an iterable.

Syntax: map(function, iterable)

Example

numbers = [1, 2, 3, 4, 5]  

squares = list(map(lambda x: x ** 2, numbers))  
print(squares)  

Output: [1, 4, 9, 16, 25]

🔹 5. Using map() with a Normal Function

def double(x):
    return x * 2

numbers = [1, 2, 3, 4]
result = list(map(double, numbers))
print(result)

Output: [2, 4, 6, 8]

🔹 6. The filter() Function

The filter() function selects only those elements that satisfy a condition.

Syntax: filter(function, iterable)

Example

numbers = [1, 2, 3, 4, 5, 6]  

even = list(filter(lambda x: x % 2 == 0, numbers))  

print(even)  

Output: [2, 4, 6]

🔹 7. The reduce() Function

The reduce() function applies a function repeatedly to reduce an iterable to a single value.

It is available in the functools module.

from functools import reduce
numbers = [1, 2, 3, 4]
result = reduce(lambda a, b: a + b, numbers)
print(result)

Output: 10

🔹 8. Difference Between map(), filter(), and reduce()

map(): Transforms every element in an iterable and returns a new iterable.

filter(): Keeps only elements that match a condition and returns a filtered iterable.

reduce(): Combines all elements into a single value.

🔹 9. Real-World Data Science Example

Suppose you have customer purchase amounts.

purchases = [1200, 450, 1800, 900, 2500]
high_value = list(filter(lambda x: x > 1000, purchases))
print(high_value)

Output: [1200, 1800, 2500]

Now calculate the total revenue.

from functools import reduce
total = reduce(lambda a, b: a + b, purchases)
print(total)

Output: 6850

🔹 10. Combining map() and filter()

numbers = [1, 2, 3, 4, 5, 6]
result = list(
    map(
        lambda x: x * 10,
        filter(lambda x: x % 2 == 0, numbers)
    )
)
print(result)