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.

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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)

Output:

[20, 40, 60]

 

First, filter() keeps only even numbers.

Then, map() multiplies each by 10.

🔹 11. Common Mistakes

❌ Forgetting to Convert map() to a List 

result = map(lambda x: x * 2, numbers)

→

<map object at ...>

 

Correct:

print(list(result))

❌ Forgetting to Import reduce()

result = reduce(lambda a, b: a + b, [1, 2, 3])

→

NameError

 

Correct:

from functools import reduce

🎯 Practice Questions 

1. Create a lambda function that returns the cube of a number. 

2. Use map() to convert a list of temperatures from Celsius to Fahrenheit. 

3. Use filter() to find numbers greater than 50. 

4. Use reduce() to calculate the product of a list of numbers. 

5. Combine filter() and map() to square only the odd numbers in a list.

🎯 Key Takeaways

✅ Lambda functions are short, anonymous functions.

✅ map() transforms every element in an iterable.

✅ filter() selects elements based on a condition.

✅ reduce() combines all elements into a single value.

✅ These functions are widely used for data transformation, preprocessing, and feature engineering in Data Science.

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🚀 Data Science Roadmap 2026

📘 Phase 1: Programming Fundamentals

🐍 Topic 10: Python Modules, Packages & File Handling

Welcome back! 👋

So far, you've learned Python fundamentals, functions, data structures, list comprehensions, and functional programming. In this lesson, you'll learn how to organize your code into modules and packages and how to read from and write to files.

These skills are essential for every Data Scientist because real-world projects involve working with multiple Python files, libraries, and datasets stored in files.

🔹 1. What is a Module?

A module is a Python file (".py") that contains functions, variables, or classes that can be reused in other Python programs.

Instead of writing the same code repeatedly, you can create a module once and import it wherever needed.

Example:

Suppose you have a file named calculator.py

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

def subtract(a, b):
    return a - b

Now use it in another file:

import calculator

print(calculator.add(10, 5))

Output: 15

🔹 2. Importing Modules

Python provides different ways to import modules.

Import the Entire Module

import math
print(math.sqrt(25))

Output: 5.0

Import Specific Functions

from math import sqrt
print(sqrt(49))

Output: 7.0

Import with an Alias

Aliases make long module names easier to use.

import math as m
print(m.pi)

Output: 3.141592653589793

🔹 3. Common Built-in Modules

Some commonly used Python modules are:

• "math" → Mathematical operations

• "random" → Generate random numbers

• "datetime" → Work with dates and times

• "os" → Interact with the operating system

• "sys" → Access system-specific information

• "statistics" → Perform statistical calculations

Example:

import random
print(random.randint(1, 10))

This generates a random integer between 1 and 10.

🔹 4. What is a Package?

A package is a collection of related modules organized into folders.

Example:

project/
│
├── main.py
├── utilities/
│   ├── init.py
│   ├── calculator.py
│   └── helper.py

Packages help organize large Python projects into manageable sections.

🔹 5. File Handling

Most Data Science projects involve reading data from files such as:

• CSV files

• Text files

• Excel files

• JSON files

Python provides built-in functions for file handling.

🔹 6. Opening a File

Syntax: open(file_name, mode)

Common modes:

Mode | Description

"r" | Read

"w" | Write (overwrites existing content)

"a" | Append

"x" | Create a new file

"rb" | Read binary files

"wb" | Write binary files

🔹 7. Reading a File

Suppose sample.txt contains:

Welcome to Python
Learning File Handling

Python code:

file = open("sample.txt", "r")
print(file.read())
file.close()

Output:

Welcome to Python
Learning File Handling

🔹 8. Writing to a File

file = open("sample.txt", "w")
file.write("Hello Data Science!")
file.close()

This replaces the previous contents of the file.

🔹 9. Appending to a File

file = open("sample.txt", "a")
file.write("\nPython is awesome!")
file.close()

This adds new content without removing existing data.

🔹 10. Using the "with" Statement ⭐

The recommended way to work with files is by using the "with" statement.

It automatically closes the file after use.

with open("sample.txt", "r") as file:
    print(file.read())

You don't need to call

close()

manually.

🔹 11. Reading a File Line by Line

with open("sample.txt", "r") as file:
    for line in file:
        print(line.strip())

This is useful for processing large files efficiently.

🔹 12. Real-World Data Science Example

Suppose you have a text file containing sales data:

100
250
175
300

Python code:

total = 0
with open("sales.txt", "r") as file:
    for line in file:
        total += int(line)
print(total)

Output:

825

In real-world projects, similar logic is used to process datasets before loading them into Pandas.

🔹 13. Common Mistakes 

❌ Forgetting to Close the File

file = open("sample.txt", "r")
print(file.read())

Always use:

with open("sample.txt", "r") as file:
    print(file.read())

❌ Opening a Non-Existent File

open("data.txt", "r")

If the file doesn't exist, Python raises a

FileNotFoundError

.

Always verify that the file exists or handle exceptions appropriately.

🎯 Practice Questions 

1. Create your own Python module with two functions and import it into another file. 

2. Import the "math" module and calculate the square root of 144. 

3. Create a text file and write five lines into it. 

4. Read a text file line by line using the "with" statement. 

5. Read a file containing numbers and calculate their average. 

🎯 Key Takeaways

✅ A module is a reusable Python file containing code.

✅ A package is a collection of related modules.

✅ Use "import" to access modules and their functions.

✅ Use "open()" to read and write files.

✅ Prefer the "with" statement because it automatically closes files.

✅ File handling is a fundamental skill for reading datasets, logs, configuration files, and other real-world data sources. 

Mastering modules, packages, and file handling will prepare you for working with Python libraries like Pandas, NumPy, and Scikit-learn, where data is frequently loaded from external files.

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🚀 Data Science Roadmap 2026

📘 Phase 2: Mathematics for Data Science

📖 Topic 1: Basic Mathematics (Arithmetic, Fractions, Exponents & Logarithms)

Now it's time to build the mathematical foundation behind Machine Learning and Artificial Intelligence.

🔹 1. Why Mathematics is Important in Data Science?

Mathematics helps Data Scientists:

✅ Understand Machine Learning algorithms

✅ Analyze data correctly

✅ Optimize models

✅ Measure performance

Without mathematics, it becomes difficult to understand how models learn from data.

🔹 2. Arithmetic Operations

Arithmetic is the foundation of all mathematical calculations.

The five basic operations are:

Addition: Symbol +

Example: 10 + 5 = 15

Subtraction: Symbol -

Example: 10 - 5 = 5

Multiplication: Symbol ×

Example: 10 × 5 = 50

Division: Symbol ÷

Example: 10 ÷ 5 = 2

Modulus: Symbol %

Example: 10 % 3 = 1

🔹 3. Order of Operations (BODMAS / PEMDAS)

When an expression contains multiple operations, follow this order:

1. Brackets ( )

2. Orders (Powers/Roots)

3. Division

4. Multiplication

5. Addition

6. Subtraction

Example: 5 + 2 × 3

First perform multiplication: 2 × 3 = 6

Then addition: 5 + 6 = 11

🔹 4. Fractions

A fraction represents a part of a whole.

Example: 3/4

Here: Numerator = 3, Denominator = 4

Converting Fractions to Decimals

Example: 3 ÷ 4 = 0.75

Converting Decimals to Percentages

Multiply by 100.

Example: 0.75 × 100 = 75%

🔹 5. Percentages

Percentage means "per hundred."

Formula: Percentage = (Part / Total) × 100

Example: A student scored 90 out of 120. (90 / 120) × 100 = 75%

Percentages are widely used in: Accuracy, Precision, Recall, Business reports

🔹 6. Exponents (Powers)

An exponent tells us how many times a number is multiplied by itself.

Example: 2³ = 2 × 2 × 2 = 8

More examples: 5² = 25, 10² = 100, 3⁴ = 81

🔹 7. Square Root

Square root is the opposite of squaring.

Example: √49 = 7, √100 = 10, √144 = 12

Square roots are used in: Standard Deviation, Euclidean Distance, Machine Learning algorithms

🔹 8. Logarithms ⭐

Logarithms are one of the most important mathematical concepts in Data Science.

A logarithm answers: "To what power should we raise a number to get another number?"

Example: log₂(8) = 3 because 2³ = 8

Another example: log₁₀(1000) = 3 because 10³ = 1000

🔹 9. Why Logarithms Matter in Data Science?

Logarithms are used in:

✅ Feature Engineering

✅ Data Transformation

✅ Loss Functions

✅ Machine Learning Algorithms

✅ Neural Networks

For example, if salary values range from ₹10,000 to ₹10,00,000, applying a logarithmic transformation reduces the range, making the data easier for some machine learning models to learn from.

🔹 10. Real-World Example

Suppose a company's revenue grows like this: 100, 1,000, 10,000, 100,000, 1,000,000

This range is very large.

Using logarithms it becomes: 2, 3, 4, 5, 6

The data becomes much easier to visualize and analyze.

🔹 11. Common Mistakes

❌ Ignoring the order of operations.

Example: 5 + 2 × 3

Correct answer: 11

❌ Confusing percentages with decimals.

Remember: 0.25 = 25%, 0.50 = 50%, 1.00 = 100%

🎯 Practice Questions

1. Calculate 25 + 15 × 2.

2. Convert 7/8 into a decimal.

3. Convert 0.45 into a percentage.

4. Find the value of 6².

5. What is log₁₀(100)?

🎯 Key Takeaways

✅ Arithmetic forms the foundation of mathematics.

✅ Always follow the BODMAS/PEMDAS rule.

✅ Fractions, decimals, and percentages are interchangeable representations.

✅ Exponents represent repeated multiplication.

✅ Square roots are widely used in statistics and machine learning.

✅ Logarithms help transform large numerical values and are commonly used in Data Science and Machine Learning.

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Essential Python Libraries to build your career in Data Science 📊👇

1. NumPy:
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2. Pandas:
- Data manipulation and analysis with powerful data structures (DataFrame, Series).

3. Matplotlib:
- 2D plotting library for creating visualizations.

4. Seaborn:
- Statistical data visualization built on top of Matplotlib.

5. Scikit-learn:
- Machine learning toolkit for classification, regression, clustering, etc.

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7. PyTorch:
- Deep learning library, particularly popular for neural network research.

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9. Statsmodels:
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10. NLTK (Natural Language Toolkit):
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14. Beautiful Soup:
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15. OpenCV:
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