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Step-by-Step Approach to Learn Python
➊ Learn the Basics → Syntax, Variables, Data Types (int, float, string, boolean)
↓
➋ Control Flow → If-Else, Loops (For, While), List Comprehensions
↓
➌ Data Structures → Lists, Tuples, Sets, Dictionaries
↓
➍ Functions & Modules → Defining Functions, Lambda Functions, Importing Modules
↓
➎ File Handling → Reading/Writing Files, CSV, JSON
↓
➏ Object-Oriented Programming (OOP) → Classes, Objects, Inheritance, Polymorphism
↓
➐ Error Handling & Debugging → Try-Except, Logging, Debugging Techniques
↓
➑ Advanced Topics → Regular Expressions, Multi-threading, Decorators, Generators

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What are the common built-in data types in Python?

Python supports the below-mentioned built-in data types:

Immutable data types:

👉Number
👉String
👉Tuple

Mutable data types:

👉List
👉Dictionary
👉set

What is the lambda function in Python?

A lambda function is an anonymous function (a function that does not have a name) in Python. To define anonymous functions, we use the ‘lambda’ keyword instead of the ‘def’ keyword, hence the name ‘lambda function’. Lambda functions can have any number of arguments but only one statement.

Example:

l = lambda x,y : x*y
print(a(5, 6))

Output:30

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Important Sorting Algorithms-

Bubble Sort: Bubble Sort is the most basic sorting algorithm, and it works by repeatedly swapping adjacent elements if they are out of order.

Merge Sort: Merge sort is a sorting technique that uses the divide and conquer strategy.

Quicksort: Quicksort is a popular sorting algorithm that performs n log n comparisons on average when sorting an array of n elements. It is a more efficient and faster sorting algorithm.

Heap Sort: Heap sort works by visualizing the array elements as a special type of complete binary tree known as a heap.

Important Searching Algorithms-

Binary Search: Binary search employs the divide and conquer strategy, in which a sorted list is divided into two halves and the item is compared to the list’s middle element. If a match is found, the middle element’s location is returned.

Breadth-First Search(BFS): Breadth-first search is a graph traversal algorithm that begins at the root node and explores all neighboring nodes.

Depth-First Search(DFS): The depth-first search (DFS) algorithm begins with the first node of the graph and proceeds to go deeper and deeper until we find the goal node or node with no children.

#coding

Here is the list of few projects (found on kaggle). They cover Basics of Python, Advanced Statistics, Supervised Learning (Regression and Classification problems) & Data Science

Please also check the discussions and notebook submissions for different approaches and solution after you tried yourself.

1. Basic python and statistics

Pima Indians :- https://www.kaggle.com/uciml/pima-indians-diabetes-database
Cardio Goodness fit :- https://www.kaggle.com/saurav9786/cardiogoodfitness
Automobile :- https://www.kaggle.com/toramky/automobile-dataset

2. Advanced Statistics

Game of Thrones:-https://www.kaggle.com/mylesoneill/game-of-thrones
World University Ranking:-https://www.kaggle.com/mylesoneill/world-university-rankings
IMDB Movie Dataset:- https://www.kaggle.com/carolzhangdc/imdb-5000-movie-dataset

3. Supervised Learning

a) Regression Problems

How much did it rain :- https://www.kaggle.com/c/how-much-did-it-rain-ii/overview
Inventory Demand:- https://www.kaggle.com/c/grupo-bimbo-inventory-demand
Property Inspection predictiion:- https://www.kaggle.com/c/liberty-mutual-group-property-inspection-prediction
Restaurant Revenue prediction:- https://www.kaggle.com/c/restaurant-revenue-prediction/data
IMDB Box office Prediction:-https://www.kaggle.com/c/tmdb-box-office-prediction/overview

b) Classification problems

Employee Access challenge :- https://www.kaggle.com/c/amazon-employee-access-challenge/overview
Titanic :- https://www.kaggle.com/c/titanic
San Francisco crime:- https://www.kaggle.com/c/sf-crime
Customer satisfcation:-https://www.kaggle.com/c/santander-customer-satisfaction
Trip type classification:- https://www.kaggle.com/c/walmart-recruiting-trip-type-classification
Categorize cusine:- https://www.kaggle.com/c/whats-cooking

4. Some helpful Data science projects for beginners

https://www.kaggle.com/c/house-prices-advanced-regression-techniques

https://www.kaggle.com/c/digit-recognizer

https://www.kaggle.com/c/titanic

5. Intermediate Level Data science Projects

Black Friday Data : https://www.kaggle.com/sdolezel/black-friday

Human Activity Recognition Data : https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones

Trip History Data : https://www.kaggle.com/pronto/cycle-share-dataset

Million Song Data : https://www.kaggle.com/c/msdchallenge

Census Income Data : https://www.kaggle.com/c/census-income/data

Movie Lens Data : https://www.kaggle.com/grouplens/movielens-20m-dataset

Twitter Classification Data : https://www.kaggle.com/c/twitter-sentiment-analysis2

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Python Detailed Roadmap 🚀

📌 1. Basics
◼ Data Types & Variables
◼ Operators & Expressions
◼ Control Flow (if, loops)

📌 2. Functions & Modules
◼ Defining Functions
◼ Lambda Functions
◼ Importing & Creating Modules

📌 3. File Handling
◼ Reading & Writing Files
◼ Working with CSV & JSON

📌 4. Object-Oriented Programming (OOP)
◼ Classes & Objects
◼ Inheritance & Polymorphism
◼ Encapsulation

📌 5. Exception Handling
◼ Try-Except Blocks
◼ Custom Exceptions

📌 6. Advanced Python Concepts
◼ List & Dictionary Comprehensions
◼ Generators & Iterators
◼ Decorators

📌 7. Essential Libraries
◼ NumPy (Arrays & Computations)
◼ Pandas (Data Analysis)
◼ Matplotlib & Seaborn (Visualization)

📌 8. Web Development & APIs
◼ Web Scraping (BeautifulSoup, Scrapy)
◼ API Integration (Requests)
◼ Flask & Django (Backend Development)

📌 9. Automation & Scripting
◼ Automating Tasks with Python
◼ Working with Selenium & PyAutoGUI

📌 10. Data Science & Machine Learning
◼ Data Cleaning & Preprocessing
◼ Scikit-Learn (ML Algorithms)
◼ TensorFlow & PyTorch (Deep Learning)

📌 11. Projects
◼ Build Real-World Applications
◼ Showcase on GitHub

📌 12. ✅ Apply for Jobs
◼ Strengthen Resume & Portfolio
◼ Prepare for Technical Interviews

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🔰 Deep Python Roadmap for Beginners 🐍

Setup & Installation 🖥⚙️
• Install Python, choose an IDE (VS Code, PyCharm)
• Set up virtual environments for project isolation 🌎

Basic Syntax & Data Types 📝🔢
• Learn variables, numbers, strings, booleans
• Understand comments, basic input/output, and simple expressions ✍️

Control Flow & Loops 🔄🔀
• Master conditionals (if, elif, else)
• Practice loops (for, while) and use control statements like break and continue 👮

Functions & Scope ⚙️🎯
• Define functions with def and learn about parameters and return values
• Explore lambda functions, recursion, and variable scope 📜

Data Structures 📊📚
• Work with lists, tuples, sets, and dictionaries
• Learn list comprehensions and built-in methods for data manipulation ⚙️

Object-Oriented Programming (OOP) 🏗👩‍💻
• Understand classes, objects, and methods
• Dive into inheritance, polymorphism, and encapsulation 🔍

React "❤️" for Part 2

Python Cheatsheet 🚀

1️⃣ Variables & Data Types

x = 10 (Integer)

y = 3.14 (Float)

name = "Python" (String)

is_valid = True (Boolean)

items = [1, 2, 3] (List)

data = (1, 2, 3) (Tuple)

person = {"name": "Alice", "age": 25} (Dictionary)


2️⃣ Operators

Arithmetic: +, -, *, /, //, %, **

Comparison: ==, !=, >, <, >=, <=

Logical: and, or, not

Membership: in, not in


3️⃣ Control Flow

If-Else:

if age > 18:
print("Adult")
elif age == 18:
print("Just turned 18")
else:
print("Minor")

Loops:

for i in range(5):
print(i)
while x < 10:
x += 1


4️⃣ Functions

Defining & Calling:

def greet(name):
return f"Hello, {name}"
print(greet("Alice"))

Lambda Functions: add = lambda x, y: x + y


5️⃣ Lists & Dictionary Operations

Append: items.append(4)

Remove: items.remove(2)

List Comprehension: [x**2 for x in range(5)]

Dictionary Access: person["name"]


6️⃣ File Handling

Read File:

with open("file.txt", "r") as f:
content = f.read()

Write File:

with open("file.txt", "w") as f:
f.write("Hello, World!")


7️⃣ Exception Handling

try:
result = 10 / 0
except ZeroDivisionError:
print("Cannot divide by zero!")
finally:
print("Done")

8️⃣ Modules & Packages

Importing:

import math
print(math.sqrt(25))

Creating a Module (mymodule.py):

def add(x, y):
return x + y

Usage: from mymodule import add


9️⃣ Object-Oriented Programming (OOP)

Defining a Class:

class Person:
def init(self, name, age):
self.name = name
self.age = age
def greet(self):
return f"Hello, my name is {self.name}"

Creating an Object: p = Person("Alice", 25)


🔟 Useful Libraries

NumPy: import numpy as np

Pandas: import pandas as pd

Matplotlib: import matplotlib.pyplot as plt

Requests: import requests

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Python Cheatsheet 🚀

1️⃣ Variables & Data Types

x = 10 (Integer)

y = 3.14 (Float)

name = "Python" (String)

is_valid = True (Boolean)

items = [1, 2, 3] (List)

data = (1, 2, 3) (Tuple)

person = {"name": "Alice", "age": 25} (Dictionary)


2️⃣ Operators

Arithmetic: +, -, *, /, //, %, **

Comparison: ==, !=, >, <, >=, <=

Logical: and, or, not

Membership: in, not in


3️⃣ Control Flow

If-Else:

if age > 18:
print("Adult")
elif age == 18:
print("Just turned 18")
else:
print("Minor")

Loops:

for i in range(5):
print(i)
while x < 10:
x += 1


4️⃣ Functions

Defining & Calling:

def greet(name):
return f"Hello, {name}"
print(greet("Alice"))

Lambda Functions: add = lambda x, y: x + y


5️⃣ Lists & Dictionary Operations

Append: items.append(4)

Remove: items.remove(2)

List Comprehension: [x**2 for x in range(5)]

Dictionary Access: person["name"]


6️⃣ File Handling

Read File:

with open("file.txt", "r") as f:
content = f.read()

Write File:

with open("file.txt", "w") as f:
f.write("Hello, World!")


7️⃣ Exception Handling

try:
result = 10 / 0
except ZeroDivisionError:
print("Cannot divide by zero!")
finally:
print("Done")

8️⃣ Modules & Packages

Importing:

import math
print(math.sqrt(25))

Creating a Module (mymodule.py):

def add(x, y):
return x + y

Usage: from mymodule import add


9️⃣ Object-Oriented Programming (OOP)

Defining a Class:

class Person:
def init(self, name, age):
self.name = name
self.age = age
def greet(self):
return f"Hello, my name is {self.name}"

Creating an Object: p = Person("Alice", 25)


🔟 Useful Libraries

NumPy: import numpy as np

Pandas: import pandas as pd

Matplotlib: import matplotlib.pyplot as plt

Requests: import requests

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