Python Roadmap for 2025: Complete Guide
1. Python Fundamentals
1.1 Variables, constants, and comments.
1.2 Data types: int, float, str, bool, complex.
1.3 Input and output (input(), print(), formatted strings).
1.4 Python syntax: Indentation and code structure.
2. Operators
2.1 Arithmetic: +, -, *, /, %, //, **.
2.2 Comparison: ==, !=, <, >, <=, >=.
2.3 Logical: and, or, not.
2.4 Bitwise: &, |, ^, ~, <<, >>.
2.5 Identity: is, is not.
2.6 Membership: in, not in.
3. Control Flow
3.1 Conditional statements: if, elif, else.
3.2 Loops: for, while.
3.3 Loop control: break, continue, pass.
4. Data Structures
4.1 Lists: Indexing, slicing, methods (append(), pop(), sort(), etc.).
4.2 Tuples: Immutability, packing/unpacking.
4.3 Dictionaries: Key-value pairs, methods (get(), items(), etc.).
4.4 Sets: Unique elements, set operations (union, intersection).
4.5 Strings: Immutability, methods (split(), strip(), replace()).
5. Functions
5.1 Defining functions with def.
5.2 Arguments: Positional, keyword, default, *args, **kwargs.
5.3 Anonymous functions (lambda).
5.4 Recursion.
6. Modules and Packages
6.1 Importing: import, from ... import.
6.2 Standard libraries: math, os, sys, random, datetime, time.
6.3 Installing external libraries with pip.
7. File Handling
7.1 Open and close files (open(), close()).
7.2 Read and write (read(), write(), readlines()).
7.3 Using context managers (with open(...)).
8. Object-Oriented Programming (OOP)
8.1 Classes and objects.
8.2 Methods and attributes.
8.3 Constructor (init).
8.4 Inheritance, polymorphism, encapsulation.
8.5 Special methods (str, repr, etc.).
9. Error and Exception Handling
9.1 try, except, else, finally.
9.2 Raising exceptions (raise).
9.3 Custom exceptions.
10. Comprehensions
10.1 List comprehensions.
10.2 Dictionary comprehensions.
10.3 Set comprehensions.
11. Iterators and Generators
11.1 Creating iterators using iter() and next().
11.2 Generators with yield.
11.3 Generator expressions.
12. Decorators and Closures
12.1 Functions as first-class citizens.
12.2 Nested functions.
12.3 Closures.
12.4 Creating and applying decorators.
13. Advanced Topics
13.1 Context managers (with statement).
13.2 Multithreading and multiprocessing.
13.3 Asynchronous programming with async and await.
13.4 Python's Global Interpreter Lock (GIL).
14. Python Internals
14.1 Mutable vs immutable objects.
14.2 Memory management and garbage collection.
14.3 Python's name == "main" mechanism.
15. Libraries and Frameworks
15.1 Data Science: NumPy, Pandas, Matplotlib, Seaborn.
15.2 Web Development: Flask, Django, FastAPI.
15.3 Testing: unittest, pytest.
15.4 APIs: requests, http.client.
15.5 Automation: selenium, os.
15.6 Machine Learning: scikit-learn, TensorFlow, PyTorch.
16. Tools and Best Practices
16.1 Debugging: pdb, breakpoints.
16.2 Code style: PEP 8 guidelines.
16.3 Virtual environments: venv.
16.4 Version control: Git + GitHub.
👇 Python Interview 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀
https://news.1rj.ru/str/dsabooks
📘 𝗣𝗿𝗲𝗺𝗶𝘂𝗺 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 : https://topmate.io/coding/914624
📙 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲: https://whatsapp.com/channel/0029VaxbzNFCxoAmYgiGTL3Z
Join What's app channel for jobs updates: t.me/getjobss
1. Python Fundamentals
1.1 Variables, constants, and comments.
1.2 Data types: int, float, str, bool, complex.
1.3 Input and output (input(), print(), formatted strings).
1.4 Python syntax: Indentation and code structure.
2. Operators
2.1 Arithmetic: +, -, *, /, %, //, **.
2.2 Comparison: ==, !=, <, >, <=, >=.
2.3 Logical: and, or, not.
2.4 Bitwise: &, |, ^, ~, <<, >>.
2.5 Identity: is, is not.
2.6 Membership: in, not in.
3. Control Flow
3.1 Conditional statements: if, elif, else.
3.2 Loops: for, while.
3.3 Loop control: break, continue, pass.
4. Data Structures
4.1 Lists: Indexing, slicing, methods (append(), pop(), sort(), etc.).
4.2 Tuples: Immutability, packing/unpacking.
4.3 Dictionaries: Key-value pairs, methods (get(), items(), etc.).
4.4 Sets: Unique elements, set operations (union, intersection).
4.5 Strings: Immutability, methods (split(), strip(), replace()).
5. Functions
5.1 Defining functions with def.
5.2 Arguments: Positional, keyword, default, *args, **kwargs.
5.3 Anonymous functions (lambda).
5.4 Recursion.
6. Modules and Packages
6.1 Importing: import, from ... import.
6.2 Standard libraries: math, os, sys, random, datetime, time.
6.3 Installing external libraries with pip.
7. File Handling
7.1 Open and close files (open(), close()).
7.2 Read and write (read(), write(), readlines()).
7.3 Using context managers (with open(...)).
8. Object-Oriented Programming (OOP)
8.1 Classes and objects.
8.2 Methods and attributes.
8.3 Constructor (init).
8.4 Inheritance, polymorphism, encapsulation.
8.5 Special methods (str, repr, etc.).
9. Error and Exception Handling
9.1 try, except, else, finally.
9.2 Raising exceptions (raise).
9.3 Custom exceptions.
10. Comprehensions
10.1 List comprehensions.
10.2 Dictionary comprehensions.
10.3 Set comprehensions.
11. Iterators and Generators
11.1 Creating iterators using iter() and next().
11.2 Generators with yield.
11.3 Generator expressions.
12. Decorators and Closures
12.1 Functions as first-class citizens.
12.2 Nested functions.
12.3 Closures.
12.4 Creating and applying decorators.
13. Advanced Topics
13.1 Context managers (with statement).
13.2 Multithreading and multiprocessing.
13.3 Asynchronous programming with async and await.
13.4 Python's Global Interpreter Lock (GIL).
14. Python Internals
14.1 Mutable vs immutable objects.
14.2 Memory management and garbage collection.
14.3 Python's name == "main" mechanism.
15. Libraries and Frameworks
15.1 Data Science: NumPy, Pandas, Matplotlib, Seaborn.
15.2 Web Development: Flask, Django, FastAPI.
15.3 Testing: unittest, pytest.
15.4 APIs: requests, http.client.
15.5 Automation: selenium, os.
15.6 Machine Learning: scikit-learn, TensorFlow, PyTorch.
16. Tools and Best Practices
16.1 Debugging: pdb, breakpoints.
16.2 Code style: PEP 8 guidelines.
16.3 Virtual environments: venv.
16.4 Version control: Git + GitHub.
👇 Python Interview 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀
https://news.1rj.ru/str/dsabooks
📘 𝗣𝗿𝗲𝗺𝗶𝘂𝗺 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 : https://topmate.io/coding/914624
📙 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲: https://whatsapp.com/channel/0029VaxbzNFCxoAmYgiGTL3Z
Join What's app channel for jobs updates: t.me/getjobss
❤2
SQL Basics for Data Analysts
SQL (Structured Query Language) is used to retrieve, manipulate, and analyze data stored in databases.
1️⃣ Understanding Databases & Tables
Databases store structured data in tables.
Tables contain rows (records) and columns (fields).
Each column has a specific data type (INTEGER, VARCHAR, DATE, etc.).
2️⃣ Basic SQL Commands
Let's start with some fundamental queries:
🔹 SELECT – Retrieve Data
🔹 WHERE – Filter Data
🔹 ORDER BY – Sort Data
🔹 LIMIT – Restrict Number of Results
🔹 DISTINCT – Remove Duplicates
Mini Task for You: Try to write an SQL query to fetch the top 3 highest-paid employees from an "employees" table.
You can find free SQL Resources here
👇👇
https://news.1rj.ru/str/mysqldata
Like this post if you want me to continue covering all the topics! 👍❤️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
#sql
SQL (Structured Query Language) is used to retrieve, manipulate, and analyze data stored in databases.
1️⃣ Understanding Databases & Tables
Databases store structured data in tables.
Tables contain rows (records) and columns (fields).
Each column has a specific data type (INTEGER, VARCHAR, DATE, etc.).
2️⃣ Basic SQL Commands
Let's start with some fundamental queries:
🔹 SELECT – Retrieve Data
SELECT * FROM employees; -- Fetch all columns from 'employees' table SELECT name, salary FROM employees; -- Fetch specific columns
🔹 WHERE – Filter Data
SELECT * FROM employees WHERE department = 'Sales'; -- Filter by department SELECT * FROM employees WHERE salary > 50000; -- Filter by salary
🔹 ORDER BY – Sort Data
SELECT * FROM employees ORDER BY salary DESC; -- Sort by salary (highest first) SELECT name, hire_date FROM employees ORDER BY hire_date ASC; -- Sort by hire date (oldest first)
🔹 LIMIT – Restrict Number of Results
SELECT * FROM employees LIMIT 5; -- Fetch only 5 rows SELECT * FROM employees WHERE department = 'HR' LIMIT 10; -- Fetch first 10 HR employees
🔹 DISTINCT – Remove Duplicates
SELECT DISTINCT department FROM employees; -- Show unique departments
Mini Task for You: Try to write an SQL query to fetch the top 3 highest-paid employees from an "employees" table.
You can find free SQL Resources here
👇👇
https://news.1rj.ru/str/mysqldata
Like this post if you want me to continue covering all the topics! 👍❤️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
#sql
❤3
If I wanted to get my opportunity to interview at Google or Amazon for SDE roles in the next 6-8 months…
Here’s exactly how I’d approach it (I’ve taught this to 100s of students and followed it myself to land interviews at 3+ FAANGs):
► Step 1: Learn to Code (from scratch, even if you’re from non-CS background)
I helped my sister go from zero coding knowledge (she studied Biology and Electrical Engineering) to landing a job at Microsoft.
We started with:
- A simple programming language (C++, Java, Python — pick one)
- FreeCodeCamp on YouTube for beginner-friendly lectures
- Key rule: Don’t just watch. Code along with the video line by line.
Time required: 30–40 days to get good with loops, conditions, syntax.
► Step 2: Start with DSA before jumping to development
Why?
- 90% of tech interviews in top companies focus on Data Structures & Algorithms
- You’ll need time to master it, so start early.
Start with:
- Arrays → Linked List → Stacks → Queues
- You can follow the DSA videos on my channel.
- Practice while learning is a must.
► Step 3: Follow a smart topic order
Once you’re done with basics, follow this path:
1. Searching & Sorting
2. Recursion & Backtracking
3. Greedy
4. Sliding Window & Two Pointers
5. Trees & Graphs
6. Dynamic Programming
7. Tries, Heaps, and Union Find
Make revision notes as you go — note down how you solved each question, what tricks worked, and how you optimized it.
► Step 4: Start giving contests (don’t wait till you’re “ready”)
Most students wait to “finish DSA” before attempting contests.
That’s a huge mistake.
Contests teach you:
- Time management under pressure
- Handling edge cases
- Thinking fast
Platforms: LeetCode Weekly/ Biweekly, Codeforces, AtCoder, etc.
And after every contest, do upsolving — solve the questions you couldn’t during the contest.
► Step 5: Revise smart
Create a “Revision Sheet” with 100 key problems you’ve solved and want to reattempt.
Every 2-3 weeks, pick problems randomly and solve again without seeing solutions.
This trains your recall + improves your clarity.
Coding Projects:👇
https://whatsapp.com/channel/0029VazkxJ62UPB7OQhBE502
ENJOY LEARNING 👍👍
Here’s exactly how I’d approach it (I’ve taught this to 100s of students and followed it myself to land interviews at 3+ FAANGs):
► Step 1: Learn to Code (from scratch, even if you’re from non-CS background)
I helped my sister go from zero coding knowledge (she studied Biology and Electrical Engineering) to landing a job at Microsoft.
We started with:
- A simple programming language (C++, Java, Python — pick one)
- FreeCodeCamp on YouTube for beginner-friendly lectures
- Key rule: Don’t just watch. Code along with the video line by line.
Time required: 30–40 days to get good with loops, conditions, syntax.
► Step 2: Start with DSA before jumping to development
Why?
- 90% of tech interviews in top companies focus on Data Structures & Algorithms
- You’ll need time to master it, so start early.
Start with:
- Arrays → Linked List → Stacks → Queues
- You can follow the DSA videos on my channel.
- Practice while learning is a must.
► Step 3: Follow a smart topic order
Once you’re done with basics, follow this path:
1. Searching & Sorting
2. Recursion & Backtracking
3. Greedy
4. Sliding Window & Two Pointers
5. Trees & Graphs
6. Dynamic Programming
7. Tries, Heaps, and Union Find
Make revision notes as you go — note down how you solved each question, what tricks worked, and how you optimized it.
► Step 4: Start giving contests (don’t wait till you’re “ready”)
Most students wait to “finish DSA” before attempting contests.
That’s a huge mistake.
Contests teach you:
- Time management under pressure
- Handling edge cases
- Thinking fast
Platforms: LeetCode Weekly/ Biweekly, Codeforces, AtCoder, etc.
And after every contest, do upsolving — solve the questions you couldn’t during the contest.
► Step 5: Revise smart
Create a “Revision Sheet” with 100 key problems you’ve solved and want to reattempt.
Every 2-3 weeks, pick problems randomly and solve again without seeing solutions.
This trains your recall + improves your clarity.
Coding Projects:👇
https://whatsapp.com/channel/0029VazkxJ62UPB7OQhBE502
ENJOY LEARNING 👍👍
❤3
Beginner’s Roadmap to Learn Data Structures & Algorithms
1. Foundations: Start with the basics of programming and mathematical concepts to build a strong foundation.
2. Data Structure: Dive into essential data structures like arrays, linked lists, stacks, and queues to organise and store data efficiently.
3. Searching & Sorting: Learn various search and sort techniques to optimise data retrieval and organisation.
4. Trees & Graphs: Understand the concepts of binary trees and graph representation to tackle complex hierarchical data.
5. Recursion: Grasp the principles of recursion and how to implement recursive algorithms for problem-solving.
6. Advanced Data Structures: Explore advanced structures like hashing, heaps, and hash maps to enhance data manipulation.
7. Algorithms: Master algorithms such as greedy, divide and conquer, and dynamic programming to solve intricate problems.
8. Advanced Topics: Delve into backtracking, string algorithms, and bit manipulation for a deeper understanding.
9. Problem Solving: Practice on coding platforms like LeetCode to sharpen your skills and solve real-world algorithmic challenges.
10. Projects & Portfolio: Build real-world projects and showcase your skills on GitHub to create an impressive portfolio.
Best DSA RESOURCES: https://topmate.io/coding/886874
All the best 👍👍
1. Foundations: Start with the basics of programming and mathematical concepts to build a strong foundation.
2. Data Structure: Dive into essential data structures like arrays, linked lists, stacks, and queues to organise and store data efficiently.
3. Searching & Sorting: Learn various search and sort techniques to optimise data retrieval and organisation.
4. Trees & Graphs: Understand the concepts of binary trees and graph representation to tackle complex hierarchical data.
5. Recursion: Grasp the principles of recursion and how to implement recursive algorithms for problem-solving.
6. Advanced Data Structures: Explore advanced structures like hashing, heaps, and hash maps to enhance data manipulation.
7. Algorithms: Master algorithms such as greedy, divide and conquer, and dynamic programming to solve intricate problems.
8. Advanced Topics: Delve into backtracking, string algorithms, and bit manipulation for a deeper understanding.
9. Problem Solving: Practice on coding platforms like LeetCode to sharpen your skills and solve real-world algorithmic challenges.
10. Projects & Portfolio: Build real-world projects and showcase your skills on GitHub to create an impressive portfolio.
Best DSA RESOURCES: https://topmate.io/coding/886874
All the best 👍👍
❤2