🌟 Why Java Feels Harder Than C++ Sometimes 🌟

1. Java: Why write one line when you can write 5? Every variable needs its type, every method needs a class.
C++: Write code anywhere. Procedural, OOP, or just chaos. Your call. 🚀


2. Java: Even the simplest main needs a whole public class. 🤡
C++: Need a main()? Just write it. Done.


3. Java: Checked exceptions—catch me or I'll scream. 🎭
C++: Exceptions? Handle them... or don’t. No hard feelings, bro.


4. Java: No unsigned integers. Imagine life without them. Big sad. 😔
C++: Choose your poison—int, unsigned, long long. So many options to confuse you.


5. Java: Print something? Get ready: System.out.println(). Keyboard workout. ⌨️💪
C++: cout <<. Done faster than you can say “hello world.”


6. Java: Multi-threading? Write a novel and pray the JVM approves. 📚
C++: Threading? Use libraries. Use APIs. DIY is the way.


7. Java: Generics—looks simple but screams at you later. 🤯
C++: Templates are like magic. Dark magic—you’ll debug forever, though.


8. Java: Misspell a package name? Good luck. 📦
C++: Namespaces confuse, but fewer typos.


9. Java: The JVM controls your code and life. 👁️
C++: Compile once, run anywhere—until undefined behavior strikes. 🌀


10. Java: No operators in the name.
C++: The name is an operator. That's how cool it is. 😎

8 Best Note-Taking Apps for Programmers

📜 Notion
📜 CoderNotes
📜 Quiver
📜 MedleyText
📜 Boostnote
📜 Stackedit
📜 Evernote
📜 Microsoft onenote

Ages of Operating Systems👨🏻‍💻😎

📂 Windows 11 (3 years old)
🪟 Windows 10 (8 years old)
🍎 macOS Yosemite (10 years old)
🐉 Kali Linux (11 years old)
💻 Windows 8 (12 years old)
🌐 Manjaro (11 years old)
💻 Windows 7 (14 years old)
🖥️ Windows Vista (17 years old)
🌿 Linux Mint (18 years old)
🐧 Ubuntu (20 years old)
⚙️ Fedora (20 years old)
🔧 OpenSUSE (20 years old)
⚙️ CentOS (20 years old)
🐧 Arch Linux (22 years old)
🍏 macOS (22 years old)
💻 Windows XP (23 years old)
🖥️ Windows 2000 (24 years old)
📱 Windows 98 (25 years old)
🌍 Windows 95 (28 years old)
💻 Windows 3.1 (29 years old)
🖥️ OS/2 (32 years old)
🐧 Debian (31 years old)
🔴 Red Hat Linux (30 years old)
🎮 AmigaOS (34 years old)
🖥️ Xenix (40 years old)
📀 VMS (44 years old)
💾 MS-DOS (42 years old)
💾 CP/M (49 years old)
🖥️ Unix (54 years old)

Top 20 DSA questions asked in Data Engineering interviews at MAANG


1. Given an array of integers and a target value, find two numbers in the array that sum up to the target. Return the indices of the two numbers.
2. How do you reverse a singly linked list?
3. How do you find the missing number in an array of size N with numbers from 1 to N+1?
4. What is the approach to detect a cycle in a linked list?
5. How would you merge two sorted arrays?
6. How do you find the Kth largest element in an unsorted array?
7. What’s the approach to finding the longest substring without repeating characters?
8. How do you find the middle element of a linked list?
9. Explain level order traversal in a binary tree.
10. How do you find the Lowest Common Ancestor (LCA) of two nodes in a binary tree?
11. How do you find the maximum sum subarray (using Kadane's Algorithm)?
12. What’s the approach for finding the longest common subsequence between two strings?
13. How would you merge two sorted linked lists?
14. How do you check if parentheses in an expression are valid?
15. What is the binary search algorithm and how do you implement it?
16. How would you find the duplicate number in an array containing numbers from 1 to N?
17. What is the approach to reversing a string?
18. How do you find the product of all elements in an array except the current element?
19. How would you group anagrams from a list of strings?
20. How would you solve a Sudoku puzzle using backtracking?

DSA Interview Preparation Resources: https://topmate.io/coding/886874

ENJOY LEARNING 👍👍

Python vs C++ vs Java

What is Python?

- Python is a programming language 🐍

- It's known for being easy to learn and read 📖

- You can use it for web development, data analysis, artificial intelligence, and more 💻🌐📊

- Python is like writing instructions for a computer in a clear and simple way 📝💡

- Python supports working with a lot of data, making it great for projects that involve big data and statistics 📈🔍

- It has a huge community, which means lots of support and resources for learners 🌍🤝

- Python is versatile; it's used in scientific fields, finance, and even in making movies and video games 🧪💰🎬🎮

- It can run on different platforms like Windows, macOS, Linux, and even Raspberry Pi 🖥️🍏🐧🍓

- Python has many libraries and frameworks that help speed up the development process for web applications, machine learning, and more 🛠️🚀

Want to practice for your next interview?

Then use this prompt and ask Chat GPT to act as an interviewer 😄👇 (Tap to copy)

I want you to act as an interviewer. I will be the
candidate and you will ask me the
interview questions for the position position. I
want you to only reply as the interviewer.
Do not write all the conservation at once. I
want you to only do the interview with me.
Ask me the questions and wait for my answers.
Do not write explanations. Ask me the
questions one by one like an interviewer does
and wait for my answers. My first
sentence is "Hi"

Now see how it goes. All the best for your preparation
Like this post if you need more content like this👍❤️

Free Resources To Crack Coding Interviews
👇👇

Coding Interview Prep FREE CERTIFIED COURSE

https://www.freecodecamp.org/learn/coding-interview-prep/#take-home-projects

Python Interview Questions and Answers

https://t.me/dsabooks/75

Beginner's guide for DSA

https://www.geeksforgeeks.org/the-ultimate-beginners-guide-for-dsa/amp/

Cracking the coding interview FREE BOOK

https://www.pdfdrive.com/cracking-the-coding-interview-189-programming-questions-and-solutions-d175292720.html

DSA Interview Questions and Answers

https://t.me/crackingthecodinginterview/77

Cracking the Coding interview: Learn 5 Essential Patterns
[4.5 star ratings out of 5]

https://bit.ly/3GUBk56

Data Science Interview Questions and Answers

https://t.me/datasciencefun/958

Java Interview Questions with Answers

https://t.me/Curiousprogrammer/106

ENJOY LEARNING 👍👍

🎁❗️TODAY FREE❗️🎁

Entry to our VIP channel is completely free today. Tomorrow it will cost $500! 🔥

JOIN 👇

https://t.me/+fN34RDsHXcZkNTUy
https://t.me/+fN34RDsHXcZkNTUy
https://t.me/+fN34RDsHXcZkNTUy

Python for Everything 👆

How Git Commands Work

Git can seem confusing at first, but a few key concepts make it clearer:

There are 4 locations for your code:
- Working Directory
- Staging Area
- Local Repository
- Remote Repository (like GitHub)

Basic commands move code between these locations
- git add stages changes
- git commit saves them locally
- git push shares them remotely
- git pull fetches updates from others

Branching allows isolated development.

Concepts like git clone, merge, rebase enable collaboration.

Graphical tools like GitHub Desktop also help by providing visual interfaces and shortcuts.

While advanced workflows are possible, understanding this basic flow unlocks Git's power.

AccioJob is conducting a walk-in drive with RSL! ✨

💼 Position: Software Engineer
💰 CTC: 7 LPA
📍 Work Location: Pune

Don't miss out! 🚀

👉🏻 For more details, eligibility, apply now: https://links.acciojob.com/4govkmk

Want to become a Data Scientist?

Here’s a quick roadmap with essential concepts:

1. Mathematics & Statistics

Linear Algebra: Matrix operations, eigenvalues, eigenvectors, and decomposition, which are crucial for machine learning.

Probability & Statistics: Hypothesis testing, probability distributions, Bayesian inference, confidence intervals, and statistical significance.

Calculus: Derivatives, integrals, and gradients, especially partial derivatives, which are essential for understanding model optimization.


2. Programming

Python or R: Choose a primary programming language for data science.

Python: Libraries like NumPy, Pandas for data manipulation, and Scikit-Learn for machine learning.

R: Especially popular in academia and finance, with libraries like dplyr and ggplot2 for data manipulation and visualization.


SQL: Master querying and database management, essential for accessing, joining, and filtering large datasets.


3. Data Wrangling & Preprocessing

Data Cleaning: Handle missing values, outliers, duplicates, and data formatting.
Feature Engineering: Create meaningful features, handle categorical variables, and apply transformations (scaling, encoding, etc.).
Exploratory Data Analysis (EDA): Visualize data distributions, correlations, and trends to generate hypotheses and insights.


4. Data Visualization

Python Libraries: Use Matplotlib, Seaborn, and Plotly to visualize data.
Tableau or Power BI: Learn interactive visualization tools for building dashboards.
Storytelling: Develop skills to interpret and present data in a meaningful way to stakeholders.


5. Machine Learning

Supervised Learning: Understand algorithms like Linear Regression, Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, and Support Vector Machines (SVM).
Unsupervised Learning: Study clustering (K-means, DBSCAN) and dimensionality reduction (PCA, t-SNE).
Evaluation Metrics: Understand accuracy, precision, recall, F1-score for classification and RMSE, MAE for regression.


6. Advanced Machine Learning & Deep Learning

Neural Networks: Understand the basics of neural networks and backpropagation.
Deep Learning: Get familiar with Convolutional Neural Networks (CNNs) for image processing and Recurrent Neural Networks (RNNs) for sequential data.
Transfer Learning: Apply pre-trained models for specific use cases.
Frameworks: Use TensorFlow Keras for building deep learning models.


7. Natural Language Processing (NLP)

Text Preprocessing: Tokenization, stemming, lemmatization, stop-word removal.
NLP Techniques: Understand bag-of-words, TF-IDF, and word embeddings (Word2Vec, GloVe).
NLP Models: Work with recurrent neural networks (RNNs), transformers (BERT, GPT) for text classification, sentiment analysis, and translation.


8. Big Data Tools (Optional)

Distributed Data Processing: Learn Hadoop and Spark for handling large datasets. Use Google BigQuery for big data storage and processing.


9. Data Science Workflows & Pipelines (Optional)

ETL & Data Pipelines: Extract, Transform, and Load data using tools like Apache Airflow for automation. Set up reproducible workflows for data transformation, modeling, and monitoring.
Model Deployment: Deploy models in production using Flask, FastAPI, or cloud services (AWS SageMaker, Google AI Platform).


10. Model Validation & Tuning

Cross-Validation: Techniques like K-fold cross-validation to avoid overfitting.
Hyperparameter Tuning: Use Grid Search, Random Search, and Bayesian Optimization to optimize model performance.
Bias-Variance Trade-off: Understand how to balance bias and variance in models for better generalization.


11. Time Series Analysis

Statistical Models: ARIMA, SARIMA, and Holt-Winters for time-series forecasting.
Time Series: Handle seasonality, trends, and lags. Use LSTMs or Prophet for more advanced time-series forecasting.


12. Experimentation & A/B Testing

Experiment Design: Learn how to set up and analyze controlled experiments.
A/B Testing: Statistical techniques for comparing groups & measuring the impact of changes.

ENJOY LEARNING 👍👍

#datascience

Data Structure Cheatsheet ✅

List of most asked Programming Interview Questions.

Are you preparing for a coding interview? This tweet is for you. It contains a list of the most asked interview questions from each topic.

Arrays

- How is an array sorted using quicksort?
- How do you reverse an array?
- How do you remove duplicates from an array?
- How do you find the 2nd largest number in an unsorted integer array?

Linked Lists

- How do you find the length of a linked list?
- How do you reverse a linked list?
- How do you find the third node from the end?
- How are duplicate nodes removed in an unsorted linked list?

Strings

- How do you check if a string contains only digits?
- How can a given string be reversed?
- How do you find the first non-repeated character?
- How do you find duplicate characters in strings?

Binary Trees

- How are all leaves of a binary tree printed?
- How do you check if a tree is a binary search tree?
- How is a binary search tree implemented?
- Find the lowest common ancestor in a binary tree?

Graph

- How to detect a cycle in a directed graph?
- How to detect a cycle in an undirected graph?
- Find the total number of strongly connected components?
- Find whether a path exists between two nodes of a graph?
- Find the minimum number of swaps required to sort an array.

Dynamic Programming

1. Find the longest common subsequence?
2. Find the longest common substring?
3. Coin change problem?
4. Box stacking problem?
5. Count the number of ways to cover a distance?

Sample email template to reach out to HR’s as fresher

Hi Jasneet,

I recently came across your LinkedIn post seeking a React.js developer intern, and I am writing to express my interest in the position at Airtel. As a recent graduate, I am eager to begin my career and am excited about the opportunity.

I am a quick learner and have developed a strong set of dynamic and user-friendly web applications using various technologies, including HTML, CSS, JavaScript, Bootstrap, React.js, Vue.js, PHP, and MySQL. I am also well-versed in creating reusable components, implementing responsive designs, and ensuring cross-browser compatibility.

I am confident that my eagerness to learn and strong work ethic will make me an asset to your team.

I have attached my resume for your review. Thank you for considering my application. I look forward to hearing from you soon.

Thanks!

I hope you will found this helpful 🙂