Coding Interview Resources

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?

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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

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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.

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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👍❤️

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