Coding Interview Resources

Here is an A-Z list of essential programming terms: 1. Array: A data structure that stores a collection of elements of the same type in contiguous memory locations. 2. Boolean: A data type that represents true or false values. 3. Conditional Statement: A statement that executes different code based on a condition. 4. Debugging: The process of identifying and fixing errors or bugs in a program. 5. Exception: An event that occurs during the execution of a program that disrupts the normal flow of instructions. 6. Function: A block of code that performs a specific task and can be called multiple times in a program. 7. GUI (Graphical User Interface): A visual way for users to interact with a computer program using graphical elements like windows, buttons, and menus. 8. HTML (Hypertext Markup Language): The standard markup language used to create web pages. 9. Integer: A data type that represents whole numbers without any fractional part. 10. JSON (JavaScript Object Notation): A lightweight data interchange format commonly used for transmitting data between a server and a web application. 11. Loop: A programming construct that allows repeating a block of code multiple times. 12. Method: A function that is associated with an object in object-oriented programming. 13. Null: A special value that represents the absence of a value. 14. Object-Oriented Programming (OOP): A programming paradigm based on the concept of "objects" that encapsulate data and behavior. 15. Pointer: A variable that stores the memory address of another variable. 16. Queue: A data structure that follows the First-In-First-Out (FIFO) principle. 17. Recursion: A programming technique where a function calls itself to solve a problem. 18. String: A data type that represents a sequence of characters. 19. Tuple: An ordered collection of elements, similar to an array but immutable. 20. Variable: A named storage location in memory that holds a value. 21. While Loop: A loop that repeatedly executes a block of code as long as a specified condition is true. Best Programming Resources: https://topmate.io/coding/898340 Join for more: https://t.me/programming_guide ENJOY LEARNING 👍👍

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Some important questions to crack data science interview Q. Describe how Gradient Boosting works. A. Gradient boosting is a type of machine learning boosting. It relies on the intuition that the best possible next model, when combined with previous models, minimizes the overall prediction error. If a small change in the prediction for a case causes no change in error, then next target outcome of the case is zero. Gradient boosting produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees. Q. Describe the decision tree model. A. Decision Trees are a type of Supervised Machine Learning where the data is continuously split according to a certain parameter. The leaves are the decisions or the final outcomes. A decision tree is a machine learning algorithm that partitions the data into subsets. Q. What is a neural network? A. Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling or clustering raw input. They, also known as Artificial Neural Networks, are the subset of Deep Learning. Q. Explain the Bias-Variance Tradeoff A. The bias–variance tradeoff is the property of a model that the variance of the parameter estimated across samples can be reduced by increasing the bias in the estimated parameters. Q. What’s the difference between L1 and L2 regularization? A. The main intuitive difference between the L1 and L2 regularization is that L1 regularization tries to estimate the median of the data while the L2 regularization tries to estimate the mean of the data to avoid overfitting. That value will also be the median of the data distribution mathematically. ENJOY LEARNING 👍👍

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Master Power BI with this Cheat Sheet🔥 If you're preparing for a Power BI interview, this cheat sheet covers the key concepts and DAX commands you'll need. Bookmark it for last-minute revision! 📝 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗕𝗮𝘀𝗶𝗰𝘀: DAX Functions: - SUMX: Sum of values based on a condition. - FILTER: Filter data based on a given condition. - RELATED: Retrieve a related column from another table. - CALCULATE: Perform dynamic calculations. - EARLIER: Access a column from a higher context. - CROSSJOIN: Create a Cartesian product of two tables. - UNION: Combine the results from multiple tables. - RANKX: Rank data within a column. - DISTINCT: Filter unique rows. Data Modeling: - Relationships: Create, manage, and modify relationships. - Hierarchies: Build time-based hierarchies (e.g., Date, Month, Year). - Calculated Columns: Create calculated columns to extend data. - Measures: Write powerful measures to analyze data effectively. Data Visualization: - Charts: Bar charts, line charts, pie charts, and more. - Table & Matrix: Display tabular data and matrix visuals. - Slicers: Create interactive filters. - Tooltips: Enhance visual interactivity with tooltips. - Map: Display geographical data effectively. ✨ 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗧𝗶𝗽𝘀: ✅ Use DAX for efficient data analysis. ✅ Optimize data models for performance. ✅ Utilize drill-through and drill-down for deeper insights. ✅ Leverage bookmarks for enhanced navigation. ✅ Annotate your reports with comments for clarity. Like this post if you need more content like this 👍❤️

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✅ Top 10 Coding Interview Questions (2025) 💼👨‍💻 1️⃣ Subarray with given sum  Find continuous subarray that sums to a target value. 2️⃣ Count triplets with given sum  Find triplets in array whose sum equals a target. 3️⃣ Kadane’s Algorithm  Find maximum sum subarray in O(n). 4️⃣ Missing number in array  Find the one number missing from 1 to N. 5️⃣ Sort an array of 0s, 1s and 2s  Dutch National Flag problem — sort in a single scan. 6️⃣ Depth First Traversal (Graph)  Traverse graph nodes using stack or recursion. 7️⃣ Topological Sort  Order nodes in a Directed Acyclic Graph (DAG). 8️⃣ Activity Selection (Greedy)  Select max non-overlapping activities. 9️⃣ Longest Increasing Subsequence (DP)  Find length of longest increasing subsequence in array. 🔟 N-Queen Problem (Backtracking)  Place N queens on an N×N board so none attack each other. 💬 Tap ❤️ for more

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1. What is the AdaBoost Algorithm? AdaBoost also called Adaptive Boosting is a technique in Machine Learning used as an Ensemble Method. The most common algorithm used with AdaBoost is decision trees with one level that means with Decision trees with only 1 split. These trees are also called Decision Stumps. What this algorithm does is that it builds a model and gives equal weights to all the data points. It then assigns higher weights to points that are wrongly classified. Now all the points which have higher weights are given more importance in the next model. It will keep training models until and unless a lower error is received. 2. What is the Sliding Window method for Time Series Forecasting? Time series can be phrased as supervised learning. Given a sequence of numbers for a time series dataset, we can restructure the data to look like a supervised learning problem. In the sliding window method, the previous time steps can be used as input variables, and the next time steps can be used as the output variable. In statistics and time series analysis, this is called a lag or lag method. The number of previous time steps is called the window width or size of the lag. This sliding window is the basis for how we can turn any time series dataset into a supervised learning problem. 3. What do you understand by sub-queries in SQL? A subquery is a query inside another query where a query is defined to retrieve data or information back from the database. In a subquery, the outer query is called as the main query whereas the inner query is called subquery. Subqueries are always executed first and the result of the subquery is passed on to the main query. It can be nested inside a SELECT, UPDATE or any other query. A subquery can also use any comparison operators such as >,< or =. 4. Explain the Difference Between Tableau Worksheet, Dashboard, Story, and Workbook? Tableau uses a workbook and sheet file structure, much like Microsoft Excel. A workbook contains sheets, which can be a worksheet, dashboard, or a story. A worksheet contains a single view along with shelves, legends, and the Data pane. A dashboard is a collection of views from multiple worksheets. A story contains a sequence of worksheets or dashboards that work together to convey information. 5. How is a Random Forest related to Decision Trees? Random forest is an ensemble learning method that works by constructing a multitude of decision trees. A random forest can be constructed for both classification and regression tasks. Random forest outperforms decision trees, and it also does not have the habit of overfitting the data as decision trees do. A decision tree trained on a specific dataset will become very deep and cause overfitting. To create a random forest, decision trees can be trained on different subsets of the training dataset, and then the different decision trees can be averaged with the goal of decreasing the variance. 6. What are some disadvantages of using Naive Bayes Algorithm? Some disadvantages of using Naive Bayes Algorithm are: It relies on a very big assumption that the independent variables are not related to each other. It is generally not suitable for datasets with large numbers of numerical attributes. It has been observed that if a rare case is not in the training dataset but is in the testing dataset, then it will most definitely be wrong.