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I applied via LinkedIn and was interviewed before Sep 2023. There were 2 interview rounds.
Excel workbook complete
The gender equation refers to the balance of power, opportunities, and representation between different genders in society.
The gender equation is a complex issue influenced by cultural, social, and economic factors.
It involves examining disparities in areas such as pay, leadership positions, and access to education and healthcare.
Efforts to address the gender equation include promoting gender equality, diversity, and i...
The graph shows a steady increase in sales over the past year.
The graph indicates a positive trend in sales performance.
There is a clear upward trajectory in the sales data.
The sales figures have been consistently improving over time.
The company's revenue appears to be growing steadily.
There may have been successful marketing campaigns or product launches contributing to the sales growth.
Top trending discussions
I applied via LinkedIn and was interviewed in Dec 2024. There were 2 interview rounds.
Based on my CV, they assigned me a task related to data migration.
I appeared for an interview in May 2025, where I was asked the following questions.
Handling missing data involves identifying, assessing, and applying appropriate techniques to manage gaps in datasets.
Identify missing data: Use methods like 'isnull()' in Python to find missing values.
Assess the impact: Determine how missing data affects your analysis and results.
Imputation: Replace missing values with mean, median, or mode. For example, use the median for skewed distributions.
Remove missing data: If ...
Inner join returns matching records from both tables, while left join returns all records from the left table and matching from the right.
Inner Join: Combines rows from two tables where there is a match in both tables.
Left Join: Returns all rows from the left table and matched rows from the right table; unmatched rows from the right will show NULL.
Example of Inner Join: SELECT * FROM TableA INNER JOIN TableB ON TableA....
Choosing the right visualization depends on data type, audience, and insights needed.
Use bar charts for categorical comparisons (e.g., sales by region).
Line charts are ideal for trends over time (e.g., monthly revenue).
Pie charts can show proportions but are less effective for many categories.
Scatter plots help identify relationships between two variables (e.g., age vs. income).
Heatmaps visualize data density or correl...
I applied via Naukri.com and was interviewed before Aug 2023. There was 1 interview round.
Coding test, technical round, HR round
posted on 4 May 2019
I applied via Naukri.com and was interviewed in Oct 2018. There were 3 interview rounds.
This question involves creating a specific pattern using loops and conditional statements in programming.
Identify the desired pattern (e.g., asterisks, numbers).
Use nested loops: outer loop for rows, inner loop for columns.
Control the output format with conditional statements.
Example: For a pyramid pattern, increase spaces and asterisks in each row.
I appeared for an interview in Mar 2025, where I was asked the following questions.
In five years, I envision myself as a skilled professional, contributing significantly to my field and leading innovative projects.
I aim to have advanced my skills through continuous learning and professional development, such as obtaining relevant certifications.
I see myself taking on leadership roles, perhaps managing a team or leading projects that drive impactful results.
I hope to be involved in innovative projects...
I tend to be overly critical of my work, which can slow down my progress but helps me maintain high standards.
I often spend extra time reviewing my work to ensure it's perfect, which can lead to missed deadlines.
For example, during a group project, I focused too much on perfecting my section, causing delays for the team.
I've learned to set time limits for revisions to balance quality and efficiency.
I also seek feedback...
I appeared for an interview in Mar 2025, where I was asked the following questions.
Developed a task management application using the MERN stack to streamline team collaboration and project tracking.
Utilized MongoDB for database management, storing user tasks and project details.
Implemented Express.js to create RESTful APIs for task CRUD operations.
Used React.js for building a responsive user interface, allowing users to add, edit, and delete tasks seamlessly.
Incorporated Node.js for server-side logic...
Managing routes in a MERN stack involves defining API endpoints and client-side routes for seamless navigation and data handling.
Use Express.js for backend routing: Define routes in a separate file, e.g., 'routes/user.js'.
Implement RESTful API conventions: Use GET, POST, PUT, DELETE methods for CRUD operations.
Utilize React Router for client-side routing: Set up routes in 'App.js' using <BrowserRouter> and <Ro...
I appeared for an interview in Feb 2025, where I was asked the following questions.
React hooks are functions that let you use state and lifecycle features in functional components.
useState: Allows you to add state to functional components. Example: const [count, setCount] = useState(0);
useEffect: Lets you perform side effects in function components. Example: useEffect(() => { document.title = `Count: ${count}`; }, [count]);
useContext: Provides a way to pass data through the component tree without ...
I appeared for an interview in Oct 2024, where I was asked the following questions.
Supervised learning is a machine learning approach where a model is trained on labeled data to make predictions or classifications.
Involves training a model on a dataset with input-output pairs.
Common algorithms include linear regression, decision trees, and support vector machines.
Used for tasks like spam detection (classifying emails) and image recognition (identifying objects in images).
The model learns from the lab...
MSE, or Mean Squared Error, is a common metric used to measure the average squared difference between predicted and actual values.
MSE is calculated as the average of the squares of the errors: MSE = (1/n) * Σ(actual - predicted)².
A lower MSE indicates a better fit of the model to the data.
For example, if actual values are [3, -0.5, 2] and predicted values are [2.5, 0.0, 2], MSE = (1/3) * ((3-2.5)² + (-0.5-0)² + (2-2)²)...
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