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I applied via Referral and was interviewed in Jan 2023. There were 2 interview rounds.
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Use SQL query with ORDER BY and LIMIT to find the second highest salary.
Use SELECT statement to retrieve salary column from the table.
Use ORDER BY clause to sort the salaries in descending order.
Use LIMIT 1,1 to get the second highest salary.
Use SQL query with GROUP BY and window function to check for duplicate rows.
Use GROUP BY to group rows with same values together
Use COUNT() function to count the number of occurrences of each group
Use window function like ROW_NUMBER() to assign a unique number to each row within a group
I applied via Referral and was interviewed in Sep 2024. There were 2 interview rounds.
Node.js is a runtime environment that allows you to run JavaScript on the server side.
Node.js is built on Chrome's V8 JavaScript engine.
It uses an event-driven, non-blocking I/O model that makes it lightweight and efficient.
Node.js is commonly used for building server-side applications, APIs, and real-time applications.
It has a large ecosystem of libraries and frameworks, such as Express.js and Socket.io.
In Node.js, timers can be implemented using functions like setTimeout() and setInterval().
Use setTimeout() to execute a function once after a specified delay
Use setInterval() to execute a function repeatedly at a specified interval
Clear a timer using clearTimeout() or clearInterval()
Example: setTimeout(() => { console.log('Timer executed!'); }, 2000);
I applied via Referral and was interviewed in Sep 2023. There were 4 interview rounds.
Technical and coding
I was interviewed in Feb 2025.
Data analysis , SQL, python, ML
Excellent performance
I applied via LinkedIn and was interviewed before May 2021. There were 4 interview rounds.
The aptitude test is pretty basic and logical
I applied via LinkedIn and was interviewed before Oct 2023. There was 1 interview round.
I applied via Approached by Company and was interviewed in Sep 2024. There were 3 interview rounds.
Based upon the profile vary, time series related problems, power BI
I applied via Indeed and was interviewed before Apr 2021. There were 5 interview rounds.
There are several types of ML algorithms, including supervised learning, unsupervised learning, and reinforcement learning.
Supervised learning: algorithms learn from labeled data to make predictions or classifications (e.g., linear regression, decision trees)
Unsupervised learning: algorithms find patterns or relationships in unlabeled data (e.g., clustering, dimensionality reduction)
Reinforcement learning: algorithms l...
Time series classification involves using machine learning algorithms to classify time series data based on patterns and trends.
Preprocess the time series data by removing noise and outliers
Extract features from the time series data using techniques such as Fourier transforms or wavelet transforms
Train a machine learning algorithm such as a decision tree or neural network on the extracted features
Evaluate the performan...
PCA stands for Principal Component Analysis. It is a statistical technique used for dimensionality reduction.
PCA is used to reduce the number of variables in a dataset while retaining the maximum amount of information.
It is commonly used in data preprocessing and exploratory data analysis.
PCA is also used in image processing, speech recognition, and finance.
It works by transforming the original variables into a new set...
It is a typical Data Science assignment. We have to answer few questions asked in the assignment like why do you choose the features? or where can you use this model?
The thought process for choosing the model involved considering the problem requirements, available data, and the desired outcome.
Identified the problem requirements and objectives
Explored the available data and its quality
Considered the nature of the problem (classification, regression, etc.)
Evaluated different models suitable for the problem
Analyzed the strengths and weaknesses of each model
Selected the model that be...
EDA involved exploratory analysis of data to identify patterns and insights. Features included demographic and behavioral data. Metrics used were accuracy, precision, recall, and F1 score.
EDA involved data cleaning, visualization, and statistical analysis
Features included age, gender, income, education, and purchase history
Metrics used were accuracy, precision, recall, and F1 score to evaluate model performance
Explorat...
I expect a competitive salary based on my experience, skills, and the market rate for data scientists.
I have researched the average salary range for data scientists in the industry.
I have considered my level of experience and expertise in the field.
I am open to discussing the salary package based on the overall compensation package offered by the company.
I value fair compensation that aligns with the responsibilities a
I worked as a Data Scientist at XYZ company.
Developed machine learning models to predict customer churn.
Analyzed large datasets to identify patterns and trends.
Collaborated with cross-functional teams to develop data-driven solutions.
Implemented data visualization techniques to communicate insights to stakeholders.
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