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I applied via LinkedIn and was interviewed in Nov 2024. There were 2 interview rounds.
An extremely challenging assignment was given with a very tight deadline. After submitting the assignment, an interview was scheduled during which numerous questions were asked about it, along with requests for complex modifications. Overall, the process was quite difficult.
I applied via Internshala and was interviewed in Jun 2024. There were 3 interview rounds.
Basic simple easy aptitude questions
They gave 6 days for a Web Scrapping-NLP based assignment project to submit
Handling imbalanced data involves techniques like resampling, using different algorithms, and adjusting class weights.
Use resampling techniques like oversampling or undersampling to balance the dataset
Utilize algorithms that are robust to imbalanced data, such as Random Forest, XGBoost, or SVM
Adjust class weights in the model to give more importance to minority class
To maintain data integrity and generalization, use techniques like data cleaning, normalization, and feature engineering.
Perform data cleaning to remove errors, duplicates, and inconsistencies.
Normalize data to ensure consistency and comparability.
Utilize feature engineering to create new features or transform existing ones for better model performance.
I have used various software for data analysis including Python, R, SQL, Tableau, and Excel.
Python - for data cleaning, manipulation, and modeling
R - for statistical analysis and visualization
SQL - for querying databases
Tableau - for creating interactive visualizations
Excel - for basic data analysis and visualization
Web scrapping and sentiment analysis
I approach assignments by breaking them down into smaller tasks, setting deadlines, and regularly checking progress.
Break down the assignment into smaller tasks to make it more manageable
Set deadlines for each task to stay on track
Regularly check progress and make adjustments as needed
Seek feedback from peers or supervisors to improve the quality of work
posted on 29 Feb 2024
I applied via Approached by Company and was interviewed before Feb 2023. There were 3 interview rounds.
Overfitting occurs when a machine learning model learns the training data too well, including noise and outliers, leading to poor generalization on new data.
Overfitting happens when a model is too complex and captures noise in the training data.
It leads to poor performance on unseen data as the model fails to generalize well.
Techniques to prevent overfitting include cross-validation, regularization, and early stopping.
...
Overfitting occurs when a model learns the details and noise in the training data to the extent that it negatively impacts the model's performance on new data.
Overfitting happens when a model is too complex and captures noise in the training data.
It leads to poor generalization and high accuracy on training data but low accuracy on new data.
Techniques to prevent overfitting include cross-validation, regularization, and...
I applied via LinkedIn and was interviewed in Jul 2024. There was 1 interview round.
I have a strong background in data analysis, machine learning, and problem-solving skills that make me a valuable asset to your team.
Extensive experience in data analysis and machine learning techniques
Proven track record of solving complex problems using data-driven approaches
Strong communication and collaboration skills demonstrated through team projects and internships
As a Data Science Intern, I should contribute by analyzing data, developing models, and providing insights to drive decision-making.
Analyze data to identify trends and patterns
Develop predictive models to forecast outcomes
Provide actionable insights to stakeholders
Contribute to data-driven decision-making processes
I appeared for an interview before Mar 2024.
I am proficient in various data analysis tools, including Excel, SQL, Python, and visualization software like Tableau.
Excel: Advanced functions, pivot tables, and data visualization.
SQL: Writing complex queries for data extraction and manipulation.
Python: Utilizing libraries like Pandas and NumPy for data analysis.
Tableau: Creating interactive dashboards for data visualization.
R: Statistical analysis and data visualiza
A pivot table in Excel is a data summarization tool that allows you to reorganize and summarize selected columns and rows of data.
Allows users to summarize and analyze large datasets
Can easily reorganize data by dragging and dropping fields
Provides options to calculate sums, averages, counts, etc. for data
Helps in creating interactive reports and charts
Useful for identifying trends and patterns in data
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.
based on 5 interviews
Interview experience
based on 11 reviews
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