Data science interview questions 👇

𝗦𝗤𝗟
- How do you write a query to fetch the top 5 highest salaries in each department?
- What’s the difference between the HAVING and WHERE clauses in SQL?
- How do you handle NULL values in SQL, and how do they affect aggregate functions?

𝗣𝘆𝘁𝗵𝗼𝗻
- How do you handle large datasets in Python, and which libraries would you use for performance?
- What are context managers in Python, and how do they help with resource management?
- How do you manage and log errors in Python-based ETL pipelines?

𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴
- Explain the difference between bias and variance in a machine learning model. How do you balance them?
- What is cross-validation, and how does it improve the performance of machine learning models?
- How do you deal with class imbalance in classification tasks, and what techniques would you apply?

𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴
- What is the vanishing gradient problem in deep learning, and how can it be mitigated?
- Explain how a convolutional neural network (CNN) works and when you would use it.
- What is dropout in neural networks, and how does it help prevent overfitting?

𝗗𝗮𝘁𝗮 𝗪𝗿𝗮𝗻𝗴𝗹𝗶𝗻𝗴
- How would you handle outliers in a dataset, and when is it appropriate to remove or keep them?
- Explain how to merge two datasets in Python, and how would you handle duplicate or missing entries in the merged data?
- What is data normalization, and when should you apply it to your dataset?

𝗗𝗮𝘁𝗮 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 - 𝗧𝗮𝗯𝗹𝗲𝗮𝘂
- How do you create a dual-axis chart in Tableau, and when would you use it?
- How would you filter data in Tableau to create a dynamic dashboard that updates based on user input?
- What are calculated fields in Tableau, and how would you use them to create a custom metric?

#datascience #interview