Data Science Interview Questions With Answers Part-3
21. How do you select important features?
Techniques include statistical tests (chi-square, ANOVA), correlation analysis, feature importance from models (like tree-based algorithms), recursive feature elimination, and regularization methods.
22. What is ensemble learning?
Combining predictions from multiple models (e.g., bagging, boosting, stacking) to improve accuracy, reduce overfitting, and create more robust predictions.
23. Basics of time series analysis.
Analyzing data points collected over time considering trends, seasonality, and noise. Key methods include ARIMA, exponential smoothing, and decomposition.
24. How do you tune hyperparameters?
Using techniques like grid search, random search, or Bayesian optimization with cross-validation to find the best model parameter settings.
25. What are activation functions in neural networks?
Functions that introduce non-linearity into the model, enabling it to learn complex patterns. Examples: sigmoid, ReLU, tanh.
26. Explain transfer learning.
Using a pre-trained model on one task as a starting point for a related task, reducing training time and data needed.
27. How do you deploy machine learning models?
Methods include REST APIs, batch processing, cloud services (AWS, Azure), containerization (Docker), and monitoring after deployment.
28. What are common challenges in big data?
Handling volume, variety, velocity, data quality, storage, processing speed, and ensuring security and privacy.
29. Define ROC curve and AUC score.
ROC curve plots true positive rate vs false positive rate at various thresholds. AUC (Area Under Curve) measures overall model discrimination ability; closer to 1 is better.
30. What is deep learning?
A subset of machine learning using multi-layered neural networks (like CNNs, RNNs) to learn hierarchical feature representations from data, excelling in unstructured data tasks.
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21. How do you select important features?
Techniques include statistical tests (chi-square, ANOVA), correlation analysis, feature importance from models (like tree-based algorithms), recursive feature elimination, and regularization methods.
22. What is ensemble learning?
Combining predictions from multiple models (e.g., bagging, boosting, stacking) to improve accuracy, reduce overfitting, and create more robust predictions.
23. Basics of time series analysis.
Analyzing data points collected over time considering trends, seasonality, and noise. Key methods include ARIMA, exponential smoothing, and decomposition.
24. How do you tune hyperparameters?
Using techniques like grid search, random search, or Bayesian optimization with cross-validation to find the best model parameter settings.
25. What are activation functions in neural networks?
Functions that introduce non-linearity into the model, enabling it to learn complex patterns. Examples: sigmoid, ReLU, tanh.
26. Explain transfer learning.
Using a pre-trained model on one task as a starting point for a related task, reducing training time and data needed.
27. How do you deploy machine learning models?
Methods include REST APIs, batch processing, cloud services (AWS, Azure), containerization (Docker), and monitoring after deployment.
28. What are common challenges in big data?
Handling volume, variety, velocity, data quality, storage, processing speed, and ensuring security and privacy.
29. Define ROC curve and AUC score.
ROC curve plots true positive rate vs false positive rate at various thresholds. AUC (Area Under Curve) measures overall model discrimination ability; closer to 1 is better.
30. What is deep learning?
A subset of machine learning using multi-layered neural networks (like CNNs, RNNs) to learn hierarchical feature representations from data, excelling in unstructured data tasks.
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Data Science Interview Questions Part 4:
31. What is reinforcement learning?
A type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize cumulative rewards through trial and error.
32. What tools and libraries do you use?
Commonly used tools: Python, R, Jupyter Notebooks, SQL, Excel. Libraries: Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, Matplotlib, Seaborn.
33. How do you interpret model results for non-technical audiences?
Use simple language, visualize key insights (charts, dashboards), focus on business impact, avoid jargon, and use analogies or stories.
34. What is dimensionality reduction?
Techniques like PCA or t-SNE to reduce the number of features while preserving essential information, improving model efficiency and visualization.
35. Handling categorical variables in machine learning.
Use encoding methods like one-hot encoding, label encoding, target encoding depending on model requirements and feature cardinality.
36. What is exploratory data analysis (EDA)?
The process of summarizing main characteristics of data often using visual methods to understand patterns, spot anomalies, and test hypotheses.
37. Explain t-test and chi-square test.
โฆ t-test compares means between two groups to see if they are statistically different.
โฆ Chi-square test assesses relationships between categorical variables.
38. How do you ensure fairness and avoid bias in models?
Audit data for bias, use balanced training datasets, apply fairness-aware algorithms, monitor model outcomes, and include diverse perspectives in evaluation.
39. Describe a complex data problem you solved.
(Your personal story here, describing the problem, approach, tools used, and impact.)
40. How do you stay updated with new data science trends?
Follow blogs, research papers, online courses, attend webinars, participate in communities (Kaggle, Stack Overflow), and read newsletters.
Data science interview questions: https://t.iss.one/datasciencefun/3668
Double Tap โฅ๏ธ If This Helped You
31. What is reinforcement learning?
A type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize cumulative rewards through trial and error.
32. What tools and libraries do you use?
Commonly used tools: Python, R, Jupyter Notebooks, SQL, Excel. Libraries: Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, Matplotlib, Seaborn.
33. How do you interpret model results for non-technical audiences?
Use simple language, visualize key insights (charts, dashboards), focus on business impact, avoid jargon, and use analogies or stories.
34. What is dimensionality reduction?
Techniques like PCA or t-SNE to reduce the number of features while preserving essential information, improving model efficiency and visualization.
35. Handling categorical variables in machine learning.
Use encoding methods like one-hot encoding, label encoding, target encoding depending on model requirements and feature cardinality.
36. What is exploratory data analysis (EDA)?
The process of summarizing main characteristics of data often using visual methods to understand patterns, spot anomalies, and test hypotheses.
37. Explain t-test and chi-square test.
โฆ t-test compares means between two groups to see if they are statistically different.
โฆ Chi-square test assesses relationships between categorical variables.
38. How do you ensure fairness and avoid bias in models?
Audit data for bias, use balanced training datasets, apply fairness-aware algorithms, monitor model outcomes, and include diverse perspectives in evaluation.
39. Describe a complex data problem you solved.
(Your personal story here, describing the problem, approach, tools used, and impact.)
40. How do you stay updated with new data science trends?
Follow blogs, research papers, online courses, attend webinars, participate in communities (Kaggle, Stack Overflow), and read newsletters.
Data science interview questions: https://t.iss.one/datasciencefun/3668
Double Tap โฅ๏ธ If This Helped You
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Follow the UNESCOโAl Fozan International Prize for inspiring stories, breakthroughs, and opportunities in STEM (Science, Technology, Engineering, and Mathematics).
๐ฒ Follow us here:
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๐ฏ ๐๐ถ๐ฟ๐ฏ๐ป๐ฏ ๐ข๐ฝ๐ฒ๐ป ๐๐ฎ๐๐ฎ ๐
https://www.kaggle.com/datasets/arianazmoudeh/airbnbopendata
๐กThis dataset describes the listing activity of homestays in New York City
๐ฏ ๐ง๐ผ๐ฝ ๐ฆ๐ฝ๐ผ๐๐ถ๐ณ๐ ๐๐ผ๐ป๐ด๐ ๐ณ๐ฟ๐ผ๐บ ๐ฎ๐ฌ๐ญ๐ฌ-๐ฎ๐ฌ๐ญ๐ต ๐ต
https://www.kaggle.com/datasets/leonardopena/top-spotify-songs-from-20102019-by-year
๐ฏ๐ช๐ฎ๐น๐บ๐ฎ๐ฟ๐ ๐ฆ๐๐ผ๐ฟ๐ฒ ๐ฆ๐ฎ๐น๐ฒ๐ ๐๐ผ๐ฟ๐ฒ๐ฐ๐ฎ๐๐๐ถ๐ป๐ด ๐
https://www.kaggle.com/c/walmart-recruiting-store-sales-forecasting/data
๐กUse historical markdown data to predict store sales
๐ฏ ๐ก๐ฒ๐๐ณ๐น๐ถ๐ ๐ ๐ผ๐๐ถ๐ฒ๐ ๐ฎ๐ป๐ฑ ๐ง๐ฉ ๐ฆ๐ต๐ผ๐๐ ๐บ
https://www.kaggle.com/datasets/shivamb/netflix-shows
๐กListings of movies and tv shows on Netflix - Regularly Updated
๐ฏ๐๐ถ๐ป๐ธ๐ฒ๐ฑ๐๐ป ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ ๐ท๐ผ๐ฏ๐ ๐น๐ถ๐๐๐ถ๐ป๐ด๐ ๐ผ
https://www.kaggle.com/datasets/cedricaubin/linkedin-data-analyst-jobs-listings
๐กMore than 8400 rows of data analyst jobs from USA, Canada and Africa.
ENJOY LEARNING ๐๐
๐ฏ ๐๐ถ๐ฟ๐ฏ๐ป๐ฏ ๐ข๐ฝ๐ฒ๐ป ๐๐ฎ๐๐ฎ ๐
https://www.kaggle.com/datasets/arianazmoudeh/airbnbopendata
๐กThis dataset describes the listing activity of homestays in New York City
๐ฏ ๐ง๐ผ๐ฝ ๐ฆ๐ฝ๐ผ๐๐ถ๐ณ๐ ๐๐ผ๐ป๐ด๐ ๐ณ๐ฟ๐ผ๐บ ๐ฎ๐ฌ๐ญ๐ฌ-๐ฎ๐ฌ๐ญ๐ต ๐ต
https://www.kaggle.com/datasets/leonardopena/top-spotify-songs-from-20102019-by-year
๐ฏ๐ช๐ฎ๐น๐บ๐ฎ๐ฟ๐ ๐ฆ๐๐ผ๐ฟ๐ฒ ๐ฆ๐ฎ๐น๐ฒ๐ ๐๐ผ๐ฟ๐ฒ๐ฐ๐ฎ๐๐๐ถ๐ป๐ด ๐
https://www.kaggle.com/c/walmart-recruiting-store-sales-forecasting/data
๐กUse historical markdown data to predict store sales
๐ฏ ๐ก๐ฒ๐๐ณ๐น๐ถ๐ ๐ ๐ผ๐๐ถ๐ฒ๐ ๐ฎ๐ป๐ฑ ๐ง๐ฉ ๐ฆ๐ต๐ผ๐๐ ๐บ
https://www.kaggle.com/datasets/shivamb/netflix-shows
๐กListings of movies and tv shows on Netflix - Regularly Updated
๐ฏ๐๐ถ๐ป๐ธ๐ฒ๐ฑ๐๐ป ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ ๐ท๐ผ๐ฏ๐ ๐น๐ถ๐๐๐ถ๐ป๐ด๐ ๐ผ
https://www.kaggle.com/datasets/cedricaubin/linkedin-data-analyst-jobs-listings
๐กMore than 8400 rows of data analyst jobs from USA, Canada and Africa.
ENJOY LEARNING ๐๐
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