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Data Science Interview Questions with Answers Part-2
11. What is the difference between mean, median, and mode?
The mean is the average value calculated by dividing the sum of all values by the total count. The median is the middle value when data is sorted. The mode is the most frequently occurring value. Mean is sensitive to extreme values, while median handles outliers better. Mode is useful for categorical or repetitive data.
12. What is standard deviation and variance?
Variance measures how far data points spread from the mean by averaging squared deviations. Standard deviation is the square root of variance and is expressed in the same unit as the data. A high standard deviation shows high variability, while a low value shows data clustered around the mean.
13. What is probability distribution?
A probability distribution describes how likely different outcomes are for a random variable. It shows the relationship between values and their probabilities. Common examples include normal, binomial, and Poisson distributions. Distributions help model uncertainty and make statistical inferences.
14. What is normal distribution and where is it used?
Normal distribution is a symmetric, bell-shaped distribution where mean, median, and mode are equal. Most values lie near the center and fewer at the extremes. It is widely used in statistics, hypothesis testing, quality control, and natural phenomena such as heights, errors, and measurement noise.
15. What is skewness and kurtosis?
Skewness measures the asymmetry of a distribution. Positive skew has a long right tail, negative skew has a long left tail. Kurtosis measures how heavy the tails are compared to a normal distribution. High kurtosis indicates more extreme values, while low kurtosis indicates flatter distributions.
16. What is correlation vs causation?
Correlation measures the strength and direction of a relationship between two variables. Causation means one variable directly affects another. Correlation does not imply causation because two variables may move together due to coincidence or a third factor. Decisions based only on correlation can be misleading.
17. What is hypothesis testing?
Hypothesis testing is a statistical method used to make decisions using data. It starts with a null hypothesis that assumes no effect or difference. Data is analyzed to determine whether there is enough evidence to reject the null hypothesis in favor of an alternative hypothesis.
18. What are Type I and Type II errors?
A Type I error occurs when a true null hypothesis is rejected, also called a false positive. A Type II error occurs when a false null hypothesis is not rejected, also called a false negative. Reducing one often increases the other, so balance depends on business risk.
19. What is p-value?
A p-value measures the probability of observing results as extreme as the sample data assuming the null hypothesis is true. A small p-value indicates strong evidence against the null hypothesis. It helps decide whether results are statistically significant.
20. What is confidence interval?
A confidence interval provides a range of values within which the true population parameter is expected to lie with a certain level of confidence. For example, a 95 percent confidence interval means the method captures the true value in 95 out of 100 similar samples.
Double Tap โฅ๏ธ For Part-3
11. What is the difference between mean, median, and mode?
The mean is the average value calculated by dividing the sum of all values by the total count. The median is the middle value when data is sorted. The mode is the most frequently occurring value. Mean is sensitive to extreme values, while median handles outliers better. Mode is useful for categorical or repetitive data.
12. What is standard deviation and variance?
Variance measures how far data points spread from the mean by averaging squared deviations. Standard deviation is the square root of variance and is expressed in the same unit as the data. A high standard deviation shows high variability, while a low value shows data clustered around the mean.
13. What is probability distribution?
A probability distribution describes how likely different outcomes are for a random variable. It shows the relationship between values and their probabilities. Common examples include normal, binomial, and Poisson distributions. Distributions help model uncertainty and make statistical inferences.
14. What is normal distribution and where is it used?
Normal distribution is a symmetric, bell-shaped distribution where mean, median, and mode are equal. Most values lie near the center and fewer at the extremes. It is widely used in statistics, hypothesis testing, quality control, and natural phenomena such as heights, errors, and measurement noise.
15. What is skewness and kurtosis?
Skewness measures the asymmetry of a distribution. Positive skew has a long right tail, negative skew has a long left tail. Kurtosis measures how heavy the tails are compared to a normal distribution. High kurtosis indicates more extreme values, while low kurtosis indicates flatter distributions.
16. What is correlation vs causation?
Correlation measures the strength and direction of a relationship between two variables. Causation means one variable directly affects another. Correlation does not imply causation because two variables may move together due to coincidence or a third factor. Decisions based only on correlation can be misleading.
17. What is hypothesis testing?
Hypothesis testing is a statistical method used to make decisions using data. It starts with a null hypothesis that assumes no effect or difference. Data is analyzed to determine whether there is enough evidence to reject the null hypothesis in favor of an alternative hypothesis.
18. What are Type I and Type II errors?
A Type I error occurs when a true null hypothesis is rejected, also called a false positive. A Type II error occurs when a false null hypothesis is not rejected, also called a false negative. Reducing one often increases the other, so balance depends on business risk.
19. What is p-value?
A p-value measures the probability of observing results as extreme as the sample data assuming the null hypothesis is true. A small p-value indicates strong evidence against the null hypothesis. It helps decide whether results are statistically significant.
20. What is confidence interval?
A confidence interval provides a range of values within which the true population parameter is expected to lie with a certain level of confidence. For example, a 95 percent confidence interval means the method captures the true value in 95 out of 100 similar samples.
Double Tap โฅ๏ธ For Part-3
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Data Science Interview Questions with Answers Part-3
21. How do you handle missing values?
Missing values are handled based on the reason and the impact on the problem. You first check whether data is missing at random or systematic. Common approaches include removing rows or columns if the missing percentage is small, imputing with mean, median, or mode for numerical data, using a separate category for missing values in categorical data, or applying model-based imputation when data loss affects predictions.
22. How do you treat outliers?
Outliers are treated after understanding their cause. If they result from data entry errors, they are corrected or removed. If they represent real but rare events, they are kept. Treatment methods include capping values, applying transformations like log scaling, or using robust models that handle outliers naturally. Blind removal is avoided.
23. What is data normalization and standardization?
Normalization rescales data to a fixed range, usually between zero and one. Standardization rescales data to have a mean of zero and a standard deviation of one. Both techniques ensure features contribute equally to model learning, especially for distance-based and gradient-based algorithms.
24. When do you use Min-Max scaling vs Z-score?
Min-Max scaling is used when data has a fixed range and no extreme outliers, such as image pixel values. Z-score scaling is used when data follows a normal distribution or contains outliers. Many machine learning models perform better with standardized data.
25. How do you handle imbalanced datasets?
Imbalanced datasets are handled by resampling techniques like oversampling the minority class or undersampling the majority class. You can also use algorithms that support class weighting or focus on metrics like recall, precision, and AUC instead of accuracy. The choice depends on business cost of false positives and false negatives.
26. What is one-hot encoding?
One-hot encoding converts categorical variables into binary columns. Each category becomes a separate column with values zero or one. This avoids ordinal assumptions and works well with most machine learning algorithms, especially linear and tree-based models.
27. What is label encoding?
Label encoding assigns a unique numeric value to each category. It is suitable when categories have an inherent order or when using tree-based models that handle ordinal values well. It is avoided for nominal data in linear models due to unintended ranking.
28. How do you detect data leakage?
Data leakage is detected by checking whether future or target-related information is present in training features. You validate time-based splits, review feature creation logic, and ensure preprocessing steps are applied separately on training and test data. Sudden high model accuracy is often a red flag.
29. What is duplicate data and how do you handle it?
Duplicate data refers to repeated records representing the same entity or event. Duplicates are identified using unique identifiers or key feature combinations. They are removed or merged based on business logic to prevent bias, inflated metrics, and incorrect model learning.
30. How do you validate data quality?
Data quality is validated by checking completeness, consistency, accuracy, and validity. This includes range checks, schema validation, distribution analysis, and reconciliation with source systems. Automated checks and dashboards are often used to monitor quality continuously.
Double Tap โฅ๏ธ For Part-4
21. How do you handle missing values?
Missing values are handled based on the reason and the impact on the problem. You first check whether data is missing at random or systematic. Common approaches include removing rows or columns if the missing percentage is small, imputing with mean, median, or mode for numerical data, using a separate category for missing values in categorical data, or applying model-based imputation when data loss affects predictions.
22. How do you treat outliers?
Outliers are treated after understanding their cause. If they result from data entry errors, they are corrected or removed. If they represent real but rare events, they are kept. Treatment methods include capping values, applying transformations like log scaling, or using robust models that handle outliers naturally. Blind removal is avoided.
23. What is data normalization and standardization?
Normalization rescales data to a fixed range, usually between zero and one. Standardization rescales data to have a mean of zero and a standard deviation of one. Both techniques ensure features contribute equally to model learning, especially for distance-based and gradient-based algorithms.
24. When do you use Min-Max scaling vs Z-score?
Min-Max scaling is used when data has a fixed range and no extreme outliers, such as image pixel values. Z-score scaling is used when data follows a normal distribution or contains outliers. Many machine learning models perform better with standardized data.
25. How do you handle imbalanced datasets?
Imbalanced datasets are handled by resampling techniques like oversampling the minority class or undersampling the majority class. You can also use algorithms that support class weighting or focus on metrics like recall, precision, and AUC instead of accuracy. The choice depends on business cost of false positives and false negatives.
26. What is one-hot encoding?
One-hot encoding converts categorical variables into binary columns. Each category becomes a separate column with values zero or one. This avoids ordinal assumptions and works well with most machine learning algorithms, especially linear and tree-based models.
27. What is label encoding?
Label encoding assigns a unique numeric value to each category. It is suitable when categories have an inherent order or when using tree-based models that handle ordinal values well. It is avoided for nominal data in linear models due to unintended ranking.
28. How do you detect data leakage?
Data leakage is detected by checking whether future or target-related information is present in training features. You validate time-based splits, review feature creation logic, and ensure preprocessing steps are applied separately on training and test data. Sudden high model accuracy is often a red flag.
29. What is duplicate data and how do you handle it?
Duplicate data refers to repeated records representing the same entity or event. Duplicates are identified using unique identifiers or key feature combinations. They are removed or merged based on business logic to prevent bias, inflated metrics, and incorrect model learning.
30. How do you validate data quality?
Data quality is validated by checking completeness, consistency, accuracy, and validity. This includes range checks, schema validation, distribution analysis, and reconciliation with source systems. Automated checks and dashboards are often used to monitor quality continuously.
Double Tap โฅ๏ธ For Part-4
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Data Science Interview Questions with Answers Part-4
โข 31. Why is Python popular in data science?
Python is popular because it is simple to read, easy to write, and fast to prototype. It has strong libraries for data analysis, machine learning, and visualization. It integrates well with databases, cloud platforms, and production systems. This makes it practical for both experimentation and deployment.
โข 32. Difference between list, tuple, set, and dictionary?
A list is an ordered and mutable collection used to store items that can change. A tuple is ordered but immutable, useful for fixed data. A set stores unique elements and is unordered, useful for removing duplicates. A dictionary stores key-value pairs and is used for fast lookups and structured data.
โข 33. What is NumPy and why is it fast?
NumPy is a library for numerical computing that provides efficient array operations. It is fast because operations run in optimized C code instead of Python loops. It uses contiguous memory and vectorized operations, which reduces execution time significantly for large datasets.
โข 34. What is Pandas and where do you use it?
Pandas is a data manipulation library used for cleaning, transforming, and analyzing structured data. It provides DataFrame and Series objects to work with tabular data. It is used for data cleaning, feature engineering, aggregation, and exploratory analysis before modeling.
โข 35. Difference between loc and iloc?
loc is label-based indexing, meaning it selects data using column names and row labels. iloc is position-based indexing, meaning it selects data using numeric row and column positions. loc is more readable, while iloc is useful when working with index positions.
โข 36. What are vectorized operations?
Vectorized operations apply computations to entire arrays at once instead of using loops. They are faster and more memory efficient. NumPy and Pandas rely heavily on vectorization to handle large datasets efficiently.
โข 37. What is lambda function?
A lambda function is an anonymous, single-line function used for short operations. It is commonly used with functions like map, filter, and sort. Lambdas improve readability when logic is simple and used only once.
โข 38. What is list comprehension?
List comprehension is a concise way to create lists using a single line of code. It combines looping and condition logic in a readable format. It is faster and cleaner than traditional for-loops for simple transformations.
โข 39. How do you handle large datasets in Python?
Large datasets are handled by reading data in chunks, optimizing data types, and using efficient libraries like NumPy and Pandas. For very large data, distributed frameworks such as Spark or Dask are used. Memory usage is monitored to avoid crashes.
โข 40. What are common Python libraries used in data science?
Common libraries include NumPy for numerical computing, Pandas for data manipulation, Matplotlib and Seaborn for visualization, Scikit-learn for machine learning, SciPy for scientific computing, and TensorFlow or PyTorch for deep learning.
Double Tap โฅ๏ธ For Part-5
โข 31. Why is Python popular in data science?
Python is popular because it is simple to read, easy to write, and fast to prototype. It has strong libraries for data analysis, machine learning, and visualization. It integrates well with databases, cloud platforms, and production systems. This makes it practical for both experimentation and deployment.
โข 32. Difference between list, tuple, set, and dictionary?
A list is an ordered and mutable collection used to store items that can change. A tuple is ordered but immutable, useful for fixed data. A set stores unique elements and is unordered, useful for removing duplicates. A dictionary stores key-value pairs and is used for fast lookups and structured data.
โข 33. What is NumPy and why is it fast?
NumPy is a library for numerical computing that provides efficient array operations. It is fast because operations run in optimized C code instead of Python loops. It uses contiguous memory and vectorized operations, which reduces execution time significantly for large datasets.
โข 34. What is Pandas and where do you use it?
Pandas is a data manipulation library used for cleaning, transforming, and analyzing structured data. It provides DataFrame and Series objects to work with tabular data. It is used for data cleaning, feature engineering, aggregation, and exploratory analysis before modeling.
โข 35. Difference between loc and iloc?
loc is label-based indexing, meaning it selects data using column names and row labels. iloc is position-based indexing, meaning it selects data using numeric row and column positions. loc is more readable, while iloc is useful when working with index positions.
โข 36. What are vectorized operations?
Vectorized operations apply computations to entire arrays at once instead of using loops. They are faster and more memory efficient. NumPy and Pandas rely heavily on vectorization to handle large datasets efficiently.
โข 37. What is lambda function?
A lambda function is an anonymous, single-line function used for short operations. It is commonly used with functions like map, filter, and sort. Lambdas improve readability when logic is simple and used only once.
โข 38. What is list comprehension?
List comprehension is a concise way to create lists using a single line of code. It combines looping and condition logic in a readable format. It is faster and cleaner than traditional for-loops for simple transformations.
โข 39. How do you handle large datasets in Python?
Large datasets are handled by reading data in chunks, optimizing data types, and using efficient libraries like NumPy and Pandas. For very large data, distributed frameworks such as Spark or Dask are used. Memory usage is monitored to avoid crashes.
โข 40. What are common Python libraries used in data science?
Common libraries include NumPy for numerical computing, Pandas for data manipulation, Matplotlib and Seaborn for visualization, Scikit-learn for machine learning, SciPy for scientific computing, and TensorFlow or PyTorch for deep learning.
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Most people who are skilled enough would be able to clear technical rounds with ease.
But when it comes to ๐ฏ๐ฒ๐ต๐ฎ๐๐ถ๐ผ๐ฟ๐ฎ๐น/๐ฐ๐๐น๐๐๐ฟ๐ฒ ๐ณ๐ถ๐ rounds, some folks may falter and lose the potential offer.
Many companies schedule a behavioral round with a top-level manager in the organization to understand the culture fit (except for freshers).
One needs to clear this round to reach the salary negotiation round.
Here are some tips to clear such rounds:
1๏ธโฃ Once the HR schedules the interview, try to find the LinkedIn profile of the interviewer using the name in their email ID.
2๏ธโฃ Learn more about his/her past experiences and try to strike up a conversation on that during the interview.
3๏ธโฃ This shows that you have done good research and also helps strike a personal connection.
4๏ธโฃ Also, this is the round not just to evaluate if you're a fit for the company, but also to assess if the company is a right fit for you.
5๏ธโฃ Hence, feel free to ask many questions about your role and company to get a clear understanding before taking the offer. This shows that you really care about the role you're getting into.
๐ก ๐๐ผ๐ป๐๐ ๐๐ถ๐ฝ - Be polite yet assertive in such interviews. It impresses a lot of senior folks.
Most people who are skilled enough would be able to clear technical rounds with ease.
But when it comes to ๐ฏ๐ฒ๐ต๐ฎ๐๐ถ๐ผ๐ฟ๐ฎ๐น/๐ฐ๐๐น๐๐๐ฟ๐ฒ ๐ณ๐ถ๐ rounds, some folks may falter and lose the potential offer.
Many companies schedule a behavioral round with a top-level manager in the organization to understand the culture fit (except for freshers).
One needs to clear this round to reach the salary negotiation round.
Here are some tips to clear such rounds:
1๏ธโฃ Once the HR schedules the interview, try to find the LinkedIn profile of the interviewer using the name in their email ID.
2๏ธโฃ Learn more about his/her past experiences and try to strike up a conversation on that during the interview.
3๏ธโฃ This shows that you have done good research and also helps strike a personal connection.
4๏ธโฃ Also, this is the round not just to evaluate if you're a fit for the company, but also to assess if the company is a right fit for you.
5๏ธโฃ Hence, feel free to ask many questions about your role and company to get a clear understanding before taking the offer. This shows that you really care about the role you're getting into.
๐ก ๐๐ผ๐ป๐๐ ๐๐ถ๐ฝ - Be polite yet assertive in such interviews. It impresses a lot of senior folks.
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Data Science Interview Questions with Answers Part-5
41. Why is data visualization important?
Data visualization helps you understand patterns, trends, and anomalies quickly. It simplifies complex data and supports faster decision-making. Visuals also help communicate insights clearly to stakeholders who do not work with raw data.
42. Difference between bar chart and histogram?
A bar chart compares discrete categories using separate bars. A histogram shows the distribution of continuous data using bins. Bar charts focus on comparison, while histograms focus on frequency and shape of data.
43. When do you use box plots?
Box plots are used to visualize data distribution, spread, and outliers. They help compare distributions across multiple groups and quickly highlight median, quartiles, and extreme values.
44. What does a scatter plot show?
A scatter plot shows the relationship between two numerical variables. It helps identify correlations, clusters, trends, and outliers. It is commonly used during exploratory analysis.
45. What are common mistakes in data visualization?
Common mistakes include using the wrong chart type, misleading scales, cluttered visuals, poor labeling, and ignoring context. These errors lead to incorrect interpretation and poor decisions.
46. Difference between Seaborn and Matplotlib?
Matplotlib is a low-level visualization library that provides full control over plots. Seaborn is built on top of Matplotlib and provides high-level, statistical visualizations with better default styling.
47. What is a heatmap used for?
A heatmap visualizes values using color intensity. It is commonly used to show correlations, missing values, or patterns across large matrices where numbers alone are hard to interpret.
48. How do you visualize distributions?
Distributions are visualized using histograms, density plots, and box plots. These charts help understand spread, skewness, and presence of outliers in data.
49. What is dashboarding?
Dashboarding is the process of creating interactive visual reports that track key metrics in real time or near real time. Dashboards support monitoring, analysis, and decision-making.
50. How do you choose the right chart?
You choose a chart based on the data type and the question being answered. Comparisons use bar charts, trends use line charts, relationships use scatter plots, and distributions use histograms or box plots.
Double Tap โฅ๏ธ For Part-6
41. Why is data visualization important?
Data visualization helps you understand patterns, trends, and anomalies quickly. It simplifies complex data and supports faster decision-making. Visuals also help communicate insights clearly to stakeholders who do not work with raw data.
42. Difference between bar chart and histogram?
A bar chart compares discrete categories using separate bars. A histogram shows the distribution of continuous data using bins. Bar charts focus on comparison, while histograms focus on frequency and shape of data.
43. When do you use box plots?
Box plots are used to visualize data distribution, spread, and outliers. They help compare distributions across multiple groups and quickly highlight median, quartiles, and extreme values.
44. What does a scatter plot show?
A scatter plot shows the relationship between two numerical variables. It helps identify correlations, clusters, trends, and outliers. It is commonly used during exploratory analysis.
45. What are common mistakes in data visualization?
Common mistakes include using the wrong chart type, misleading scales, cluttered visuals, poor labeling, and ignoring context. These errors lead to incorrect interpretation and poor decisions.
46. Difference between Seaborn and Matplotlib?
Matplotlib is a low-level visualization library that provides full control over plots. Seaborn is built on top of Matplotlib and provides high-level, statistical visualizations with better default styling.
47. What is a heatmap used for?
A heatmap visualizes values using color intensity. It is commonly used to show correlations, missing values, or patterns across large matrices where numbers alone are hard to interpret.
48. How do you visualize distributions?
Distributions are visualized using histograms, density plots, and box plots. These charts help understand spread, skewness, and presence of outliers in data.
49. What is dashboarding?
Dashboarding is the process of creating interactive visual reports that track key metrics in real time or near real time. Dashboards support monitoring, analysis, and decision-making.
50. How do you choose the right chart?
You choose a chart based on the data type and the question being answered. Comparisons use bar charts, trends use line charts, relationships use scatter plots, and distributions use histograms or box plots.
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