Top 50 Data Science Interview Questions with Answers

Q1:What is Data Science?

Data science is the study of data using statistical techniques and computer programming to predict what will happen,  recommend what is good, find associations, understand patterns, etc. It is a multidisciplinary approach that combines principles and practices from the fields of mathematics, statistics, and computer engineering to analyze large amounts of data.

Data science is used to study data in four main ways:

• • Predictive Analysis: Predictive analysis is used to study historical data to make accurate forecasts about data patterns that may occur in the future.
  • Prescriptive Analysis: Prescriptive analysis is used to recommend the best course of action based on given data input. 
  • Descriptive Analysis: Descriptive analysis examines data to gain insights into what happened or what is happening in the data environment.
  • Diagnostic Analysis: Diagnosis analysis is used for detailed data examination to understand why something happened.

Q2: Explain the difference between Data Analytics and Data Science.

 Data Analytics involves examining data to draw conclusions about it, often to support decision-making. Data Science, on the other hand, covers a broader range of activities, including data collection, cleansing, analysis, and predictive modeling. It’s a more comprehensive field that uses scientific methods, algorithms, processes, and systems to extract insights and knowledge from data.

Q3: Explain Supervised and Unsupervised Learning.

Supervised Learning: In supervised learning, the algorithm is trained on labeled data, where input features are associated with corresponding target labels. It aims to learn a mapping between inputs and outputs.

Unsupervised Learning: In unsupervised learning, the algorithm explores the data’s inherent structure without explicit target labels. Clustering and dimensionality reduction are common tasks in unsupervised learning.

Q4: Explaining Feature Engineering. Also, explain different feature engineering techniques.

Feature engineering is the process of transforming raw data into useful features that better represent the underlying patterns in the data. It involves selecting, creating, and modifying features to enhance the performance of machine learning models. This process is crucial because the quality of features directly impacts the model’s ability to make accurate predictions.

Some of the Feature Engineering techniques are:

• Scaling: Scaling involves normalizing numerical features to a consistent scale. Common methods include Min-Max Scaling and Z-score Standardization. This ensures that features with different scales contribute equally to model training.
• One-Hot Encoding: One-Hot Encoding is used to convert categorical variables into binary vectors. Each category becomes a separate binary feature, preventing the model from misinterpreting categorical data as ordinal.
• Creating New Features: Feature creation involves generating new features from existing ones.
• Handling Missing Values: Dealing with missing values includes imputation techniques such as mean, median, or mode imputation to replace missing values.
• Binning: Binning groups continuous data into discrete intervals, reducing noise and capturing non-linear relationships.
• Log Transformation: It involves applying the natural logarithm function to the values of a numerical feature. This transformation is often used to handle data that has a wide range of values or data that is skewed towards larger values. Log transformation can help make the data more suitable for certain types of models by reducing the impact of extreme values and making the distribution of the data more symmetric. This can lead to improved model performance and better capture of relationships between variables.
• Feature Selection: Feature selection is like picking the most important parts from a collection of items. In the world of data and machine learning, it means choosing the most relevant features or attributes from a dataset. By selecting the right features, you simplify your data and improve the performance of your model. Feature selection methods like Recursive Feature Elimination (RFE) and SelectKBest help choose the most relevant features, reducing model complexity and improving interpretability.
• Domain-Specific Features: Domain-specific features leverage domain knowledge to engineer features that are contextually relevant. These features enhance model performance by incorporating subject-specific insights.

Feature engineering is a critical step in the data preprocessing pipeline, as it not only enhances model performance but also ensures that the data is effectively represented for machine learning algorithms. Different techniques cater to different data types and model requirements, aiming to extract meaningful information from raw data.

Q5: Can you explain Outliers?

Outliers are data points that significantly deviate from the rest of the data. They can skew statistical analyses and machine learning models. Outliers can arise due to measurement errors or genuine anomalies in the data distribution.

Q6: Explain Linear Regression:

Linear Regression is a statistical technique used to model the relationship between a dependent variable and one or more independent variables. It assumes a linear relationship and aims to find the best-fit line that minimizes the sum of squared differences between actual and predicted values.

Q7: What is the purpose of a Linear Regression?

The purpose of Linear Regression is to predict or explain the value of a dependent variable based on one or more independent variables. It helps us understand the relationship between variables and make predictions using the learned linear equation.

Q8: Explain Intercept and Slope.

In a linear regression equation (y = B0 + Bx), the intercept (B0) is the value of the dependent variable when the independent variable is zero. The slope (B) represents the change in the dependent variable for a unit change in the independent variable.

Q9: What is a residual error?

The residual error is the difference between the actual observed value and the predicted value from a model. It measures how well the model’s predictions align with the actual data points. Minimizing the sum of squared residuals is a common approach to finding the best-fit line in Linear Regression.

Q10: What is Bias and Variance?

Bias refers to the error introduced by approximating a real-world problem, which may be complex, by a simplified model. Variance, on the other hand, measures the model’s sensitivity to small fluctuations in the training data. Balancing bias and variance is crucial for creating models that generalize well to unseen data.

Q11: Explain Bias-Variance Tradeoff.

The bias-variance tradeoff is the balance between a model’s ability to fit the training data accurately (low bias) and its ability to generalize to new data (low variance). As bias decreases, variance typically increases, and vice versa. Achieving the right balance is essential for model performance.

Q12: What is the Goodness of Fit?

The goodness of fit measures how well a model’s predictions match the actual observed data. It indicates how well the model fits the underlying pattern in the data. Common metrics for measuring goodness of fit include R-squared and Mean Squared Error (MSE).

Q13: What do you understand by the Significant Variables in a Model?

The number of significant variables in a model refers to the number of independent variables that have a statistically significant impact on the dependent variable. It’s important for model interpretability and eliminating noise from the model.

Q14: What is  R-squared?

R-squared (coefficient of determination) is a statistical measure that represents the proportion of the variance in the dependent variable that is explained by the independent variables in a regression model. It ranges from 0 to 1.

Q15: Can you explain skewness?

Skewness measures the asymmetry in the distribution of a dataset. A positive skew indicates a longer tail on the right side of the distribution, while a negative skew indicates a longer tail on the left side.

Q16: What is Standard Deviation?

Standard deviation is a measure of the dispersion or spread of a dataset. It quantifies how much individual data points deviate from the mean. A higher standard deviation indicates greater variability.

Q17: What do you understand by p-value?

P-value is a measure in statistics that helps determine the significance of a result. It indicates the probability of observing a test statistic as extreme as the one computed from the sample data, assuming the null hypothesis is true. A lower p-value suggests stronger evidence against the null hypothesis. Variables with a p-value of less than 0.05 are considered as significant variables in the model.

Q18: What is Pearson Correlation?

Pearson correlation measures the linear relationship between two continuous variables. It ranges from -1 to 1, where -1 indicates a perfect negative linear correlation, 1 indicates a perfect positive linear correlation, and 0 indicates no linear correlation.

Q19: What is the difference between Covariance and Correlation?

Covariance measures the degree to which two variables change together, while correlation standardizes this measure by dividing it by the product of the variables’ standard deviations. Correlation provides insight into the strength and direction of the relationship.

Q20: Explain Probability.

Probability is a measure of the likelihood of an event occurring. It ranges from 0 (impossible event) to 1 (certain event). It’s a fundamental concept in statistics and is used to make predictions and decisions based on uncertain outcomes.

Q21: What is Joint Probability?

A Joint probability is the probability of two or more events occurring simultaneously. It’s calculated by multiplying the individual probabilities of each event.

Q22: Explain Conditional Probability.

Conditional probability is the probability of an event occurring given that another event has already occurred. It’s calculated by dividing the joint probability of both events by the probability of the condition event.

Q23:What is Kurtosis?

Kurtosis measures the peakiness of a distribution. A higher kurtosis indicates heavier tails and a more peaked distribution, while a lower kurtosis indicates lighter tails and a flatter distribution compared to a normal distribution.

Q24: How do you check the presence of outliers in a variable?

Outliers can be identified using methods like the IQR (Interquartile Range) method, Z-score, or visualization techniques like box plots. Outliers are points that fall significantly outside the expected range of values.

Q25: Which Python libraries did you use in your projects?

I have used many Python libraries in my projects, including pandas for data manipulation, NumPy for numerical operations, scikit-learn for machine learning, Matplotlib and Seaborn for data visualization, and more depending on the specific project requirements.

Q26: What is EDA?

EDA (Exploratory Data Analysis) is the process of analyzing and visualizing data to uncover patterns, relationships, and insights. It involves summary statistics, data visualization, and often leads to formulating hypotheses for further analysis.

Q27: Explain Logistic Regression.

Logistic Regression is a classification algorithm used to model the probability of a binary outcome (0 or 1). It’s commonly used in scenarios where the dependent variable represents a categorical response.

Q28: What is the difference between Linear and Logistic Regression?

Linear Regression predicts a continuous outcome, while Logistic Regression predicts

a categorical outcome. Linear Regression models the relationship between variables using a straight line, whereas Logistic Regression predicts the probability of a binary outcome (like “yes” or “no”).

Q29: What is Confusion Matrix?

A confusion matrix is a table used to describe the performance of a classification model. It presents the counts of true positive, true negative, false positive, and false negative predictions, helping to assess the model’s accuracy and error types.

Q30: Can you explain True Positive, True Negative, False Positive, and False Negative?

• • True Positive (TP): Model correctly predicts positive class.
  • True Negative (TN): Model correctly predicts negative class.
  • False Positive (FP): Model incorrectly predicts positive class.
  • False Negative (FN): Model incorrectly predicts negative class.

Q31: What is hypothesis testing?

Hypothesis testing is a method in statistics used to check if a statement or assumption about a population is likely to be true based on a sample of data. It involves comparing the sample data to the assumptions made in the statement, and then deciding whether the differences or patterns observed are significant enough to support or challenge those assumptions.

Q32: Explain the Null and Alternate Hypothesis.

Null Hypothesis (H0): The null hypothesis is like the default assumption. It’s a statement that suggests there’s no significant effect, no difference, or no relationship in the population. In other words, it’s the idea that whatever you’re investigating has no real impact or change. For example, if you’re testing a new drug’s effectiveness, the null hypothesis might be that the drug has no effect on patients’ health.

Alternate Hypothesis (Ha or H1): The alternate hypothesis is the opposite of the null hypothesis. It’s what you’re trying to find evidence for. It suggests that there is a significant effect, difference, or relationship in the population. It proposes that something interesting or important is happening. Using the same example, the alternate hypothesis could state that the new drug indeed has a positive effect on patients’ health.

Q33: Explain error types (Type-I and Type-II Error).

Type-I and Type-II errors are concepts that relate to hypothesis testing and decision-making in statistics:

Type-I Error (False Positive): A Type-I error occurs when you wrongly reject the null hypothesis when it’s actually true. In other words, you conclude that there’s a significant effect or difference when, in reality, there isn’t one. Type-I error is also called an alpha error.

Type-II Error (False Negative): A Type-II error happens when you fail to reject the null hypothesis when it’s actually false. This means you miss detecting a real effect or difference that exists in the population. It’s like failing to notice something important that’s right in front of you. Type-II error is also called a beta error.

To put it in simpler terms:

• • Type-I Error (False Positive): Rejecting a true null hypothesis.
  • Type-II Error (False Negative): Failing to reject a false null hypothesis. 

Q34:What is Precision?

Precision is a measure of accuracy in binary classification tasks. It measures the accuracy of positive predictions made by the model.

If the precision is high, it means that when the model says something is positive, it’s usually correct. It’s like having a reliable detector that doesn’t often sound false alarms. On the other hand, if precision is low, the model might be labeling too many things as positive, even when they’re not.

Precision= True Positives / (True Positives+False Positives)

​Q35: What is Recall?

Recall, also known as sensitivity or true positive rate, is the ratio of true positive predictions to the actual positives. It measures the model’s ability to capture all positive instances. If the model has a high recall, it means it’s good at finding most of the positive cases. 

Recall= True Positives / (True Positives+False Negatives)

Q36: Why do we use Precision and Recall?
Precision and recall are used when we want to evaluate the performance of a machine-learning model, especially in binary classification tasks. 

Q37: What is F1 Score?

F1 score is a metric that combines precision and recall. It’s the harmonic mean of the two and provides a balanced measure of a model’s accuracy, especially when dealing with imbalanced datasets.

Q38: Explain Confidence Interval (CI). 

A confidence interval is like a range of values that helps us estimate where the true value of a population parameter, like a mean or a proportion, is likely to be. It’s a way to express the uncertainty or variability in your sample data.

For example, if we try to make a good guess about the average height of people in a city, we can’t measure everyone, so we take a sample. The confidence interval gives us a range of values within which we are pretty sure the actual average height for the whole city falls.

If we calculate a 95% confidence interval for the average height and it’s from 160 cm to 170 cm, it means we are 95% confident that the true average height of the entire population falls within this range.

Let’s see another example. 

Let’s say we are conducting a survey to estimate the average number of hours people spend watching TV per week in a city. We have collected data from a sample of 100 people and found that the average number of hours they watch TV is 15 hours, with a standard deviation of 2 hours.

Now, we want to create a confidence interval to estimate the true average number of hours people in the entire city watch TV per week.

Using a 95% confidence level and the data we collected, we can calculate the confidence interval as follows:

• Calculate the standard error of the mean:

Standard Error=Standard Deviation / Sqrt(Sample Size) =2 / Sqrt(100)=0.2

• Find the margin of error:

Margin of Error=Critical Value×Standard Error

For a 95% confidence level, the critical value is typically around 1.96.

Margin of Error=1.96×0.2=0.392

• Calculate the confidence interval:

Lower Limit=Sample Mean−Margin of Error=15−0.392=14.608

Upper Limit=Sample Mean+Margin of Error=15+0.392=15.392

So, 95% confidence interval for the average number of hours people watch TV per week in the town is approximately 14.608 to 15.392 hours. This means we are 95% confident that the true average falls within this range based on the sample data you collected.

Q39: Why do we use confidence intervals?

It is more or less impossible to study every single data in a population, so we select a sample or sub-group of the population. Hence, a confidence interval is simply a way to measure how well a sample represents the population we are studying.

Q40: Explain the non-parametric test.

The non-parametric test is a statistical method used in hypothesis testing, which does not make assumptions about the frequency distribution of variables that are to be evaluated. The non-parametric experiment is used when there are skewed data, and it comprises techniques that do not depend on data pertaining to any particular distribution.

Non-parametric tests are particularly useful when:

• • Data Distribution is Unknown
  • Data is Ordinal or Categorical
  • Data Contains Outliers
  • Sample Size is Smal

Examples of non-parametric tests include the Wilcoxon signed-rank test for paired data and the Kruskal-Wallis test for comparing multiple groups.

Q41: Explaining Decision Tree.

A Decision Tree is a supervised machine learning algorithm used for classification and regression tasks. It creates a tree-like structure where each internal node represents a decision based on a feature, leading to different branches and eventual predictions at leaf nodes.

Q42: What is Pruning?

Pruning is the process of removing branches from a decision tree to prevent overfitting. It involves cutting back on tree depth or eliminating branches with low predictive power.

Q43: What is entropy?

Entropy is a measure of impurity or disorder in a set of data. In a decision tree, it’s used to decide the best split at each node by minimizing entropy in the resulting child nodes.

Q44: What is Information Gain?

Information gain measures the reduction in entropy achieved by splitting a dataset based on a particular attribute. It helps to determine the best attribute for making decisions in a decision tree.

Q45: What is Selection Bias?

Selection bias occurs when the data used for analysis is not representative of the entire population due to non-random sampling. This can lead to skewed or inaccurate results.

Q46: Explain Random Sampling.

Random sampling is a technique where each record in a population has an equal chance of being selected for the sample. It helps reduce bias and ensures that the sample is representative of the population.

Q47: What do you mean by a normal distribution?

Normal distribution, also known as Gaussian distribution, is a symmetric probability distribution characterized by a bell-shaped curve. 

Q48: What is k in the KNN algorithm?

In the k-nearest neighbors (KNN) algorithm, “k” refers to the number of nearest neighbors that are considered when making a prediction for a new data point. 

Q49: What is Bootstrapping?

Bootstrapping is a statistical resampling technique used to estimate the sampling distribution of a statistic by repeatedly resampling with replacement from the original data. It’s a powerful method for making inferences about a population without assuming a specific underlying probability distribution.

Q50: What is Bagging?

Bagging (Bootstrap Aggregating): Bagging is an ensemble machine learning technique that aims to improve the stability and accuracy of models by combining multiple individual models into a single, more robust model. It involves creating multiple subsets of the original training data through random sampling with replacement (bootstrap sampling). A separate model is trained on each subset, and the predictions of these models are aggregated, often by majority voting (for classification) or averaging (for regression). Random Forest is a well-known example of a bagging algorithm.

In simple terms, bagging combines models in parallel by training them on bootstrapped subsets of data, aiming to reduce variance and increase stability.