Of all the newly emerged professions, data science has gained the maximum popularity due to the vast ocean of opportunities it offers.

Are you wondering how to become a Data Scientist? But don't know how to crack it? Here are 25+ Data Science interview questions and answers often asked.

  1. What is Data Science?
  2. Differentiate between Data Analytics and Data Science
  3. Explain the steps in making a decision tree
  4. Differentiate between univariate, bivariate, and multivariate analysis
  5. How should you maintain a deployed model?
  6. What is a Confusion Matrix?
  7. Differences between supervised and unsupervised learning
  8. What does it mean when the p-values are high and low?
  9. When is resampling done?
  10. What do you understand by Imbalanced Data?
  11. Are there any differences between the expected value and the mean value?
  12. What do you understand by Survivorship Bias?
  13. Define the terms KPI, lift, model fitting, robustness, and DOE.
  14. Define confounding variables.
  15. Define and explain selection bias
  16. What is the bias-variance trade-off?
  17. What is logistic regression? State an example where you have recently used logistic regression.
  18. What is linear regression? What are some of the major drawbacks of the linear model?
  19. What is a random forest?
  20. What is deep learning?
  21. Differences between deep learning and machine learning
  22. What is a Gradient and Gradient Descent?
  23. How are the time series problems different from other regression problems?
  24. What are RMSE and MSE in a linear regression model?
  25. So, you have done some projects in machine learning and data science and we see you are a bit experienced in the field. Let's say your laptop's RAM is only 4GB and you want to train your model on 10GB data set. What will you do? Have you experienced such an issue before?

What is Data Science?

Data science is a multidisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data.

Data scientists use their skills in mathematics, statistics, computer science, and domain knowledge to solve complex problems.

Data science is used in a wide variety of industries, including healthcare, finance, marketing, and technology.

Data scientists play a vital role in helping organizations to make better decisions based on data.

Differentiate between Data Analytics and Data Science

Data analytics is a subset of data science that focuses on the collection, cleaning, and analysis of data to extract insights.

Data scientists use data analytics to understand the past and present and to make predictions about the future.

Data science is a broader field that encompasses data analytics, as well as machine learning, artificial intelligence, and other disciplines.

Data scientists use their skills to solve complex problems that require a deep understanding of data and the ability to apply advanced analytical techniques.

Explain the steps in making a decision tree

A decision tree is a machine learning algorithm that can be used for classification and regression tasks. It is a supervised learning algorithm, which means that it learns from a dataset of labeled data.


To make a decision tree, the algorithm follows these steps:

  1. Choose a split variable. The split variable is the variable that is most predictive of the target variable.
  2. Split the data into two subsets based on the split variable.
  3. Repeat steps 1 and 2 recursively on each subset until the subsets are pure, meaning that all of the data points in each subset have the same target value.
  4. Build a tree that represents the splitting process. The leaves of the tree represent the pure subsets, and the internal nodes of the tree represent the split variables.
  5. To make a prediction using a decision tree, the algorithm follows the tree from the root node to a leaf node.

At each internal node, the algorithm compares the value of the split variable to the value of the data point being predicted. The algorithm then follows the branch of the tree that corresponds to the value of the split variable.

Once the algorithm reaches a leaf node, it predicts the target value for the data point based on the target values of the data points in the leaf node.

Decision trees are a powerful machine learning algorithm that can be used to solve a wide variety of problems.

They are relatively easy to understand and interpret, and they can be trained on relatively small datasets.

Differentiate between univariate, bivariate, and multivariate analysis

Univariate analysis is a statistical technique that is used to analyze a single variable. It is used to describe the distribution of the variable and to identify any patterns or trends.

Bivariate analysis is a statistical technique that is used to analyze two variables. It is used to identify the relationship between the two variables and to determine whether the relationship is statistically significant.

Multivariate analysis is a statistical technique that is used to analyze three or more variables. It is used to identify the relationships between the variables and to determine how the variables interact with each other.

Here is a table that summarizes the key differences between univariate, bivariate, and multivariate analysis:

Type of analysisNumber of variablesPurpose
Univariate1To describe the distribution of a single variable and to identify any patterns or trends.
Bivariate2To identify the relationship between two variables and to determine whether the relationship is statistically significant.
Multivariate3 or moreTo identify the relationships between the variables and to determine how the variables interact with each other.

How should you maintain a deployed model?


Once a machine learning model has been deployed, it is important to monitor its performance and to retrain it as needed.

There are a number of things that can be done to maintain a deployed model, including:

Monitor the model's performance. This can be done by collecting metrics such as accuracy, precision, recall, and F1 score. If the model's performance starts to decline, it may be necessary to retrain the model. Retrain the model on new data. As new data becomes available, it is important to retrain the model on the new data. This will help to ensure that the model is still accurate and up-to-date.

Monitor for data drift. Data drift is a phenomenon that occurs when the distribution of the data changes over time. If data drift occurs, it may be necessary to retrain the model on a new dataset.

Update the model's features. As new features become available, it may be necessary to update the model's features. This will help to improve the model's performance.

What is a Confusion Matrix?

A confusion matrix is a table that is used to evaluate the performance of a machine-learning model. It shows the number of correct and incorrect predictions that the model made.

The rows of the confusion matrix represent the actual values of the target variable, and the columns of the confusion matrix represent the predicted values of the target variable.

The following table shows an example of a confusion matrix:

Actual Predicted
Positive Positive
True positive (TP) False negative (FN)
Positive Negative
False positive (FP) True negative (TN)

The following are some of the metrics that can be calculated from a confusion matrix:

Accuracy: Accuracy is the proportion of all predictions that were correct. It is calculated by dividing the sum of the TP and TN values by the total number of predictions.
Precision: Precision is the proportion of positive predictions that were correct. It is calculated by dividing the TP value by the TP + FP value.
Recall: Recall is the proportion of actual positives that were correctly predicted. It is calculated by dividing the TP value by the TP + FN value.
F1 score: The F1 score is a harmonic mean of precision and recall. It is calculated by taking the average of the precision and recall values, weighted by 2.

Confusion matrices are a valuable tool for evaluating the performance of machine learning models.

They can be used to identify areas where the model is struggling and to make improvements to the model.

Differences between supervised and unsupervised learning


Supervised learning

Supervised learning is a type of machine learning where the algorithm is trained on a labeled dataset.

The labeled dataset contains input data and the corresponding output data.

The algorithm learns the relationship between the input data and the output data and then uses that relationship to make predictions on new, unseen data.

Unsupervised learning

Unsupervised learning is a type of machine learning where the algorithm is trained on an unlabeled dataset.

The unlabeled dataset contains only input data. The algorithm learns to identify patterns and relationships in the input data and then uses that knowledge to make predictions or decisions.

Here is a table that summarizes the key differences between supervised and unsupervised learning:

CharacteristicSupervised learningUnsupervised learning
Labeled dataYesNo
Prediction taskYesNo
Common tasksClassification, regressionClustering, anomaly detection, recommendation systems

What does it mean when the p-values are high and low?

A p-value is the probability of obtaining a test statistic as extreme or more extreme than the one observed, assuming that the null hypothesis is true.

Low p-value: A low p-value indicates that the observed data is unlikely to have occurred by chance, and therefore provides evidence against the null hypothesis.

High p-value: A high p-value indicates that the observed data is likely to have occurred by chance, and therefore does not provide evidence against the null hypothesis.

For example, suppose we are testing the null hypothesis that the average height of men is equal to the average height of women. We collect a sample of men and women and measure their heights.

We then perform a statistical test to compare the average heights of the two groups.

If the p-value is low, then we can conclude that there is a statistically significant difference in average height between men and women.

If the p-value is high, then we cannot conclude that there is a statistically significant difference in average height between men and women.

When is resampling done?

Resampling is a statistical technique that is used to estimate the distribution of a statistic by drawing repeated samples from a dataset.

It is often used to evaluate the performance of a machine-learning model and to assess the statistical significance of the results.

Resampling can be used in a variety of situations, including:

Cross-validation: Cross-validation is a technique that is used to evaluate the performance of a machine-learning model on unseen data.

In cross-validation, the dataset is split into multiple folds. The model is trained on each fold and evaluated on the remaining folds.

The average performance of the model on the held-out folds is used to estimate the performance of the model on unseen data.

Bootstrapping: Bootstrapping is a technique that is used to estimate the standard error of a statistic. In bootstrapping, multiple samples with replacements are drawn from the dataset.

The statistic is calculated for each sample. The standard deviation of the statistic across the samples is used to estimate the standard error of the statistic.

Permutation testing: Permutation testing is a technique that is used to assess the statistical significance of a test statistic without making any assumptions about the distribution of the data.

In permutation testing, the labels of the data points are shuffled and the test statistic is calculated for the shuffled data.

This process is repeated many times. The p-value is calculated as the proportion of times that the test statistic for the shuffled data is as extreme or more extreme than the test statistic for the original data.

What do you understand by Imbalanced Data?

Imbalanced data is a dataset in which the classes are not evenly represented. For example, a dataset of fraudulent transactions might have a very small number of fraudulent transactions compared to the number of legitimate transactions.

Imbalanced data can pose a challenge for machine learning models, as the models may learn to ignore the minority class.

There are a number of techniques that can be used to address imbalanced data, such as:

Oversampling: Oversampling involves creating additional data points for the minority class. This can be done by duplicating existing data points or by creating synthetic data points.
Undersampling: Undersampling involves removing data points from the majority class. This can be done randomly or using a more sophisticated technique, such as Tomek links.
Cost-sensitive learning: Cost-sensitive learning algorithms assign different costs to misclassifying different classes. This can help to ensure that the model pays more attention to the minority class.

Are there any differences between the expected value and the mean value?


The expected value and the mean value are two different ways of measuring the central tendency of a dataset. The expected value is the average value of a random variable, while the mean value is the average value of a set of data points.

For a continuous distribution, the expected value and the mean value are the same. However, for a discrete distribution, the expected value and the mean value may be different.

This is because the expected value takes into account the probability of each data point occurring, while the mean value does not.

For example, suppose we have a coin that is weighted so that it is more likely to land on heads than tails.

The expected value of the number of heads that we will get in 10 flips of the coin is 6, even though the mean value of the number of heads that we will get in 10 flips of the coin is 5.

This is because the expected value takes into account the probability that we will get heads on each flip of the coin, while the mean value does not.

What do you understand by Survivorship Bias?

Survivorship bias is a logical fallacy that occurs when we only consider the survivors of an event and ignore the non-survivors.

This can lead to a distorted view of the situation. For example, if we only look at the successful entrepreneurs who have made it to the top, we might conclude that all entrepreneurs are successful.

However, this would be ignoring the many entrepreneurs who have failed along the way. Survivorship bias can also be seen in machine learning.

For example, if we only train a machine learning model on data from successful customers, the model may not be able to accurately predict the behavior of unsuccessful customers.

To avoid survivorship bias, it is important to consider all of the data, not just the data from the survivors.

Define the terms KPI, lift, model fitting, robustness, and DOE.


KPI (Key Performance Indicator)

A KPI is a measurable value that demonstrates how effectively a company is achieving key business objectives. KPIs are used to track progress towards goals and to identify areas where improvement is needed.

Examples of KPIs include:

  • Revenue growth
  • Customer satisfaction
  • Market share
  • On-time delivery

Lift

Lift is a performance measure of the target model measured against a random choice model. Lift indicates how good the model is at prediction versus if there was no model.

For example, a lift of 2.0 means that the model is twice as likely to make a correct prediction as a random choice model.

Model fitting

Model fitting is the process of training a machine learning model on a dataset. The goal of model fitting is to find a model that accurately predicts the output variable for new data points.

Robustness

Robustness is the ability of a machine learning model to perform well on new data, even if the new data is different from the data that the model was trained on.

A robust model is less likely to overfit the training data, and it is more likely to generalize well to new data.

Design of Experiments (DOE)

DOE is a systematic approach to planning and conducting experiments. DOE is used to identify the relationships between different factors and to optimize the outcome of a process.

DOE is often used in machine learning to design experiments that help to improve the performance of machine learning models.

Define confounding variables.

Confounding variables are variables that are correlated with both the independent and dependent variables in a study.

Confounding variables can make it difficult to determine the true causal relationship between the independent and dependent variables.

For example, suppose we are studying the relationship between smoking and lung cancer.

Age is a confounding variable in this study because it is correlated with both smoking and lung cancer. Older people are more likely to smoke and more likely to develop lung cancer.

Define and explain selection bias

Selection bias is a type of bias that occurs when the sample of data that is collected is not representative of the population that we are interested in. Selection bias can lead to inaccurate conclusions about the population.

For example, suppose we are studying the relationship between smoking and lung cancer.

We collect a sample of people who have been diagnosed with lung cancer. This sample is not representative of the population because it oversamples people who have lung cancer.

Selection bias can be avoided by using random sampling techniques to collect data.

What is the bias-variance trade-off?


The bias-variance trade-off is a concept in machine learning that describes the relationship between the bias and variance of a model.

Bias is the error that occurs when the model's predictions are consistently different from the true value.

Variance is the error that occurs when the model's predictions are inconsistent, even when given the same data.

There is a trade-off between bias and variance because it is impossible to create a model with both low bias and low variance.

A model with low bias will tend to have high variance, and a model with low variance will tend to have high bias.

This is because a model with low bias will be more complex and will fit the training data more closely.

However, this also means that it is more likely to overfit the training data and not generalize well to new data.

A model with low variance will be simpler and will not fit the training data as closely.

However, this also means that it is less likely to overfit the training data and will generalize better to new data.

The goal of machine learning is to find a model that has a good balance of bias and variance. This will help to ensure that the model is able to learn from the training data without overfitting and that it is able to generalize well to new data.

What is logistic regression? State an example where you have recently used logistic regression.

Logistic regression is a machine learning algorithm that is used for classification tasks.

It is a supervised learning algorithm, which means that it learns from a labeled dataset.

The labeled dataset contains input data and the corresponding output data. The output data is a binary variable, which means that it can only take on two values, such as 0 or 1, or true or false.

Logistic regression works by fitting a logistic function to the data. The logistic function is a sigmoid function that outputs a probability between 0 and 1.

The probability represents the likelihood that the input data belongs to the positive class.

Logistic regression is a widely used algorithm for classification tasks. It is easy to implement and interpret, and it can be used to solve a variety of problems, such as predicting whether a customer will churn, whether a patient has a disease, or whether a loan will be defaulted on.

Here is an example of where I recently used logistic regression:

I was working on a project to predict whether a customer would click on an ad. I used logistic regression to fit a model to the data, which included features such as the customer's demographics, interests, and past behavior.

The model was able to achieve a high accuracy on the training data, and it was also able to generalize well to new data.

The model was deployed to production, and it is now being used to help the company target its ads more effectively.

What is linear regression? What are some of the major drawbacks of the linear model?


Linear regression is a machine learning algorithm that is used for regression tasks. It is a supervised learning algorithm, which means that it learns from a labeled dataset.

The labeled dataset contains input data and the corresponding output data. The output data is a continuous variable, which means that it can take on any value.
Linear regression works by fitting a linear function to the data.

The linear function is a function of the input data that outputs a continuous value.
Linear regression is a simple and effective algorithm for regression tasks.

It is easy to implement and interpret, and it can be used to solve a variety of problems, such as predicting the price of a house, the number of customers who will visit a store on a given day or the amount of revenue that a company will generate in a given quarter.

However, there are some drawbacks to the linear model. One drawback is that it is not always able to capture the complexity of real-world data.

For example, if the data is non-linear, then the linear model will not be able to fit the data accurately.

Another drawback of the linear model is that it is sensitive to outliers. Outliers are data points that are significantly different from the rest of the data.

If the data contains outliers, then they can skew the results of the linear regression model.

Despite its drawbacks, linear regression is still a widely used algorithm for regression tasks. It is a simple and effective algorithm that can be used to solve a variety of problems.

Here are some of the major drawbacks of the linear model:

  • It cannot capture the complexity of real-world data, which is often non-linear.
  • It is sensitive to outliers. It can be difficult to interpret the coefficients of the linear model, especially when there are many features.
  • To address these drawbacks, there are a number of more advanced regression algorithms that can be used, such as decision trees, support vector machines, and random forests.
  • These algorithms are more complex than linear regression, but they can often achieve better performance on real-world data.


What is a random forest?

Random forest is a supervised machine learning algorithm that combines the predictions of multiple decision trees to produce a more accurate prediction.

It is a popular algorithm for classification and regression tasks.

This is how a random forest works:

Random forests work by constructing a large number of decision trees, each of which is trained on a random subset of the data.

The algorithm also uses a technique called feature bagging, which randomly selects a subset of the features to use at each split in the tree.

Once all of the decision trees have been trained, they are used to make predictions on new data. Each tree makes a prediction, and the final prediction of the random forest is the average of the predictions of all of the trees.

Advantages of random forests
Random forests have a number of advantages over other machine-learning algorithms, including:

  • They are very accurate for both classification and regression tasks.
  • They are robust to overfitting.
  • They can handle high-dimensional data.
  • They are relatively easy to interpret.

Disadvantages of random forests
Random forests also have a few disadvantages, including:

  • They can be computationally expensive to train, especially for large datasets.
  • They are not as good at explaining their predictions as some other machine learning algorithms.

What is deep learning?

Deep learning is a subset of machine learning that uses artificial neural networks to learn from data.

Artificial neural networks are inspired by the structure and function of the human brain.

Deep learning algorithms work by training artificial neural networks on large amounts of data.

The neural networks learn to identify patterns in the data and to make predictions based on those patterns.

Deep learning algorithms have been very successful in a wide range of tasks, including:

  • Image recognition
  • Natural language processing
  • Machine translation
  • Speech recognition


Differences between deep learning and machine learning

Deep learning is a subset of machine learning, but there are some key differences between the two.

Machine learning algorithms are typically trained on hand-crafted features, while deep learning algorithms learn their own features from the data.

Machine learning algorithms are typically simpler and less computationally expensive to train than deep learning algorithms.

Deep learning algorithms are typically better at learning complex patterns in data, but they can be more prone to overfitting.

Feature Deep learning Machine learning
Definition A subset of machine learning that uses artificial neural networks to learn from data. A field of computer science that gives computers the ability to learn without being explicitly programmed.
Learning method Learns features from the data itself. Uses hand-crafted features.
Model complexity More complex models. Less complex models.
Computational cost More computationally expensive to train. Less computationally expensive to train.
Applications Image recognition, natural language processing, machine translation, and speech recognition. Classification, regression, clustering, anomaly detection.

What is a Gradient and Gradient Descent?

A gradient is a vector that points in the direction of the steepest ascent of a function.

It is calculated as the partial derivative of the function with respect to each of its input variables.

Gradient descent is an optimization algorithm that uses the gradient of a function to find its minimum value.

It works by iteratively moving in the opposite direction of the gradient, which is the direction of the steepest descent.

Gradient descent is commonly used to train machine learning models, such as linear regression and neural networks.

How are the time series problems different from other regression problems?

Time series problems are different from other regression problems in that the data points are ordered in time.

This means that the value of a data point at a given time may depend on the values of previous data points.

One of the key challenges in time series forecasting is to identify the relationships between the data points and to use those relationships to make predictions about future data points.

Some common time series forecasting methods include:

Autoregressive (AR) models: AR models use the previous values of a time series to predict the next value.

Moving average (MA) models: MA models use the weighted average of past errors to predict the next error.

Autoregressive moving average (ARMA) models: ARMA models combine AR and MA models to produce more accurate predictions.

Artificial neural networks: Artificial neural networks can be used to learn complex patterns in time series data and to make predictions about future data points.

What are RMSE and MSE in a linear regression model?

RMSE (root mean squared error) and MSE (mean squared error) are two metrics that are used to evaluate the performance of a linear regression model.

RMSE is calculated as the square root of the average of the squared errors and MSE is calculated as the average of the squared errors.

Both RMSE and MSE are measures of how well the model's predictions fit the actual data. Lower values of RMSE and MSE indicate a better fit.

So, you have done some projects in machine learning and data science and we see you are a bit experienced in the field. Let's say your laptop's RAM is only 4GB and you want to train your model on 10GB data set. What will you do? Have you experienced such an issue before?

Yes, I have experienced the challenge of training a machine-learning model on a large dataset with limited RAM. It is a common problem for data scientists, especially those who are just starting out or who do not have access to powerful computing resources.

There are a few things you can do to address this challenge:

  1. Reduce the size of your dataset. This can be done by removing irrelevant features, downsampling the data, or using a technique called stratified sampling to ensure that the reduced dataset is representative of the original dataset.
  2. Use a cloud computing platform. Cloud computing platforms such as Google Cloud Platform, Amazon Web Services, and Microsoft Azure offer a variety of machine learning services that can be used to train models on large datasets. These services typically have much more RAM and computing power than a laptop, so they can be used to train models that would not be possible to train on a laptop.
  3. Use a distributed training algorithm. Distributed training algorithms allow you to train a model on multiple machines at the same time. This can be a good option if you have multiple laptops or desktops that you can use.
  4. Use a model compression technique. Model compression techniques can be used to reduce the size of a trained machine learning model without sacrificing too much accuracy. This can be a good option if you need to deploy your model on a device with limited RAM.

In the specific case of having a laptop with 4GB of RAM and a 10GB dataset, I would recommend using a cloud computing platform or a distributed training algorithm. These are the most effective ways to train a large model on a machine with limited RAM.

Here are some additional tips for training machine learning models on limited hardware:

  • Use a lightweight machine-learning library. There are a number of lightweight machine learning libraries available, such as scikit-learn and TensorFlow Lite. These libraries are designed to be used on devices with limited resources.
  • Use a GPU. GPUs can significantly accelerate the training of machine learning models. If your laptop has a GPU, be sure to use it for training your model.
  • Use a smaller model architecture. Larger model architectures require more RAM and computing power to train. If you are training on limited hardware, try using a smaller model architecture.
  • Use early stopping. Early stopping is a technique that stops the training process when the model's performance starts to degrade. This can help to prevent overfitting and can also save time and resources.

By following these tips, you can successfully train machine learning models on limited hardware.

Since you have experience in the deep learning field, can you tell us why TensorFlow is the most preferred library in deep learning?

TensorFlow is the most preferred library in deep learning for a number of reasons, including:

  1. Flexibility

    TensorFlow is a very flexible library that can be used to build a wide variety of deep-learning models.

    It can be used for both supervised and unsupervised learning, and it can be used to train models on a variety of different types of data, including images, text, and audio.
  2. Scalability

    TensorFlow is designed to be scalable, so it can be used to train and deploy large models on large datasets.

    This is important for many real-world deep learning applications, such as image recognition and natural language processing.
  3. Performance

    TensorFlow is a very performant library, and it can be used to train and deploy models on a variety of different hardware platforms, including CPUs, GPUs, and TPUs.

    This is important for many real-world deep learning applications, where speed and efficiency are critical.
  4. Community
    TensorFlow has a large and active community of users and developers. This means that there is a lot of support available for TensorFlow users, and there is a constant stream of new features and improvements being added to the library.

    In addition to these general reasons, TensorFlow is also preferred by many deep learning practitioners because it is the library that is used by many of the leading companies in the field, such as Google, Facebook, and Amazon.

    This means that there is a lot of documentation and tutorials available for TensorFlow, and it is easy to find other people who can help you if you have problems.

Here are some specific examples of how TensorFlow is used in the real world:

  1. Image recognition: TensorFlow is used to train and deploy image recognition models that are used in a variety of applications, such as self-driving cars, facial recognition, and medical imaging.
  2. Natural language processing: TensorFlow is used to train and deploy natural language processing models that are used in a variety of applications, such as machine translation, text summarization, and sentiment analysis.
  3. Speech recognition: TensorFlow is used to train and deploy speech recognition models that are used in a variety of applications, such as voice assistants and dictation software.

Overall, TensorFlow is a powerful and flexible deep-learning library that is well-suited for a wide variety of applications. It is the most preferred library in deep learning because it is flexible, scalable, performant, and has a large and active community.

With all these Data Science Interview questions, we hope you are now prepared to ace your next big interview and bag your dream job!

All the best!

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