Data science interview questions: In today’s data-driven world, a career in data science provides interesting options. Data scientists are highly sought-after specialists who use their mathematics, statistics, and programming knowledge to get insights from massive amounts of data and find solutions to challenging issues. They are essential in turning raw data into knowledge that may provide useful information and aid in the development of businesses.
Data Science Interview Questions Applicants who obtain positions are not those with the best technical talents but those who can combine those skills with interview expertise. Although the Data science course is a broad area, a few issues come up frequently in interviews. As a result, we’ve produced a list of the most often asked data science interview questions and their answers.
Top Data Science Interview Questions and Answers
Data science aspirants must consider checking out data science interview questions. In order to find patterns, trends, and correlations, data scientists frequently use sophisticated analytical methods and machine learning algorithms on a variety of data sets.
They collaborate closely with stakeholders to comprehend corporate goals and provide data-driven solutions that tackle certain problems. In recent years Data scientists have been in high demand due to the recent exponential growth in data, making this a desirable and rewarding career path.
Best Data Science Interview Questions
The hiring process for data science positions heavily relies on data science interview questions. Aspirants who are willing to join as data scientists must go through the data science interview questions. These skills also demonstrate the candidate’s proficiency in data analysis and modeling; these questions test their ability to apply their knowledge and abilities to practical situations.
Critical thinking and problem-solving:
Data science interview questions test the candidate’s aptitude required in the field of data science. Employers evaluate candidates’ abilities to approach and deconstruct complicated challenges, create winning solutions, and articulate their ideas. These inquiries give information regarding a candidate’s analytical attitude and capacity to draw conclusions from data.
Cultural Fit and Communication:
Data science interview questions can assess a candidate’s cultural fit with the company and their capacity for productive teamwork. Interviewers may use behavioral questions to evaluate candidates’ aptitude for collaboration, communication, and flexibility. These inquiries assist in determining whether the candidate can help stakeholders with their findings and ideas.
Evaluate the candidate’s abilities:
Candidates must cover all aspects that can be evaluated during a data science interview, as this is an important step in the hiring process for data scientists. Interviewers might extract useful data in this way to help them make informed selections. The performance and success of an organization can be significantly impacted by a candidate’s capacity to communicate and resolve issues effectively. The impact of the candidate’s previous work, their skill in addressing real-world data difficulties, and the practical application of data science concepts can all be learned during the interview.
Throughout the interview process, candidates are given numerous chances to talk about their prior data science initiatives, experiences, and accomplishments.
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Data science interview questions
Q1. Explain logistic regression.
Ans. Logistic regression is a data analysis technique that uses mathematics to determine the relationships between two data factors. It then uses this relationship to determine the value of one of those factors based on the other.
Q2. What are the steps involved in making a decision tree
Ans. Consider the entire data set as input.
- Calculate the entropy of the target variable, and find the predictor attributes
- Calculate your information gain of all attributes (we gain crucial information on sorting various objects from each other)
- Choose the attribute with the highest information gain as the root node
- Repeat the same process on every branch until the decision node of each branch ends
For example, Candidate has to predict the weather forecast for today. The decision tree for this case is shown below:
It is clear from the decision tree that it will rain if:
- The wind is weak
- No humidity
Q3. Differentiate between the long and wide format data.
Ans.
| Long format data | Wide format data |
| Each row of the data represents the one-time information of the subject. Each subject has its data in different/ multiple rows. | The repeated responses of a subject are part of separate columns |
| To recognize data, we can consider rows as groups. | Here we can recognize data by considering columns as groups |
| This is generally used in R data analysis and writing log files after trials | Commonly used in stats packages for repeated measures ANOVAs not in R analysis |
Examples are as follows:
Wide Format:
| Country | Capital | Currency |
| India | Delhi | Rupee |
| China | Beijing | Renminbi |
Long format:
| Country | Attributes | Entry data |
| India | Capital | Delhi |
| India | Currency | Rupee |
| China | Capital | Beijing |
| China | Currency | Renminbi |
Q4. What do you mean by Eigenvectors and Eigenvalues?
Ans. Eigenvectors are unit vectors or column vectors whose magnitude/length is 1. They are also known as right vectors. Eigenvalues are the coefficients assigned to these eigenvectors, giving these vectors varied values for magnitude or length.
Q5. You are given a data set containing variables with more than 30 percent missing values. How will you deal with them?
Ans. The following are ways to handle missing data values:
If the data set is huge, we can remove the rows with missing data values. It is the quickest way; we use the rest of the data to predict the values.
We can substitute missing values for smaller data sets with the mean or average of the rest of the data using the pandas’ data frame in Python. There are different ways to do so, such as df. Mean (), df. Fill (mean).
Q6. How should you maintain a deployed model?
Ans. The steps to maintain a deployed model are as follows:
Monitor You should consistently monitor to guarantee the good performance of all models. To evaluate the efficiency of the data, you must modify it over regular intervals of time.
Evaluate the performance. You must use evaluation metrics for new algorithms on the current model. This process will help you find model effectiveness and find where adjustments are needed.
Compare To determine which model offers the best performance; various models are compared. Comparing several models simplifies deciding which is the most effective and efficient. You must compare various models to know the best model. It becomes easy for you to make effective decisions regarding a model selection.
Rebuilt You can create a top-performing model which can be constructed using the most recent data. By doing this, the model is kept current and optimized for exact outcomes.
Q7.How do you find RMSE and MSE in a linear regression model?
Ans. RMSE and MSE are two of the most common accuracy measures for a linear regression model.
RMSE indicates the Root Mean Square Error.
MSE indicates the Mean Square Error.
Q8. How can you select k for k-means?
Ans. The elbow method to select k for k-means clustering. The idea of the elbow method is to run k-means clustering on the data set, and ‘k’ is the number of clusters. The sum of squares (WSS) is defined as the sum of the squared distance between each cluster member and its centroid.
Q9. What is the significance of the p-value?
Ans. p-value typically ≤ 0.05
This signifies strong evidence against the null hypothesis, so you reject the null hypothesis.
p-value typically > 0.05
This implies weak evidence against the null hypothesis, so you accept the null hypothesis.
p-value at cutoff 0.05
This is considered to be marginal, meaning it could go either way.
Q10. Write a basic SQL query that lists all orders with customer information.
Ans. Usually, we have order tables and customer tables that contain the following columns:
- Order Table
- Orderid
- customerId
- OrderNumber
- TotalAmount
- Customer Table
- Id
- FirstName
- LastName
- City
- Country
- The SQL query is:
- SELECT OrderNumber, TotalAmount, FirstName, LastName, City, Country
- FROM Order
- JOIN Customer
- ON Order.CustomerId = Customer.Id
Q11. Define and explain selection bias.
Ans. The selection bias occurs in the case when the researcher has to make a decision on which participant to study. The selection bias is associated with those researches when the participant selection is not random. The selection bias is also called the selection effect. The selection bias is caused by as a result of the method of sample collection.
Four types of selection bias are explained below:
- Sampling Bias: As a result of a population that is not random, some population members have fewer chances of getting included than others, resulting in a biased sample. This causes a systematic error known as sampling bias.
- Time interval: Trials may be stopped early if we reach extreme value. However, if all variables are similar invariance, the variables with the highest variance have a higher chance of achieving the extreme value.
- Data is when specific data is selected arbitrarily, and the generally agreed criteria are not followed.
- Attrition: Attrition in this context means the loss of the participants. It is the discounting of those subjects that did not complete the trial.
Q12. What do you mean by confusion matrix?
Ans. A confusion matrix is a table summarising the outcomes of predictions and is used to assess the effectiveness of a classification model. For a thorough evaluation of the model’s accuracy, precision, recall, and other performance measures, it breaks into the true positive, true negative, false positive, and false negative predictions.
Also read: 10+ Most Commonly Used Data Science Techniques in 2023
Q13. What is a gradient and Gradient Descent?
Ans. The gradient is the measure of a property of how much the output has changed with respect to a little change in the input. In other words, it is a measure of change in the weights with respect to the change in error. The gradient can be mathematically represented as the slope of a function.
Q14. What are dimensionality reduction and its benefits?
Ans. Dimensionality reduction refers to the process of converting a data set with vast dimensions into data with fewer dimensions (fields) to convey similar information concisely. This reduction helps in compressing data and reducing storage space. It also reduces computation time as fewer dimensions lead to less computing. It removes redundant features; for example, there’s no point in storing a value in two different units (meters and inches).
Q15. What are recommender systems?
Ans. A recommender system predicts how users rate a specific product based on their preferences. It can be split into two different areas:
Collaborative Filtering
As an example, and Last. Fm recommends tracks that other users with similar interests play often. This is also commonly seen on Amazon after making a purchase; customers may notice the following message accompanied by product recommendations: “Users who bought this also bought…”
Content-based Filtering For example, Pandora uses the properties of a song to recommend music with similar properties. Here, we look at content instead of looking at who else is listening to music.
Q16. What is the ROC curve?
Ans. The graph between the True Positive Rate on the y-axis and the False Positive Rate on the x-axis is called the ROC curve and is used in binary classification. The False Positive Rate (FPR) is calculated by taking the ratio between False Positives and the total number of negative samples, and the True Positive Rate (TPR) is calculated by taking the ratio between True Positives and the total number of positive samples. The TPR and FPR values are plotted on multiple threshold values to construct the ROC curve. The area range under the ROC curve ranges between 0 and 1. A completely random model, which is represented by a straight line, has a 0.5 ROC. The amount of deviation a ROC has from this straight line denotes the model’s efficiency.
Q17. Why is Python used for Data Cleaning in DS?
Ans. Data Scientists must clean and transform huge data sets into a form they can work with. It is important to deal with redundant data for better results by removing nonsensical outliers, malformed records, missing values, inconsistent formatting, etc. Python libraries such as Matplotlib, Pandas, Numpy, Keras, and SciPy are extensively used for Data cleaning and analysis. These libraries are used to load and clean the data and do effective analysis. For example, a CSV file named “Student” has information about the students of an institute, like their names, standard, address, phone number, grades, marks, etc.
Q18. What is k-fold cross-validation?
Ans. In k-fold cross-validation, we divide the dataset into k equal parts. After this, we loop over the entire dataset k times. In each iteration of the loop, one of the k parts is used for testing, and the other k − 1 parts are used for training. Using k-fold cross-validation, each of the k parts of the dataset is used for training and testing purposes.
Q19. What is an RNN (recurrent neural network)?
Ans. A recurrent neural network, or RNN for short, is a kind of Machine Learning algorithm that uses an artificial neural network. RNNs are used to find patterns from a data sequence, such as time series, stock market, temperature, etc. RNNs are a kind of feedforward network in which information from one layer passes to another layer, and each node in the network performs mathematical operations on the data. These operations are temporal, i.e., RNNs store contextual information about previous computations in the network. It is called recurrent because it performs the same operations on some data every time it is passed. However, the output may differ based on past computations and their results.
Q20. What is a kernel function in SVM?
Ans. In the SVM algorithm, a kernel function is a special mathematical function. Put, a kernel function takes data as input and converts it into a required form. This transformation of the data is based on something called a kernel trick, which is what gives the kernel function its name. Using the kernel function, we can transform the data that is not linearly separable (cannot be separated using a straight line) into one that is linearly separable.
Q21. What does the word ‘Naive’ mean in Naive Bayes?
Ans. Naive Bayes is a Data Science algorithm. It has the word ‘Bayes’ in it because it is based on the Bayes theorem, which deals with the probability of an event occurring, given that another event has already occurred. It has ‘naive’ in it because it assumes that each variable in the dataset is independent of the other. This kind of assumption is unrealistic for real-world data. However, even with this assumption, it is very useful for solving a range of complicated problems, e.g., spam email classification, etc.
Q22. What is the Computational Graph?
Ans. A directed graph with variables or operations as nodes is a computational graph. Variables can contribute to operations with their value, and operations can contribute their output to other operations. In this manner, each node in the graph establishes a function of the variables.
Q23. What is the difference between Batch and Stochastic Gradient Descent?
Ans. The difference between Batch and Stochastic Gradient Descent is as follows:
| Batch | Stochastic Gradient Descent |
| Provides assistance in calculating the gradient utilizing the entire set of data. | Helps in calculating the gradient using only a single sample. |
| Takes time to converge. | Takes less time to converge. |
| The volume is substantial enough for analysis. | The volume is lower for analysis purposes. |
| Updates the weight infrequently. | Updates the weight more frequently. |
Q24. What is an Activation function?
Ans. An activation function is a function that is incorporated into an artificial neural network to help in the network’s learning of complicated patterns in the input data. In contrast to a neuron-based model in human brains, the activation function determines what signals should be sent to the following neuron at the end.
Q25. Write the equation and calculate the precision and recall rate.
Ans. Precision = (True positive) / (True Positive + False Positive)
Recall Rate = (True Positive) / (Total Positive + False Negative)
Q26. What is survivorship bias?
Ans. Survivorship bias is the logical error of focusing on aspects that support surviving a process and casually overlooking those that did not because of their lack of prominence. This can result in wrong conclusions in various ways.
Q27. How do you work towards a random forest?
Ans. The underlying principle of this technique is that several weak learners combine to provide a strong learner. The steps involved are:
Build several decision trees on bootstrapped training samples of data.
Each time a split is considered on each tree, a random sample of mm predictors is chosen as split candidates out of all pp predictors.
Rule of thumb: At each split, m=p√m=p
Predictions: At the majority rule
This exhaustive list is sure to strengthen your preparation for data science interview questions.
Q28. What is the difference between a box plot and a histogram?
Ans. The frequency of a certain feature’s values is denoted visually by both box plots.
And histograms. Boxplots are more often used in comparing several datasets and, compared to histograms, take less space and contain fewer details. Histograms are used to know and understand the probability distribution underlying a dataset.
Q29. Difference between an error and a residual error
Ans. The difference between a residual error and an error is defined below –
| Error | Residual Error |
| The difference between the actual and predicted values is called an error.
Some of the popular means of calculating data science errors are –
|
The difference between the arithmetic mean of a group of values and the observed group of values is called a residual error. |
| An error is generally unobservable. | A residual error can be represented using a graph. |
| A residual error is used to show how the sample population data and the observed data differ from each other. | An error is how actual population data and observed data differ from each other. |
Q30. Difference between Normalisation and Standardization
Ans:
| Standardization | Normalization |
|
|
|
|
X’ = (X – Xmin) / (Xmax – Xmin) Here, Xmin – feature’s minimum value, and Xmax – feature’s maximum value. |
X’ = (X – 𝞵) / 𝞼 |
Top Data Science Interview Questions For Preparation
Q 31. Describe data science.
Ans: Statistics, algebra, specialized programming, artificial intelligence, machine learning, and other disciplines are combined in data science. Data Science is basically the application of certain ideas and analytic procedures to extract information from data for use in strategic planning, decision-making, and other similar applications. Simply put, data science is the analysis of data to get meaningful insights.
Q.32. What is a Decision Tree?
Ans: In a system, decision trees are used to categorize input and assess the likelihood of specific outcomes. The root node is the tree’s foundation. Based on the different options that might be taken at each level, the root node branches out into decision nodes. Decision nodes are connected to lead nodes, which reflect the outcomes of each decision.
Q.33. What is the Difference Between Supervised and Unsupervised Learning?
Ans: The nature of the training data provided to supervised and unsupervised learning systems differs. Supervised learning necessitates labeled training material, but unsupervised learning allows the system to detect trends in unlabeled data.
Q.34 What is Logistic Regression?
Ans: A type of predictive analysis is logistic regression. It employs a logistic regression equation to determine the associations that exist between a dependent binary variable and one or more independent variables.
Q35. Explain the K-Fold Cross-Validation.
Ans: Cross-validation is a technique for estimating the effectiveness of a machine-learning model. The parameter k is a count of the number of groups into which a dataset can be divided.
The method begins with a random shuffle of the whole dataset. It is then separated into k groups, which are referred to as folds. The following is a list of possible candidates:
- Assign one fold to be the test fold and the remaining k-1 folds to be the test set.
- Start training the model on the practice set. Train a new model that is independent of the models used in previous rounds for each cross-validation cycle.
- Validate the model against the test data and store the results of each iteration.
- The ultimate score is calculated by averaging the outcomes of each iteration.
Q 36. Describe the Random Forest Model. How Does One Create a Random Forest Model?
Ans: A random forest model is a type of supervised learning and a machine learning technique. It is most frequently utilized in regression and classification issues. The following are the steps for creating a random forest model:
- Choose n from a dataset of k records.
- Create a separate decision tree for each n data value under evaluation. A projected outcome is obtained from each of them.
- Each of the outcomes is subjected to a voting process.
- The prediction with the most votes is chosen as the final outcome.
Q 37. How Would You Handle a Dataset Missing More Than 30% of Its Values?
Ans: The method will be determined by the amount of the dataset. If the dataset is vast, the simplest technique would be to eliminate the rows with missing values. Because the dataset is vast, this will have no effect on the model’s capacity to deliver results. If the dataset is small, just removing the values is impractical. In that scenario, it is preferable to compute the mean or mode of that specific characteristic and enter that number where there are blanks. Another approach would be to forecast the missing data using a machine learning system. This can produce reliable findings unless there are entries with a very large deviation from the rest of the dataset.
Q38. What is K-Means Clustering?
Ans: K-means is an unsupervised learning method that is used to solve data clustering challenges. It goes through the processes outlined below:
- Select the number of clusters to build and set it to k.
- Pick k locations at random from the dataset to act as the centroids.
- Take each data point and group it with the centroid that is closest to it. This results in the creation of k clusters.
- Compute the dataset’s variance and assign a new centroid to each cluster as needed.
- Repeat the third step by reassigning the new centroids to each data point.
- If any reassignments have occurred, repeat the fourth step. If not, the model is complete.
Q 39. How Do You Choose K for K-Means?
Ans: The elbow technique is the most often used way for determining k for the k-means algorithm. To do so, compute the Within-Cluster-Sum of Squared Errors (WSS) for various k values. The WSS is defined as the square of the distance between each data value and its centroid.
You will next select the value of k at which the WSS error becomes minimal.
Q 40. Describe the ROC Curve.
Ans: The ROC curve is a graph that shows how a classification model performs at various classification levels. The True Positive Rate (TPR) is displayed on the y-axis, while the False Positive Rate (FPR) is plotted on the x-axis.
The TPR is calculated by dividing the number of true positives by the total of true positives and false negatives. The FPR is defined as the ratio of false positives in a dataset to the sum of false positives and true negatives.
Q 41. What is a Confusion Matrix?
Ans: A confusion matrix is utilized to assess the effectiveness of a classification method. It is employed because a classification method is ineffective when there are more than two classes of data or when the number of classes is not even.
The following is the procedure for producing a confusion matrix:
- Build a validation dataset with certain expected values as outputs.
- Predict the outcome for each row included in the dataset.
- For each class, tally the number of accurate and erroneous guesses.
- Make a matrix from the data, with each row representing a projected class and each column representing an actual class.
- Complete the table with the counts acquired in the third step.
Q.42. Describe Ensemble Learning?
Ans: Ensemble learning is a machine learning approach in which many models are employed to improve a data analysis model’s prediction performance.
Q.43. What Do You Mean by “Bagging”?
Ans: Bagging is an ensemble learning approach for reducing variance in a noisy dataset.
Q.44. Describe the concept of boosting in data science?
Ans: Boosting is an ensemble learning strategy that is used to improve a poor learning model.
Q.45. Explain the concept of Naive Bayes?
Ans: Naive Bayes is a classification technique that assumes each feature under evaluation is independent. It is dubbed naïve because of the very same assumption, which is often impractical for facts in the actual world. Yet, it does seem to function well to solve a vast range of issues.
Also read: The Role of Data Science in Healthcare Industry in 2024
Q.46. Explain Linear Regression?
Ans: Linear regression is a rapid predictive analytic method. A house’s price, for example, is determined by a variety of criteria, including its size and location. To see the link between these variables, you may create a linear regression, which predicts the line of best fit and can help you determine if these two variables are related positively or negatively.
Q.47. What are the assumptions for a Linear Regression?
Ans: Four important assumptions are made.
- The dependent variables and regressors have a linear relationship, indicating that your model matches the data.
- The errors or residuals of the data are normally distributed and independent from each other.
- There is little multicollinearity among explanatory factors.
- Homoscedasticity (the variation around the regression line) is the same for all predictor variable values.
Q.48. What is the Purpose of R in Data Visualization?
Ans: R contains a large number of data visualization packages. They need a minimal bit of code, which is why R is a popular language for data visualizations.
Q.49. List Some Popular Data Science Libraries?
Ans: TensorFlow, Matplotlib, Keras, SciPy, and PyTorch are prominent data science libraries.
Q.50. What is Data Science Variance?
Ans: Variance is the distance between a value in a dataset and the mean value.
Q.51. Explain Feature Vectors?
Ans: A feature vector specifies the characteristics of an item.
Q.52. Describe the concept of Root Cause Analysis?
Ans: The practice of using data to find the underlying trends driving a certain change is known as root cause analysis.
Q.53. What is an example of a Non-Gaussian Distribution Data Set?
Ans: A population’s height distribution is one illustration of this.
Q.54. What is the Definition of Collaborative Filtering?
Ans: Collaborative filtering is a type of content filtering that makes suggestions based on commonalities between various users.
Q.55. Why is A/B testing done?
Ans: Businesses may use A/B testing to gain insight into their consumers’ brains and learn about their preferences. You can measure the interest that various options generate in test groups, allowing you to launch the final product confidently.
Q.56. What is the Principle of Large Numbers?
Ans: This probability rule suggests that in order to obtain near to an expected result, you need to repeat an experiment several times, each independently of the other, and then average the results.
Q.57. Discuss the concept of confounding variables.
Ans: While attempting to analyze the link between a cause and its alleged effect, you may come across a third variable that has an impact on both the cause and the effect. This is referred to as a confusing variable.
Q.58. What is a Star Schema?
Ans: A star schema is a manner of arranging a database that stores measurable data in a single fact table. The primary table is termed a star schema because it resides at the heart of a logical diagram, while the smaller tables branch off like nodes in a star.
Q.59. What Is the Difference Between an Eigenvalue and an Eigenvector?
Ans: An eigenvector generates a vector in the same direction but with a larger magnitude. The eigenvalue is a measure that determines the degree to which the eigenvector becomes scaled up.
Q.60. Why is sampling done?
Ans: Sampling is a statistical strategy that analyses a representative selection of a larger dataset to identify patterns.
Also read: The Role of Data Science Engineers in Data Science 2023
Conclusion
This may appear to be an invitation to discuss all you know about data science. Still, it isn’t. Rather, recruiters want to see if you grasp the discipline’s underpinnings and how it fits into a commercial setting. To begin, define data science. Explain why it has grown in popularity as a field and how businesses might profit from it. If feasible, personalize this response to the organization where you’re interviewing and explain how data science may be utilized to tackle the sorts of problems they’re looking for answers to.
