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15 Frequently Asked Data Scientist Interview Questions

15 Frequently Asked Data Scientist Interview QuestionsData science is used in a wide range of industries, including healthcare, finance, marketing, social media, and e-commerce, among others. It has become an essential part of modern businesses and organizations, as they seek to gain a competitive advantage and improve their decision-making processes by influencing the power of data.

If you are preparing for a data scientist interview, reading Data Scientist Interview Questions can be highly helpful. The article provides a list of 15 frequently asked data scientist interview questions that cover various aspects of the data science field. By reading and studying these questions, you can gain a better understanding of what to expect during a data scientist interview and prepare yourself accordingly. Moreover, the article provides detailed answers to each question, which can help you develop a strong and exhaustive support in the interview.

15 Frequently Asked Data Scientist Interview Questions & Answers

Q#1. What is Data Science?

Answer: Data science is an analytical method for information extraction from the data used in decision-making, strategic planning, etc. Data science refers to data analysis for actionable insights. Machine learning, artificial intelligence, specialized programs, math, and statistics are combined and included in data science.

Q#2. What is the difference between data science and traditional application programming?

Answer: The major difference between data science and traditional application programming is that in data science, we don’t have to create rules to convert the input to output, but rules will be automatically created from data in data science. Data science is about analyzing data to extract insights and knowledge and its main focus is on data interpretation and analysis, while traditional application programming is about building software applications that perform specific functions and it primarily focuses on software application development.

Q#3. Differentiate between supervised and unsupervised learning?

Answer: Below is the differences between Supervised and unsupervised learning:

Supervised Learning

Unsupervised Learning

  • Labeled and known data are used by supervised learning as an input.
  • Unlabeled data are used as input for unsupervised learning.
  • There is a feedback method in supervised learning.
  • There is no feedback method in the unsupervised learning
  • Mostly used algorithms of supervised learning are support vector machines, logistic regression and decision trees.
  • Mostly used algorithms of unsupervised learning are hierarchical clustering, k-means clustering and apriori algorithm 

Q#4. What are the steps involved in creating the decision tree?

Answer: Following steps will be followed for making decision tree:

Step#1: Take input of the entire data set

Step#2: Find Target variable entropy and predictor attributes also.

Step#3: Find all attribute information gain

Step#4: And the attribute having the highest information gain selects that as a root node.

Step#5: Repeat the similar process on all the branches till the decision node of every branch is finalized.

Q#5. How can overfitting be avoided for your model?

Answer: Overfitting refers to a model which ignores the bigger picture and only sets for very small data amounts. Overfitting can be avoided in three ways:

1) Cross-validation techniques can also be used like the k-fold cross-validation method.

2) Regularization methods can also be used like LASSO by which some parameters of the model are penalized if they are likely to cause overfitting in the model.

3) Training with more data: Training data with more data is one of the ways of preventing overfitting. By this method, the better signal detection becomes easy for algorithms for minimizing errors. As more training data is feeded by the user into the model, it is not possible to overfit all the samples and for obtaining results it will be forced to generalize.

  • Data simplification: Model complexity is one of the reasons for overfitting. Data simplification is a way used for reducing overfitting by reducing the model complexity and making the model simple and easy so it does not lead to overfitting.
  • Ensembling: Ensembling is another method of preventing overfitting. It is a machine learning method that works by combining two or more model predictions. Boosting and bagging are the most commonly used ensemble techniques.

Q#6. Can you explain the methods used for feature selection for choosing the right variables?

Answer: For feature selection, there are two methods i.e. Wrapper and Filters methods.

Filter Methods

ANOVA, chi-Square, and Linear discrimination analysis are included in the fitter methods. For selecting features the best strategy is “bad data in, bad answer out”. At the time of selecting or limiting the features, it is all about the coming in data cleaning.

Wrapper Methods

Forward selection, backward selection and recursive features are included in the wrapper methods.

  • Forward Selection: One feature is tested at a time and until we get a good fit we keep adding them.
  • Backward Selection: All the features are tested and then begin to remove to find which will perform better.
  • Recursive Feature Elimination: All the different features are looked at recursively and find how they can be paired with each other.

Wrapper methods are considered labour-intensive types of methods and in the case of huge data analysis wrapper methods require high-end computers.

Q#7. How can the deployed model be maintained?

Answer: The following steps will be followed for maintaining the deployed model.

  • Monitor: For determining the performance accuracy of every model, constant monitoring is required. When some modifications are done by you, then you need to find out how these modifications will affect things. For ensuring the doing of modification and what it is expected to do it is required to Monitor it.
  • Evaluate: Current model evaluation metrics are evaluated for determining if a new algorithm is required.
  • Compare: For finding which model is performing best it is required to compare new models with exact others.
  • Rebuild: The model performing the best is rebuilt in the current data State.

Q#8. Can you explain recommender systems?

Answer: A recommender system is a system which makes a prediction of what the user will provide the rating for a particular product based on their preferences. It is divided into 2 different areas: Content-based Filtering & Collaborative Filtering.

Content-Based Filtering

Content-Based Filtering is a type of recommendation, in which relevant items are displayed to the users according to the content of the previous searches of the user. And here the content of previous searches means the tag or attribute of the products for which the user has searched and which products are liked by the user. Certain keywords are used for the product tags, and with the help of these tags, the system tries to understand what the user desires and checks out its database and then tries to provide the best recommendation to the user for the products they want. 

Let’s understand the content-based recommendation system with the help of an example. We are taking an example of the movie recommendation system and genres associated with each movie, or we can consider genre as tag/attributes. Now user X has arrived and the system does not have any data of user X. Firstly the system will recommend the most popular and most watched movies to the user and then the system will try to gather some information based on feedback given by the user for the recommended movie.

A user has given ratings to the recommended movies and he has given good ratings to the comedy genre movies and has given bad ratings to the horror genre movies. So now based on the ratings of the user, the system will recommend the comedy genre movies to the user. 

Collaborative Based Filtering

Collaborative-based filtering is a recommendation system, in which new items are recommended based on other similar user’s interests and preferences. For instance, if we buy or search for an item on Amazon then it will give recommendations by the heading “Customer who bought this also bought this”

Collaborative filtering is of two types:

  1. User-Based Collaborative Filtering
  2. Item-based Collaborative Filtering

Q#9. How will the value of k be selected for k-means?

Answer: For choosing k for k-means clustering, we use the elbow method. The idea used behind the elbow method is running k-means clustering in the given data set and here ‘k’ denotes the number of clusters.

Q#10. What is the significance of the p-value?

Answer: The p-value is a statistical metric that evaluates the strength of evidence against a null hypothesis, helping to assess whether the results of a study or experiment are statistically significant or due to chance.

  • p-value typically ≤ 0.05

Strong evidence against the null hypothesis is indicated by it, so the null hypothesis can be rejected.

  • p-value typically > 0.05

Weak evidence against the null hypothesis is indicated by it, so the null hypothesis can be accepted.

  • p-value at cutoff 0.05 

It indicates marginal, so we can take it either way.

However, the p-value is not the only measure of the strength of evidence against a null hypothesis, and it should not be used in isolation to make decisions or get conclusions. It should be considered in conjunction with other measures, such as effect size, confidence intervals, and practical significance.

Q#11. Can you define dimensionality reduction and its advantages?

Answer: Dimensionality reduction is the technique in which there is a conversion of a data set having vast dimensions into a data set having fever dimensions so that it can display the same information but in a concise form.

This method is very useful in reducing storage space and it can also compress the data. As fewer dimensions take less time in computing, it reduces computation time also. Redundant features were also removed. For example, there is no need to store the same value in two different units such as meters and inches.

Q#12. What is the true positive rate and false positive rate?

Answer: The true positive rate (TPR) and false positive rate (FPR) are two important measures used to evaluate the performance of a binary classification model.

TRUE-POSITIVE RATE: The true positive rate, also known as sensitivity or recall, is the proportion of actual positive cases that are correctly identified by the model as positive. We can also use it to find out the actual positive percentage that is verified accurately.

In other words, the probability which shows how the actual positive will turn into a positive is known as the True Positive Rate (TPR). The ratio between the {True Positives (TP)} and the {{True Positive(TP)} and the {False Negatives(FP)}} is used to calculate the True Positive Rate (TPR).

The formula for calculating the same is given below:


Where TP represents true positives (cases that are actually positive and correctly identified as positive by the model) and FN represents false negatives (cases that are actually positive but incorrectly identified as negative by the model).


The incorrect prediction proportion of the positive class is given by the false-positive rate. We can also use it to find out something that is true, but initially that is false.

The probability which shows how the actual negative will turn into a positive is known as the False Positive Rate (FPR). The ratio between the {False Positives (FP) }and the {{True Positives(TP)} and the {False Positives (FP)}} is used to calculate the False Positive Rate (TPR).

The formula for calculating the same is given below:


Where FP represents false positives (cases that are actually negative, but incorrectly identified as positive by the model) and TN represents true negatives (cases that are actually negative and correctly identified as negative by the model).

Q#13. What do you understand by the confusion matrix?

Answer: The summary of the prediction results related to a particular problem is known as the confusion matrix. It is in the form of a table and is used to explain the model performance. It is a matrix of size n*n and also evaluates the performance of the classification model.

Q#14. Can you explain the ROC curve?

Answer: The ROC curve is formed when there is a graph between the true positive rate on the y-axis and the false positive rate on the x-axis. It is implemented in binary classification.

For calculating False Positive Rate( FPR) take the ratio between the false positive and the total negative samples and for calculating True Positive Rate (TPR) take the ratio between the true positive and the total positive samples.

We need to plot the TPR and FPR values on the various threshold values so that the ROC curve can be designed. The area plotted in the ROC curve must be in the range between 0 and 1. ROC 0.5 represents a completely random model and it is displayed by a straight line. The efficiency of the model is denoted by how much the ROC deviated from this straight line.

Q#15. How to treat outlier values?

Answer:  If the outlier is a garbage value, then you can drop it. For example, the height of an adult is abc ft. But it cannot be true, as the value of height can never be in the form of a string. So, in this condition outliers will drop.

The outliers can be dropped if they have extreme values. For instance, all the data points are arranged in the range between 0 to 10 but if one point lies at 100, then this point can be dropped.

If due to some reason you are not able to remove the outliers, then you can implement the following:

  • Normalizing the data can also be used. In this technique, all the extreme points are pulled so that they can be set in the same defined range.
  • You can also implement those algorithms that are not so affected by the outliers. For example, random forests.
  • Try to implement it by selecting a different model. As sometimes linear model detected outlier’s data can be fitted by the non-linear model. So always ensure to select the correct model.


In this article, we have studied a list of 15 frequently asked data scientist interview questions along with detailed answers and explanations. It involves questions related to recommender systems, prevention of overfitting, significance of p-value, etc. By studying these questions and their answers, you can gain a better understanding of the skills and knowledge required for a career in data science.

In conclusion, preparing for a data scientist interview can be a tremendous task, especially if you are unsure of what to expect. However, by familiarizing yourself with commonly asked data scientist interview questions, you can increase your confidence and improve your chances of success.

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