#
**Top 45+ Data Science Interview Questions**

Data Science is a field of multiple expertise. It is a combination of various disciplines such as statistics, mathematics, artificial intelligence, machine learning, and many more.

Taking into consideration the massive amount of data that there is in every business nowadays, data scientists have become extremely important for any company’s functioning. The demand for Data Science will only grow in the future, you can opt for a data science course to increase your proficiency in the field.

In this blog, you will get to know the answers to various Data Science interview questions that will help you ace your interviews.

## Data Science Interview Questions for Freshers

When we talk of Data Science interview questions for freshers, it is important to keep in mind that freshers are not experienced in the field and therefore the interviewers tend to stick to the basics. Here are some of the important Data Science interview questions for freshers:

### 1. Explain the building of a random forest model.

When the data is split into groups, each set makes a decision tree. The role of a random forest model is to get the trees from different groups of data and combine them all. The following are the steps to build a random forest model.

- Select ‘k’ features from the total number of ‘m’ features. Make sure that k<m.
- Calculate node ‘d’ from among the ‘k’ features while using the best-split point.
- Split the nodes into daughter nodes.
- Finalize the leaf nodes.
- Build the random forest model by repeating all the steps to create the trees.

### 2. What will you do if your model is overfitting?

A model is overfitted if it is set for just a little amount of data and it loses sight of the big picture that the data is supposed to display to infer useful insights for the company. There are ways to avoid overfitting your model, those can be:

- Take a small number of variables into account and keep the model as simple as possible.
- Use the technique of cross-validation.
- Use LASSO regularization techniques to avoid issues like overfitting.

### 3. What is univariate analysis?

Univariate takes into consideration only one variable to find various patterns in the data.

For example, the marks of all students in French. Once the marks are fed into the system, the conclusions can be taken out by using methods such as mean, median, mode, etc.

### 4. What is bivariate analysis?

This type of analysis takes into account two sorts of variables to highlight the relationships and causes between the two variables.

For example, sales of woolen clothes in the winter season according to the temperature. If the temperature is less, the woolen clothes sell more on those days and when it is more, the woolen sales drop down. According to the table and the variables of the two, Bivariate analysis will give you such conclusions.

### 5. What is multivariate analysis?

This type of analysis involves more than two variables to take out conclusions and see the relationship between the variables. You can again give a similar example for multivariate analysis as you give for the two analyses above.

### 6. How will you select the right variables?

To choose the appropriate variables, use the feature selection method. This gives out two methods for the process of feature selection which are:

- Filter method- This method involves Linear discrimination analysis, ANOVA, and Chi-Square.
- Wrapper Methods- This method involves Forward Selection, Backward Selection, and Recursive Feature Elimination.

### 7. If your data set is missing a lot of values, what will you do?

If the data is large, then we can remove the rows that have the missing data and predict the values with the data that is already given. It is the easiest method and quickest way to deal with the problem. But when the data is small, and limited, then pandas’ data frame can be used and the missing values can be substituted by the average of the data that is still present.

### 8. What is dimensionality reduction?

The process of dimensionality reduction is used when the user wants to convert a data set with large dimensions to a data set with lesser dimensions. Both sorts of data sets give out the same amount of information. The benefit is that the storage will be less and it will reduce the computation time.

### 9. In what ways would you maintain a deployed model?

There are various ways to maintain a deployed model:

- Monitor
- Evaluate
- Compare
- Build

### 10. How would you define recommender systems?

Recommender systems refer to a class of algorithms and techniques used to make personalized recommendations to users based on their past behavior. These systems are widely used in e-commerce, social media, and other industries to help improve customer engagement and satisfaction.

### 11. Differentiate between supervised and unsupervised learning.

Supervised Learning | Unsupervised Learning |

Uses labeled data as input. | Uses unlabeled data as input. |

It has a feedback mechanism. | It has no feedback mechanism. |

### 12. Explain logistic regression.

Logistic regression is used to estimate the probability to measure the relationship between one dependent variable or variables using the underlying logistic function.

### 13. How would you select the right variables?

The user should first choose feature selection and under feature selection, there will be two methods: filter method and wrapper method. The filter method involves selecting features based on their statistical properties. It is computationally efficient and can be used to quickly identify the most important features in a dataset.

The wrapper method involves selecting features based on how well they improve the performance of a specific machine-learning model. It is trained and tested on different subsets of features, that give the best performance is selected.

### 14. What do you mean by dimensionality reduction?

Dimensionality reduction is a method that turns a dataset with huge dimensions to a dataset with lesser dimensions. It helps to compress the data.

### 15. How will you select k for k-means?

The user will use the elbow method to run k-means clustering on a data set where the number of clusters is k.

### 16. How will you treat outliers values?

The outlier values can be dropped if only it’s a garbage value. You can also try a different model, try to normalize the data, and use algorithms that are not that much affected by outliers.

### 17. By using a confusion matrix calculate accuracy.

Here you will have to give an example of a matrix and apply the formula:

Accuracy = (True Positive + True Negative) / Total Observations

### 18. With the help of an equation, calculate the precision and the recall rate.

The formula for precision that you will be using is

Precision = (True positive) / (True Positive + False Positive)

The formula for recall rate that you will be using is:

Recall Rate = (True Positive) / (Total Positive + False Negative)

**Get an assured Job guarantee by enrolling in our data science placement guarantee course.**

## Data Science Interview Questions for Intermediates

All the questions discussed above were technical questions that could be asked in an interview to determine the knowledge of the candidate. But apart from these, other questions can be asked by the interviewer to assess your experience and expertise in the field. Let’s see the type of questions for intermediates:

### 19. According to you, what is the most important skill one should have to become a data scientist?

The skills one should have are:

- Statistical analysis
- Computing
- Deep Learning
- Data Visualization
- Programming
- Processing data

### 20. Which machine learning algorithm do you usually prefer and why?

Following are some AI algorithms that you can talk about:

- Linear Regression
- Logistic Regression
- Decision Tree
- Support Vector Machine
- K-Means

### 21. What information about the customer should be there in the SQL query?

The details should include the following:

- Order Table
- OrderID
- CustomerId
- OrderNumber
- TotalAmount
- Customer Table
- ID
- First name
- Last name
- City
- Country

### 22. What is ROC Curve?

ROC is the graph between the True Positive Rate on the y-axis and the False Positive Rate on the x-axis.

### 23. What is Long Format Data?

In a long format data, the values keep on repeating in the first column and every row is a one-time point per subject.

### 24. What is Wide Format Data?

Wide format data refers to a type of data organization where each row represents a single observation, and each column represents a single variable or feature. It is used in datasets where the number of variables is relatively smaller as compared to the number of observations.

### 25. What is a variance?

The variance shows the individual figures in a set of data. It is used by data scientists to get an idea about the distribution of a data set.

### 26. Explain pruning in a decision tree algorithm.

Pruning reduces the complexity of a decision tree by reducing the rules. It also helps with the accuracy of the decision tree.

### 27. What is the Normal distribution?

Normal Distribution displays data near the mean and frequency of the data that is present at the time. It appears in the shape of a bell curve on the graph and it includes various parameters like Mean, Median, and Mode.

### 28. What is Deep Learning?

Deep Learning is built up like human thoughts as it contains algorithms that are similar to the human mind.

### 29. What is RNN?

Recurrent Neural Network (RNN) uses sequential data in language translation, voice recognition, image capture, and more. Google’s voice search and Apple’s Siri use the RNN algorithm.

### 30. What do you mean by feature vectors?

When it comes to machine learning, feature vectors stand for numeric characteristics in a mathematical way so that it becomes easy for the data scientist to analyze the features.

### 31. How would you make a decision tree?

The following steps can be taken to make a decision tree:

- Turn data into input.
- Use split to separate the data into classes.
- Apply the split method to the data again.
- Apply the last two steps on the data again to further divide the data.
- When you meet a stopping criteria, you can stop then.
- If you have split too far then clean up the tree.

### 32. What is the objective of A/B testing?

A/B testing is used to see if there are any changes in a web page so that the outcome of a strategy can be maximized to its fullest capacity.

### 33. What are the disadvantages of linear models?

Following are the disadvantages:

- The assumption of the linearity of errors.
- It can not count the binary outcomes.
- It can not solve overfitting problems.

### 34. When should an algorithm be updated?

An algorithm should be updated when:

- The underlying data source is changing.
- There is a case of non-stationary.

### 35. What is selection bias?

Selection bias is used in case of a problematic situation where an error is introduced because of a non-random population sample,

## Data Science Interview Questions for Advanced Candidates

The above Data Science interview questions were only for the freshers. Apart from these questions, other various questions can be asked if you are applying for a proper data science job.

### 36. By using a confusion matrix, how will you be calculating the accuracy?

Take a simple example for calculating accuracy. Once you have thought of an example, simply put the formula:

Accuracy = (True Positive + True Negative) / Total Observations.

### 37. The recommendation engine is a result of which algorithm?

The answer to this Data Science interview question will be collaborative filtering which, by observing the user behavior, makes predictions about what the user would like to buy next. You can also give related examples to enhance your answer.

### 38. Explain the Confusion Matrix.

A confusion Matrix is a table that summarizes the predictions of a problem that has been defined. It is also used to evaluate the performance of classification models.

### 39. Explain TPR.

True-positive rate is a way to measure the verified positives taken out of the proportion of the correct predictions of the positive class.

### 40. Explain FPR.

The false-positive rate measures something that was previously false but now it is true. It provides us with incorrect predictions of the positive class.

### 41. What difference lies between traditional application programming and Data Science?

Traditional application programming on one hand has to create rules to convert the input to output. On the other hand, Data Science already has the rules produced automatically from the data. This is the most important difference between the two.

### 42. What are some sampling techniques?

When asked this question, you will have to elaborate on Probability and Non-Probability Sampling.

### 43. Name some Data Science Libraries.

Following are the names of some Data Science libraries:

- NumPy
- SciPy
- Tensor Flow
- Scrapy
- Librosa
- MatPlotLib
- Pandas

### 44. What is regularization in machine learning?

Regularization is a machine learning approach that prevents overfitting, which occurs when a model is overly complicated and fits the training data too closely, resulting in poor generalization to new data. Regularization does this by introducing a penalty term into the loss function, which pushes the model to have smaller weights.

### 45. Explain the concept of the bias-variance tradeoff in machine learning?

The bias-variance tradeoff is a fundamental machine learning concept that refers to the tradeoff between a model’s ability to match the training data (low bias) and its ability to generalize to new data (high bias) (low variance). A model with a high bias cannot capture the complexity of the data and hence under fits the training data, whereas a model with a high variance overfits the training data and thus fails to generalize to new data.

### 46. What is ensemble learning in machine learning?

Ensemble learning is a machine learning approach that integrates many models to increase the accuracy and resilience of predictions. The core principle underlying ensemble learning is to combine the predictions of numerous weak learners (models that perform just marginally better than random guessing) to generate a strong learner (model that performs significantly better than randomguessing).

## Conclusion-

Data science uses scientific methods along with various Data Science tools on unstructured data to extract useful information based on the data itself and to give out useful insights to the company according to which they can take suitable actions. The more you know about Data Science, the higher your chances of getting the job that you want.