Data science is an ever-evolving field that mines unprocessed data, analyses it, and discovers patterns from which to derive valuable insights.
Given the high demand and limited supply of these skills, data scientists are among the highest-paid IT specialists.
Fascinating, isn’t it?
But to get a lump sum amount of money, you first need to crack the interview for various data science job roles.
Don’t worry! We have listed frequently asked data science interview questions along with advice on how to respond to them.
- Data science interview question for beginners
- Data science interview questions for experienced
- General interview questions for data science role
Are Data Science Interviews Hard?
Data science interviews aren’t always more difficult or straightforward than other interviews. There is no clear solution to this question because it is subjective.
Data science interviews can be particularly challenging due to the capabilities you’ll need to demonstrate technical skills, problem-solving abilities, and communication, as well as the industry’s traditionally high admission requirements.
You will do well if you have a solid understanding of the principles, can fully and understandably explain any projects you have worked on, and can put technical concepts into practice.
Top Data Science Interview Questions In 2022
Here is a list of the top data science questions you may anticipate being asked in the interview, along with advice on how to respond to them:
Data Science Interview Questions for Freshers
1. What is data science?
A branch of computer science known as “data science” is specifically concerned with transforming data into information and drawing valuable conclusions from it.
2. What is logistic regression?
Logistic regression is a type of predictive analysis. By using a logistic regression equation, it is possible to determine the associations between a dependent binary variable and one or more independent variables.
3. What is a confusion matrix?
A classification algorithm’s effectiveness is evaluated using a confusion matrix. It is employed because a classification algorithm fails to correctly classify data when there are more than two classes or when the number of classes is odd.
4. Difference between supervised and unsupervised machine learning type
The type of training data that supervised and unsupervised learning systems receive varies. In contrast to unsupervised learning, which uses unlabeled data to help the algorithm identify trends, supervised learning requires labelled training data.
5. What is sampling? Name a few sampling techniques
Sampling allows for easier analysis of a portion of the data while still revealing information about the entire dataset.
The following sampling methods are frequently used:
- Simple Random Sampling
- Systematic Sampling
- Cluster Sampling
- Purposive Sampling
- Quota Sampling
- Convenience Sampling
6. What is variance in data science?
Variance is a sort of inaccuracy that develops in a Data Science model as it becomes overly complicated and learns features from data, along with any noise that may be present.
7. What is a decision tree algorithm?
Decision trees are a method for categorizing data and figuring out whether certain outcomes are possible in a system. The root node is the term for the tree’s base. Based on the many choices available at each level, the root node divides into decision nodes. Lead nodes, which indicate each choice’s outcome, are formed by the flow of decision nodes.
8. What is pruning in the decision tree algorithm?
Pruning a decision tree is the process of removing non-critical subtrees to prevent the overfitting of the data under consideration. Pre-pruning involves trimming the tree as it grows, using measurements like the Gini index or information gain metrics. After a tree has been built, it must be pruned from the ground up, which is known as post-pruning.
9. What is entropy in the decision tree algorithm?
The amount of uncertainty or impurity in a dataset is measured by its entropy. The following formula describes the entropy for a dataset with N classes:
10. What is deep learning?
Using artificial neural networks, deep learning is a subclass of machine learning that focuses on supervised, unsupervised, and semi-supervised learning.
11. What is RNN?
A type of artificial neural network called a recurrent neural network bases the connections between its nodes on a time series. The only type of neural network having internal memory is an RNN, which is frequently employed in speech recognition applications.
12. What is the ROC curve?
ROC curves are graphs that show a classification model’s performance at various thresholds for classification. The True Positive Rate (TPR) and False Positive Rate (FPR) are plotted on the graph’s y and x axes, respectively.
13. What is a random forest model?
An example of supervised learning using a machine learning algorithm is the random forest model. It is most frequently applied to classification and regression issues.
14. What is precision?
Precision is an actual value that represents factual knowledge.
It is a measure of weighing valid instances.
15. What is a recall?
The proportion of events labelled as accurate is known as recall. It is an approximation.
16. What is the p-value?
The probability that an observation made regarding a dataset is a result of chance is expressed by the P-value. Any p-value that is less than 5% provides strong evidence that the observation is true and refutes the null hypothesis. A finding is less likely to be valid the higher the p-value.
17. What is the F1 score, and how to calculate it?
We can determine the test’s correctness by computing the harmonic mean of precision and recall with the aid of the F1 score. Precision and recall are accurate if F1 = 1. Precision or recall are less accurate or wholly wrong if F1 is less than 1 or equal to 0.
18. What is RMSE?
The root mean square error is referred to as RMSE. It is a metric for regression accuracy. We can determine the severity of an error caused by a regression model using the RMSE.
19. What is ensemble learning?
Ensemble learning involves bringing together a variety of learners (individual models) to enhance the model’s predictability and stability.
20. What is machine learning?
Machine Learning is the study and development of algorithms that can learn from and predict data. This topic is closely related to computational statistics.
Data Science Interview Questions for Experienced
1. Explain bagging in data science
Bagging attempts to apply comparable learners to tiny sample populations before calculating the forecasts’ average. You can use different learners on various populations for generalized bagging. As you may anticipate, this assists in lowering the variance error.
2. Explain Boosting in data science
Boosting is an iterative strategy that modifies an observation’s weight based on the most recent classification. If an observation was wrongly classified, an attempt is made to raise its weight, and vice versa. In general, boosting reduces bias error and creates powerful predictive models. They might, however, overfit the workout set.
3. Difference between machine learning and deep learning
Machine Learning, a branch of data science, is an area of computer science that deals with leveraging current data to assist systems in automatically learning new skills to carry out various activities without the need for explicitly specified rules.
On the other hand, deep learning is a branch of machine learning that focuses on creating machine learning models using algorithms that attempt to mimic the way the human brain learns from data in a system to develop new skills.
4. Explain the recommender system
A recommender system makes predictions about future user behaviour based on past evaluations of a particular item. For instance, Netflix suggests TV series and movies to viewers by looking at the content those users have previously evaluated and utilizing that information to suggest new content they might enjoy.
5. Discuss normal distribution
A probability distribution known as a “normal distribution” has values that are symmetrical on either side of the data’s mean. The implication is that values closer to the mean are more frequent than values furthest from it.
6. Explain time series analysis
A type of data analysis known as a time-series analysis examines data values gathered in a specific order. It examines the data gathered over time and takes into account the various times at which the data was gathered.
7. What is the use of the summary function?
Summary functions summarise the results of different model-fitting functions. In R, for example, the summary() function can be used to gather a quick overview of your dataset and the results that are produced by a machine learning algorithm.
8. What is K-fold cross-validation?
A machine learning model’s effectiveness can be estimated using the cross-validation technique. The parameter k keeps track of how many groups a dataset can be divided into. The method begins with a random shuffling of the entire dataset. Then, it is split into k groups, also referred to as folds.
9. What are univariate, bivariate, and multivariate analyses?
Analyzing just one variable is known as univariate analysis. Comparing two and more than two variables is the focus of bivariate and multivariate analyses, respectively.
10. What is an outlier?
A data value that differs significantly from the other values in a dataset is referred to as an outlier. An outlier could originate from an experimental mistake or a genuine value that deviates significantly from the mean.
11. How do you treat outlier values?
If outlets don’t meet a set of requirements, they are frequently filtered away during data processing. To automatically remove outliers, you can configure the data analysis tool you’re using as a filter. Outliers can occasionally provide information on low-probability scenarios, nevertheless. In that situation, analysts might classify outliers and examine each one separately.
12. What is power analysis?
The experimental design includes the power analysis as a crucial component. It assists you in figuring out the sample size needed to conclusively determine the impact of a certain size on a cause.
13. The difference between the expected value and mean value
These two have a few differences, although it should be noted that they are employed in various circumstances. The expected value is used when dealing with random variables, whereas the mean value typically refers to the probability distribution.
14. What is Naive in a Naive Bayes Algorithm?
The Bayes Theorem is the foundation of the Naive Bayes Algorithm paradigm. It gives the likelihood of an event. It is predicated on prior knowledge of circumstances that might be connected to that particular incident.
15. What is A/B testing, and why is it conducted?
To conduct random tests with two variables, A and B, AB testing is utilized. This testing technique aims to identify changes that need to be made to a web page to optimize or improve the result of a strategy.
16. Explain eigenvalue and eigenvector
For understanding linear transformations, eigenvectors are used. In data analysis, the eigenvectors of a correlation or covariance matrix are typically calculated. The directions that a specific linear transformation flips, compresses, or stretches are called eigenvectors.
The eigenvalue is also referred to as the compression factor or the strength of the transformation in the direction of the eigenvector.
17. Which language is best for text analysis – Python or R?
Python has a robust package called pandas which makes it better suited for text analytics. It enables the use of high-level data analysis tools and data structures, while R lacks this functionality.
18. What is the difference between a validation set and a test set?
A validation set is typically regarded as a component of the training set because it is utilized for parameter selection, preventing the overfitting of the model under construction.
While a test set is used to test or assess how well a trained machine learning model performs.
19. What is regularisation? Why is it useful?
The technique of regularisation involves tuning parameters in a model to bring about smoothness and prevent overfitting. Most frequently, a constant multiple is added to an existing weight vector to accomplish this.
20. What are error and residual errors?
An error is defined as the gap between the expected value and the actual value. Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error are the most often used methods for calculating errors in data science (RMSE). The difference between a set of measured values and their mathematical mean is known as the residual. In contrast to residual error, which can be seen on a graph, an error is typically unobservable.
21. What is a star schema?
A star schema is a database structure that stores measured data in a single fact table. The main table is called a star schema because it sits in the centre of a logical diagram, and the smaller tables branch off like nodes in a star.
22. Define tensors in data science
A tensor is a mathematical object that is represented as a higher-dimensional array. Tensors are data arrays with different dimensions and ranks that are fed as input to the neural network.
23. Name three disadvantages of using a linear model
The linear model has three drawbacks:
- The assumption is that the errors are linear.
- This model cannot be used for binary or count outcomes.
- There are numerous overfitting issues that it cannot solve.
24. What are true positive rates and false positive rates?
The ratio of True Positives to True Positives and False Negatives is known as the True Positive rate (TPR).
The False Positive Rate (FPR) is the percentage of False Positives to all positives (True positives and false positives). It is the possibility of a false alarm, in which a positive result is given when the true result is negative.
25. What is the need for resampling?
Resampling occurs in any of the following circumstances:
- Estimating the precision of sample statistics by using subsets of available data or randomly drawing with replacement from a set of data points
- When performing significance tests, substitute labels on data points.
- Using random subsets to validate models
General Questions for Data Science Role
It is not possible to crack the interview just by covering the technical aspect of Data Science, be prepared for some general questions too.
- Introduce yourself
- What do you know about data science?
- Why did you opt for a data science career?
- What is the most challenging project you encountered on your learning journey?
- Situational questions based on resume.
- Why do you want to work as a data scientist with this company?
- How do you ensure a high productivity level at work?
The work of data scientists is not easy, but it is rewarding, and there are many open positions. These data science interview questions will get you one step closer to landing your dream job.
I hope you find this collection of Data Science Interview Questions and Answers useful in preparing for your interviews. Best wishes!
Also Read: 10 Best Careers In Data Science
Frequently Asked Questions (FAQs)
Is data science still in demand in 2022?
The job market for data science professionals has recovered significantly after falling by more than 20.1% during the first wave of Covid-19 (from March 2020 to June 2020). From the start of Covid-19 in March 2020 to April 2022, there is a 73.5% increase in open jobs.
How do I prepare for an interview for data science?
While preparing for an interview for data science, keep these points in mind:
1. Investigate the position and determine your fit.
2. Determine what the interviewer is looking for.
3. Be truthful about your technical abilities and software experience.
4. Display your abilities. Create a portfolio to showcase your data science skills.
5. Respond to interview questions with confidence.
What are the 3 main concepts of data science?
The three main data science concepts are statistics, machine learning, and deep learning.
Do data science interviews include coding questions?
Yes, data science interviews do include coding questions. The interviewer expects data science interview candidates to understand data structures and algorithms and to be able to solve the majority of problems.