data scientist interview questions

Top Data Scientist Interview Questions 2023

Are you somebody who wants to start a career in Data Science or going to attend Interview for the Data Scientist position? This blog is for you with the ” Top Data Scientist interview questions“. Over the years data science has got immense importance due to the large production of data through various channels and the serious need of processing such data for better efficiency and execution to organizations.

Data scientists should have strong programming knowledge in various technologies like python, machine learning, SQL, R programming, etc as well as great analytical thinking.  In fact, SQL and python are the most popular technologies among professional developers.

The need for data scientists is so much that the number is almost doubled compared to the numbers in 2010. The average salary for a data scientist is $100,000 USD according to the Bureau of Labor Statistics, and that of an analyst is $70,000 USD. The best part is that in 2020, the number of job vacancies was more that the actual number of applications.

Enough with the facts. Let us jump into the top data scientist interview questions

1. What is the difference between Supervised Learning and Unsupervised Learning?

Machine Learning is a type of machine learning in which machines are trained using a ‘labeled’ dataset to derive or predict the output.  The labeled data here means that some input is already tagged with the right output.  For eg., take an example of an automatic car.  We have to provide it with a route map, and with the help of sensors it can negotiate traffic and vehicles on the roads and automatically stop when there is a traffic junction, and finally reach the destination.

Unsupervised Machine Learning uses machine learning algorithms to analyze and cluster unlabeled datasets and make predictions.  For eg., for identifying diabetics in a person early, a large dataset of previous past history medical data is provided to the machine and the machine makes learning with the algorithms at its command, compares with the normal diabetic reading of patients, and compares the reading of patient, and finally makes the prediction.  Another example could be to predict the customer’s future buying options based on past buying behavior.

  1. What is the difference between machine learning and deep learning?

Machine Learning  (ML) is a subset of Artificial Intelligence (AI), that focuses on the development of algorithms and statistical models, to enable the system to learn and identify patterns and relationships from a dataset and make predictions,  or analyze the trends and make projections for the future.

Deep Learning (DL) is the subset of Machine Learning (ML) that uses neural networks to analyze complex patterns and relationships in the dataset.  In other words, it will take a complex dataset and the data is moved through the various layers in the neural network until we get a final segregated data of a particular type.   It resembles a human brain in how the neurons are interconnected to each other. Deep Learning models can be trained using a large amounts of data and algorithms, and they have the ability to learn and improve over time with huge datasets.


S. No. Machine Learning Deep Learning
1. Machine Learning is a superset of Deep Learning Deep Learning is a subset of Machine Learning
2. The data represented in Machine Learning is quite different as compared to Deep Learning as it uses structured data The data representation is used in Deep Learning is quite different as it uses neural networks(ANN).
3. Machine Learning is an evolution of AI Deep Learning is an evolution of Machine Learning. Basically, it is how deep is the machine learning.
4. Machine learning consists of thousands of data points. Big Data: Millions of data points.
5. Outputs: Numerical Value, like classification of the score. Anything from numerical values to free-form elements, such as free text and sound.
6. Uses various types of automated algorithms that turn to model functions and predict future action from data. Uses neural network that passes data through processing layers to, interpret data features and relations.
7. Algorithms are detected by data analysts to examine specific variables in data sets. Algorithms are largely self-depicted on data analysis once they’re put into production.
8. Machine Learning is highly used to stay in the competition and learn new things. Deep Learning solves complex machine-learning issues.
9. Training can be performed using the CPU (Central Processing Unit). A dedicated GPU (Graphics Processing Unit) is required for training.
10. More human intervention is involved in getting results. Although more difficult to set up, deep learning requires less intervention once it is running.
11. Machine learning systems can be swiftly set up and run, but their effectiveness may be constrained. Although they require additional setup time, deep learning algorithms can produce results immediately (although the quality is likely to improve over time as more data becomes available).
12. Its model takes less time in training due to its small size. A huge amount of time is taken because of very big data points.
13. Humans explicitly do feature engineering. Feature engineering is not needed because important features are automatically detected by neural networks.
14. Machine learning applications are simpler compared to deep learning and can be executed on standard computers. Deep learning systems utilize much more powerful hardware and resources.
15. The results of an ML model are easy to explain. The results of deep learning are difficult to explain.
16. Machine learning models can be used to solve straightforward or a little bit challenging issues. Deep learning models are appropriate for resolving challenging issues.
17. Banks, doctor’s offices, and mailboxes all employ machine learning already. Deep learning technology enables increasingly sophisticated and autonomous algorithms, such as self-driving automobiles or surgical robots.
18. Machine learning involves training algorithms to identify patterns and relationships in data. Deep learning, on the other hand, uses complex neural networks with multiple layers to analyze more intricate patterns and relationships.
19.  Machine learning algorithms can range from simple linear models to more complex models such as decision trees and random forests. Deep learning algorithms, on the other hand, are based on artificial neural networks that consist of multiple layers and nodes.
20. Machine learning algorithms typically require fewer data than deep learning algorithms, but the quality of the data is more important. Deep learning algorithms, on the other hand, require large amounts of data to train the neural networks but can learn and improve on their own as they process more data.
21. Machine learning is used for a wide range of applications, such as regression analysis, classification, and clustering. Deep learning, on the other hand, is mostly used for complex tasks such as image and speech recognition, natural language processing, and autonomous systems.
22. machine learning algorithms for complex tasks, but they can also be more difficult to train and may require more computational resources.  deep learning algorithms are more accurate than machine learning algorithms.

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  1. What are the problems related to Overfitting and Underfitting and how will you deal with these?

Both Overfitting and Underfitting cause the degraded performance of Machine Learning models.

Overfitting occurs when we train our model more than required ie., the more we train our model, the more the chances of overfitting.  Such a problem occurs in supervised models. Underfitting occurs when the model is not able to learn with the training data provided.  To provide more training data to sort the issue. The goal of machine learning should be “Goodness to Fit” which means ie the result of the predicted value should match the true value of the data set.

Three important methods to avoid overfitting are:

  • Keeping the model simple—using fewer variables and removing the major amount of the noise in the training data
  • Using cross-validation techniques. E.g.: k folds cross-validation
  • Using regularization techniques — like LASSO, to penalize model parameters that are more likely to cause overfitting.

4. What is the importance of Data Cleansing?

Data Cleansing is a process of removing or updating information that is incorrect, incomplete, duplicated, irrelevant, or formatted improperly. It is very important to improve the quality of data and hence the accuracy and productivity of the processes.

5.  Explain Eigenvectors and Eigenvalues

Eigenvectors depict the direction in which a linear transformation moves and acts by compressing, flipping, or stretching. They are generally used to calculate the correlation or covariance matrix. The direction remains constant when a linear transformation is applied.

The Eigenvalues represent the strength of the transformation in the direction of the Eigenvector.

6. What are Autoencoders?

They are artificial neural networks that try to generate a representation as close as possible to the original input by training the network to ignore signal “noise” in between. It is used in unsupervised  Machine Learning.

7. Differentiate between univariate, bivariate, and multivariate analysis.

Univariate data contains only one variable. The univariate analysis describes the data and finds patterns that exist within it.  Bivariate data contains two different variables. The bivariate analysis deals with causes, relationships, and analysis between those two variables.

Multivariate data contains three or more variables. Multivariate analysis is similar to that of a bivariate, however, in a multivariate analysis, there exists more than one dependent variable.

Stay tuned for more data scientist interview questions in the upcoming updates.

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