Data science is the new hot job and has been gaining more attention. It is an emerging field that will be more popular with time, and it’s going to get more important in the near future. Data scientists are the people who understand the algorithms behind big data and they use them to help companies make better decisions. So, what is a typical data scientist like? How do you prepare for an interview? This is a general overview of what you should expect if you prepare for a data science interview.
Data scientists are the most sought-after candidates for job openings these days. I see data science as a combination of statistics, mathematics, computer science, and business skills, which makes it one of the most challenging professions to enter. You’ll have to be extremely prepared when you go in for an interview, so here are some sample data science interview questions and answers to help you out. The most asked data science interview questions and answers are ordered in a way that is similar to the order in which they are asked in an interview for freshers.
- What is Data Science? What are the differences between supervised and unsupervised learning?
Data Science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns in the raw data.
The differences between supervised and unsupervised learning are given below:
| Supervised Learning | Unsupervised Learning |
| Input data is labeled. | Input data is unlabelled. |
| Uses a training data set. | Uses the input data set. |
| Used for prediction. | Used for analysis. |
| Enables classification and regression. | Enables Classification, Density Estimation, & Dimension Reduction |
2. What does one understand by the term Data Science?
An interdisciplinary field that constitutes various scientific processes, algorithms, tools, and machine learning techniques working to help find common patterns and gather sensible insights from the given raw input data using statistical and mathematical analysis is called Data Science.
3. How is Data Science different from traditional application programming?
Data Science takes a fundamentally different approach to building systems than traditional applications.
In traditional programming paradigms, we analyzed the input, figured out the expected output, and wrote code, which contained the rules and statements needed to transform the provided input into the expected output. As we can imagine, these rules were not easy to write, especially for data that even computers could hardly understand, e.g., images, videos, etc.
Data Science changes the traditional process a little bit. In it, you need to access large volumes of data that contain the necessary inputs and their mappings to the expected outputs. We use data science algorithms, which use mathematical analysis to generate rules to map the given inputs to outputs.
4. What Is Logistic Regression?
Logistic regression is a type of predictive analysis. It’s used to determine the relationship(s) that exist between one or more dependent binary variables and one or more independent variables by utilizing a logistic regression equation.
5. What is Selection Bias?
In selection bias, the researcher decides which subjects are going to be studied. Selection bias is often associated with research where the selection of participants is not random. Statistics are based on a sample; so, if it is distorted, it is not a true statement. The conclusions of the study may not be accurate if the selection bias is not taken into account.
The types of selection bias include:
- Sampling bias: It is a systematic error due to a non-random sample of a population causing some members of the population to be less likely to be included than others resulting in a biased sample.
- Time interval: A trial may be terminated early at an extreme value (often for ethical reasons), but the extreme value is likely to be reached by the variable with the largest variance, even if all variables have a similar mean.
- Data: When specific subsets of data are chosen to support a conclusion or rejection of bad data on arbitrary grounds, instead of according to previously stated or generally agreed criteria.
- Attrition: Attrition bias is a kind of selection bias caused by attrition (loss of participants) discounting trial subjects/tests that did not run to completion.
6. What are dimensionality reduction and its benefits?
Dimensionality reduction is reducing the number of features in a dataset while maintaining its original data structure, so that we can learn something useful about a new point in the original space of the data.
There are many ways to reduce dimensionality such as Feature Selection Methods, Matrix Factorization Manifold, Learning Autoencoder Methods, Linear Discriminant Analysis (LDA)
7. What does NLP stand for?
NLP means natural language processing. It’s the study of programming computers to learn large amounts of textual data. An example of NLP would be tokenizing your raw text, removing the common words from your document, and then using these smaller documents to create the topics.
8. Why do you need to perform resampling?
Sampling is done in: Estimating the accuracy of sample statistics by drawing randomly with replacements from a set of the data point or using as subsets of accessible data Substituting labels on data points when performing necessary tests Validating models by using random subsets.
9. List out the libraries in Python used for Data Analysis and Scientific Computations?
Seaborn, SciPy, Pandas, Matplotlib, SciKit, NumPy e.t.c
10. Explain Collaborative filtering?
Search for the right patterns by collaborating viewpoints, multiple data sources, and various agents.
11. What is the aim of conducting A/B Testing?
In random experimentation, A and B are tested together. This testing method helps a team of developers create a change that maximizes the outcome of a strategy for a web page.
12. What is Dropout?
Dropout is a trick used for solving a classification problem in data science, which is used for dropping out hidden units of a network on a random basis. 20% of the nodes are dropped out so that the required space can be arranged for iterations needed to converge the network.
13. What do you know about Autoencoders?
Autoencoders are a class of artificial neural networks that transform inputs into outputs with minimal error. The output’s results are very close to the inputs. A couple of filters are added between the input and the output. Each filter is smaller than the size pertaining to the input layer. An autoencoder receives unlabeled input that is encoded for reconstructing the output.
14. What is a Boltzmann Machine?
A machine learning algorithm to learn patterns from data is a Boltzmann Machine. This learning algorithm enables it to discover interesting patterns that represent complex regularities in the training data.
It’s basically used for optimization of the quantity and weight for some given problem.
For many years, the simple learning algorithm used in Boltzmann Machines was slow in networks with many layers of feature detectors.
15. What is the Computational Graph?
A computational graph is a representation of your model or problem that is based on TensorFlow.
Its algorithm is designed in such a way that it can perform various mathematical operations.
The edges in these nodes are called tensors. This is the reason the computational graph is called a TensorFlow of inputs.
The computational graph is characterized by data flows in the form of a graph, so it’s also called a DataFlow Graph
16. What is Cluster Sampling?
Cluster sampling is a technique used when it becomes difficult to study the target population spread across a wide area and simple random sampling cannot be applied. A cluster Sample is a probability sample where each sampling unit is a collection or cluster of elements.
17. Explain Neural Network Fundamentals.
In the human brain, different neurons are present. These neurons combine and perform various tasks. The Neural Network in deep learning tries to imitate human brain neurons. The neural network learns the patterns from the data and uses the knowledge that it gains from various patterns to predict the output for new data, without any human assistance.
18. What is an Activation function?
In a neural network, an activation function helps add non-linearity to the network by providing the network with a value that is different from the outputs of previous layers. It helps the user learn how to do more advanced functions in the app. A neural network without an activation function will not be able to learn more than a linear function. An activation function is a mathematical formula that’s used to calculate the output from an artificial neuron. This means it has complex functions and combinations applied to it.
19. What are vanishing gradients?
A vanishing gradient is a mathematical term that refers to a situation in which the slope of the gradient vector (a vector that represents the change in magnitude of a function as a function of position) decreases over its length.
20. What is the full form of LSTM? What is its function?
Long Short-Term Memory Networks (LSTM) is a type of neural network that can learn and remember complex patterns. They are typically used for tasks such as recognizing text or images, understanding natural language, or predicting future events.
21. What is Ensemble Learning?
Ensemble learning is a type of machine learning algorithm which uses multiple models to produce a final prediction. It is used in a wide variety of different applications, including image classification, natural language processing, and speech recognition. The most well-known ensemble methods are Boosting and Bagging.
22. Define the term cross-validation?
Cross-validation is a technique used in data analysis that helps to ensure the accuracy of a model. It involves randomly splitting the data set into K groups and then using the model to predict the outcome for each group.
23. What is Pollingin CNN?
In deep learning, the word ‘pool’ means to group together the input into a single representation. Pooling helps in sliding the filter matrix over the input matrix.
24. What is an Artificial Neural Network?
An artificial neural network (ANN) is a computer algorithm that models the workings of the brain. ANNs are used in a variety of applications, including machine learning, pattern recognition, and prediction.
25. What is k-fold cross-validation?
K-fold cross-validation is a data analysis technique that helps you to determine how well your model performs on a new set of data. The technique is based on the idea that you can divide your data set into k subsets, and then train your model on each subset.