Data Science is one of the essential parts of the Information Technology sector. It is has been in more demand for the past few years, and most professionals want to work in this field. Many aspirants are already preparing themselves for a Data Science career, and for them, we have listed the Top 50 Data Science Interview Questions with the following difficulty levels.
- Data Science Interview Questions – General
- Data Science Interview Questions – Intermediate
- Data Science Interview Questions – Expert
Data Science Interview Questions – General
Let’s start Data Science Interview Questions with some beginner-level questions that cover basics and foundational knowledge.
1) What is a decision tree?
A decision tree is a model used in operations research, machine learning and strategic planning. Each endpoint connected to the branch is called a node, and generally, more nodes result in a more accurate decision. The last nodes of the decision tree are called the leaves of the tree, where a decision is made.
2) What is NLP?
Natural Language Processing, or NLP, is a branch of artificial intelligence that allows machines to read and understand human languages.
3) What is cross-validation?
Cross-validation is the technique used to assess how well a model performs on a new independent dataset.
4) What is statistical power?
Statistical power refers to the power of a binary hypothesis, the probability that the test will reject the null hypothesis when the alternative hypothesis is true.
5) What is selection bias?
Selection bias is a problematic situation in which error is introduced due to a non-random population sample.
6) What is logistic regression?
Logistic regression is a technique used to forecast the binary outcome from a linear combination of predictor variables. It is also known as the logit model.
7) Define the term deep learning
Deep Learning is concerned with algorithms inspired by the structure called artificial neural networks (ANN).
8) What is Ensemble Learning?
Ensemble Learning combines a diverse set of learners to improvise the stability and predictive power of the model.
9) What is boosting?
Boosting is one of the ensemble methods to improve a model by reducing its bias and variance, leading to converting weak learners to strong learners. The idea here is to train a weak learner and sequentially iterate and improve the model by learning from the previous learner.
10) What is multicollinearity, and what to do with it?
Multicollinearity is a state when an independent variable is highly correlated with another independent variable in a multiple regression equation. It can be problematic as it undermines the statistical significance of an independent variable.
11) What are recommender systems?
The recommender system is an information filtering system that predicts the preferences or ratings a user would give to a product.
12) What are the feature vectors?
It is an n-dimensional vector for numerical features that represents an object. In ML, feature vectors represent numeric or symbolic characteristics (called features) of an object in a mathematical way that’s easy to analyse.
13) What is collaborative filtering?
It is a method used by most recommender systems for filtering the process to find patterns and information by numerous data sources, collaborating perspectives, and several agents.
14) What is the goal of A/B Testing?
A/B is a statistical hypothesis testing for randomised experiments with two variables (A and B). It is used to detect any changes to a web page to maximise or increase the outcome of a strategy.
15) What is a recall?
It is the true positive rate ratio against the actual positive rate that ranges from 0 to 1.
Data Science Interview Questions – Intermediate
Now comes Data Science Interview Questions of intermediate level that will cover some in-depth concepts.
16) What are the differences between supervised and unsupervised learning?
| Supervised Learning | Unsupervised Learning |
|---|---|
|
|
|
|
|
|
17) Why is dimension reduction significant?
It is the process of reducing the number of features in a dataset. Dimension Reduction is important mainly when you want to reduce variance in your model (overfitting).
18) What is the law of large numbers?
It is the theorem that forms the basis of frequency-style thinking. It describes the outcome of performing the same experiment very frequently. It also states that the sample variance, the sample mean and sample standard deviation converge to what is estimated.
19) What is Prior probability and likelihood?
- Prior Probability is the ratio of the dependent variable in a data set.
- The likelihood is the probability of a given observant in the presence of some other variable.
20) What is Back Propagation?
It is the way of tuning the weights of a neural net depending upon the error rate obtained in the previous epoch.
21) What are the confounding variables?
Confounding variables are the extraneous variables in a statistical model that correlates directly or inversely with the dependent and independent variables.
22) Explain cluster sampling technique in Data science
It is a method used when it is challenging to study the target population spread across, and simple random sampling can’t be applied.
23) Do gradient descent methods always converge to similar points?
No, because they reach a local minima or a local optima point in some cases. Also, the global optima point is not reached. The data and the starting conditions govern this.
24) What is principal component analysis?
PCA or Principal Component Analysis involves project higher dimensional data (e.g. 3D) to a smaller space (e.g. 2D). It results in a lower dimension of data (2D instead of 3D) while keeping all original variables in the model.
25) What is mean imputation of missing data acceptable?
It is the practice of replacing the null values in a data set with the mean of the data.
26) Which language is best for text analytics? R or Python?
For text analytics, Python will be more suitable as it consists of a rich library known as pandas. It also allows you to use high-level data analysis tools and data structures, whereas R doesn’t offer this feature.
27) Why is MSE (Mean Square Error) a bad measure of model performance? What would you suggest instead?
MSE or Mean Squared Error gives a relatively high weight to significant errors. Therefore, it tends to put too much emphasis on large deviations. MAE (mean absolute deviation) is a more robust alternative.
28) What are Auto-Encoders?
Autoencoders are learning networks that help you to transform inputs into outputs with a few errors. In other words, you will get output to be as close to input as possible.
29) Discuss Artificial Neural Network.
ANN or Artificial Neural network has revolutionised machine learning. ANN is a unique set of algorithms that helps you to adapt the result with the changing input so the network can generate the best possible result without redesigning the output criteria.
30) Name various types of Deep Learning Frameworks.
31) Name commonly used algorithms?
The four most used algorithms by Data scientists are:
- Linear regression
- Logistic regression
- Random Forest
- KNN
32) What are the disadvantages of using a linear model?
The Three disadvantages of the linear model are:
- It cannot be used for binary or count outcomes
- The assumption of linearity of the errors.
- Plenty of overfitting problems cannot be solved.
33) What is the difference between convex and non-convex cost functions?
- A convex function is a line drawn between any two points on the graph that lies on or above the graph. It has one minimum point.
- A non-convex function is a line drawn between any two points on the graph that may intersect other points. It is characterized as “wavy”.
34) What is a univariate analysis?
An analysis that is applied to none attribute at a time is known as univariate analysis. Boxplot is a widely used, univariate model.
35) Explain Collaborative filtering
Collaborative filtering is used to search for correct patterns by collaborating viewpoints, multiple data sources, and various agents.
36) When underfitting occurs in a static model?
It occurs when a machine learning algorithm or statistical model cannot capture the underlying trend of the data.
37) Overfitting Vs Underfitting
- Model is underfitting the training data when the model cannot capture the relationship between the input (X) and target (Y) values and thus performs poorly on the training data.
- Model is overfitting the training data when the model performs well on the training data but not on the evaluation data.
Data Science Interview Questions – Expert
Now, the Data Science Interview Questions of advanced level are covered with a more in-depth knowledge of data science.
38) Explain Confusion Matrix.
Confusion Matrix summarises the performance of the classification algorithm.
39) Calculate the precision and recall rate for the above confusion matrix.
- Precision Rate = (True positive) / (True Positive + False Positive) = 30/(30+30) = 30/60 = 0.50
- Recall Rate = (True Positive) / (True Positive + False Negative) = 30/(30+10) = 30/40 = 0.75
40) Calculate the accuracy rate for the above confusion matrix.
Accuracy = (True Positive + True Negative) / Total Observations
= (30+930)/(30+30+10+930)
=960/1000
=0.96
41) Treating a categorical variable as a continuous variable would result in a better predictive model? Explain.
Yes, the categorical value is considered as a continuous variable only when the variable is ordinal. So it is a better predictive model.
42) Is it possible to capture the correlation between continuous and categorical variables?
Yes, we can use the analysis of covariance technique to capture the association between continuous and categorical variables.
43) How can you select k for k-means?
- To select k for k-means clustering, use the elbow method. It runs k-means clustering on the data set where ‘k’ is the number of clusters.
- Use Within the Sum of Squares or WSS that is defined as the sum of the squared distance between every member of the cluster and its centroid.
44) In a study of population behaviour, you want to find all users who are most similar to the four individual types that are valuable for your study. Which algorithm will be more appropriate to use for this study?
The most appropriate algorithm for this study would be k-means clustering, as we need to group people based on four different similarities that indicate the value of k.
45) Why is Naive Bayes so bad? How would you improve a spam detection algorithm that uses naive Bayes?
- One of the significant drawbacks of Naive Bayes is that it holds a strong assumption that the features are uncorrelated with each other, which generally is never the case.
- One way to improve an algorithm that uses Naive Bayes is by decorrelating the features so that the assumption holds true.
46) What is the Central Limit Theorem? Explain it. Why is it important?
- According to the theorem, the sampling distribution of the sample mean reaches a normal distribution when the sample size gets larger without taking into effect the shape of the population distribution.
- It is essential as it is used in hypothesis testing and to calculate confidence intervals.
47) How to check if the regression model fits the data well?
- R-squared/Adjusted R-squared: Relative measure of fit.
- RMSE: Absolute measure of fit.
- F1 Score: It evaluates the null hypothesis (all regression coefficients are equal to zero) vs the alternative hypothesis (at least one doesn’t equal zero).
48) Do you think 50 small decision trees are better than a large one? Why? OR Is a random forest a better model than a decision tree?
Yes, A random forest is an ensemble method that takes many weak decision trees to make a strong learner. Random forests are more robust, more accurate, and less prone to overfitting.
49) How can you avoid overfitting your model?
It refers to a model that is only set for minimal data and ignores the bigger picture. To avoid overfitting, there are three main methods.
- Cross-validation techniques, such as k folds cross-validation
- Keep the model simple – Removing the noise in the training data by Taking fewer variables into account.
- Use regularisation techniques, such as LASSO. It penalises specific model parameters if they cause overfitting.
50) How do you handle missing data? What imputation techniques do you recommend?
Some ways to handle missing data are:
- Mean/Median/Mode imputation
- Delete rows with missing data
- Assigning a unique value
- Predicting the missing values
- Using an algorithm that supports missing values, like random forests
Conclusion
Data Science has played a crucial role in the Information Technology sector. Most professionals seek careers in Data Science and prepare themselves for the challenges to get selected in the Interview. Thus, we listed the Top 50 Data Science Interview Questions on a few best topics in different difficulty levels. All these questions will surely help you in the Data Science Interview.
Related/References:
- [DP-100] Microsoft Certified Azure Data Scientist Associate: Everything you must know
- Microsoft Certified Azure Data Scientist Associate | DP 100 | Step By Step Activity Guides (Hands-On Labs)
- Azure Machine Learning Service Workflow: Overview for Beginners
- Automated ML In Azure
- Data Science VS Data Analytics VS Data Engineer
Next Task For You
To know more about the DP-100 course, why you should learn, Job opportunities, and what to study, including Hands-On labs you must perform to clear [DP-100] Microsoft Azure Data Scientist Associate Certification register for our FREE CLASS.
The post Top 50 Data Science Interview Questions appeared first on Cloud Training Program.