30 Data Science Interview Questions and Answers

1. Data Science Interview Questions

In this tutorial of 30 Data Science Interview Questions and answers, we will provide you with the 30 most asked Data Science Interview Questions. Also, there are Data Science interview questions for freshers as well as data science interview questions for experienced professionals. Moreover, it covers data scientist interview questions, data modeling interview questions, data analytics interview questions, python data science interview questions, as well as R data science interview questions. Also,  in these Data Science Interview Questions we have included all major topics. So, these Data Science Interview Questions will surely help you to sort out your each concept to clear Data Science interviews.

So, let’s explore the best Data Science Interview Questions.

Data Science Interview Questions

Data Science Interview Questions

2. 30 Data Science Interview Questions

Q.1. What is meant by feature vectors?

Basically, it is an n-dimensional vector of numerical features. That is used to represents some object. Moreover, in machine learning we can use it to represent some object.
Read more about vectors in detail.

Q.2. What do you understand by Linear Regression in R?

  • Basically, it is a type of statistical technique.
  • In this score of a variable Y is predicted from the score of a second variable X.
  • Where X is the predictor variable and also Y is known as the criterion variable.

Learn more about R linear regression.

Q.3. Which one you prefer R or Python for text analytics?

Of course, python, as it has pandas library. That provides easy to use data structures and high performance data analysis tools.
Read more about R vs python in detail.

Q.4. What is data science?

Data science includes data analysis. Also, it is an important component of the skill set required for many jobs in this area. But it’s not the only necessary skill. Moreover, they play active roles in the design and implementation work of four related areas:

  • Data architecture
  • In data acquisition
  • Data analysis
  • In data archiving

Q.5. What do you understand by the job description of data scientist?

Data Scientist has to use statistical methods. It includes mix modeling, predictive response modeling. Also, Optimization techniques to meet client business needs.
Furthermore, they have to develop and install statistical tools. Also, helps in building predictive models. And these models support clients in customer marketing and demand generation initiatives.
Moreover, Data Scientist collaborates with internal consulting teams to set analytic objectives, approach. Also, work plans to provide programming and analytic support to internal consulting.  Also, it provides statistical procedures utilizing SAS and Microsoft Office.

3. Frequently Asked Data Science Interview Questions

These are the most frequently asked Data Science interview Questions that you will most probably face in any Data Science Interview.

Q.6. What are essential skills and training needed in data Science?

  • Communication Storytelling
  • Statistic Machine Learning Optimization
  • Big Data Cloud Computing
  • Business and domain knowledge
  • Visualization of the self tool boxes
  • Programming CS fundamentals

Learn more skills needed to be a data scientist.

Q.7. What prior knowledge is required to become data scientist?

“With these skills, you’ll be eligible to apply to over 70% of all online job postings for data scientist roles,”

Q.8. What prior subject is required to become data analyst?

1. The Math skills
1.1 Probability
1.2 Statistics
1.3 Linear algebra

2. The Programming skills
2.1 Development Environment
2.2 Data Analysis
2.3 Data Visualization
2.4 machine Learning For

Q.9. What does the future hold for data scientists?

After next 5 years, they will develop the ability to utilize all sorts of data in real-time. Furthermore, this are needs of future, and it will spark the emergence of new data science paradigms.
Moreover, we can use more data to drive key business decisions. We will enable innovations like “Deep Learning”. Also, allows for accurate predictions and decision making. Further, modern applications have brought to fore new statistical paradigms.

The most important thing:

  • Skilled data scientists;
  • statisticians and
  • business analysts will be the key to unlocking the endless possibilities of big data.

Learn more about R future.

Q.10. Explain Data Science Vs Machine Learning
As both machine learning and statistics are part of data science. Also, Machine learning itself defines that the algorithms depend on some data. Further, we can use it as a training set, to fine-tune some model or algorithm parameters.

In particular, data science also covers:

  • data integration.
  • distributed architecture.
  • automating machine learning.
  • data visualization.
  • dashboards and BI.
  • data engineering.
  • deployment in production mode.
  • automated, data-driven decisions.

Q.11. What does Machine Learning mean for the Future of Data Science?
“Data science includes machine learning.”

Machine learning
Basically, it is the ability of a machine to generalize knowledge from data—call it learning. Although, without data, there are little machines can learn.
To push data science to increase relevance, a catalyst is an important thing. While it helps in increasing machine learning usage in different industries. As machine learning is good because it has data within it. Also, it has the ability to consume algorithms in it. Moreover, my expectation is that to move forward basic levels of machine learning. Thus, it will become a standard need for data scientists.

Learn about future of Machine Learning

Q.12. What is meant by logistic regression?
It is a method for fitting a regression curve, y = f(x) when y is a categorical variable.
It is a classification algorithm. We use it to predict a binary outcome (1 / 0, Yes / No, True / False) given a set of independent variables. To represent binary / categorical outcome, we use dummy variables.
It is a regression model in which the response variable was categorical values such as True/False or 0/1. Thus, it actually measures the probability of a binary response.
To perform logistic regression in R, use the command:

glm( response ~ explanantory_variables , family=binomial)

Learn more about logistic regression in R.

Q.13. What is meant by Poisson regression?
Poisson Regression Data is often collected in counts. Hence, many discrete response variables have counted as possible outcomes. While binomial counts are the number of successes in a fixed number of trials, n.
Poisson counts are the number of occurrences of some event in a certain interval of time (or space). Apart from this, Poisson counts have no upper bound and binomial counts only take values between 0 and n.
To perform logistic regression in R, we use the command:

glm( response ~ explanantory_variables , family=poisson)

Learn more about Poisson regression and Binomial regression in R.

Q.14. What is Survival Analysis?
a. Model time to event (esp. failure)a.used in medicine, biology, actuary, finance, engineering, sociology, etc.
b. Able to account for censoring
c. Able to compare between 2+ groups
d. Able to access relationship between covariates and survival time
Install Package

Description of the parameters −
1. Time is the follow-up time until the event occurs.
2. Generally, the event indicates the status of occurrence of the expected event.
3. Moreover, the formula is the relationship between the predictor variables.
Q.15. What do you mean by survival analysis in R?
a. Package: survival >library (survival)
b. Create a survival subject: Surv
c. Kaplan – Meier Estimator: survfit
d. Mantel-Haenzel Test: survdiff
e. Cox Model: coxph
Read more about survival analysis in detail.

4. Top Data Science Interview Questions and Answers

These are the top Data Science Interview questions that you must know to crack a data science interview. You can also use these questions for supporting your other answers in Data Science Interview.

Q.16. How to create survival object in R?
Created by Surv function

>Surv (time, time2, event, type=c

(‘right’, ‘left’, ‘interval’,
(‘right’, ‘left’, ‘interval’,
‘counting’, ‘interval2’), origin=0)

Q.17. What are Principal components?
It is a normalized linear combination of the original predictors in a data set. We can write the principal component in following way:
Z¹ = Φ¹¹X¹ + Φ²¹X² + Φ³¹X³ + …. +Φp¹Xp Where, Z¹ is first principal component
Φp¹ is the loading vector comprising of loadings (Φ¹, Φ²..) of a first principal component. Also, the loadings are constrained to a sum of square equals to 1. This is because the large size of loadings may lead to large variance. It also defines the direction of the principal component (Z¹) along which data varies the most. Moreover, it results in a line in p dimensional space which is closest to the n observations. We can measure closeness using average squared Euclidean distance.
X¹..Xp is normalized predictors. Normalized predictors have mean equals to zero and standard deviation equals to one.
Learn Principal components in R in detail.

Q.18. What is need of principal component analysis?

The main aim of principal components analysis is to report hidden structure in a data set. In so doing, we may be able to
a. Basically, it is prior to identifying how different variables work together.
b. Then reduce the dimensionality of the data.
c. Afterwards, it decreases redundancy in the data.
d. Filter some of the noise in the data.
e. Then compress the data.
f. Moreover, prepare the data for further analysis using other techniques.

Q.19. What functions are required to principal analysis in R?
a. prcomp() (stats)
b. princomp() (stats)
c. PCA() (FactoMineR)
d. dudi.pca() (ade4)
e. acp() (amap)
Read more about functions in detail.

Q.20. Name the methods for General component analysis and explain them?

There are two methods :
a. Spectral decomposition: Which examines the covariances/correlations between variables.
b. Singular value decomposition: Which examines the covariances/correlations between individuals. We use the function princomp() for the spectral approach. Also, use the functions prcomp() and PCA() in the singular value decomposition.

Q.21. What is meant by R statistics?

R Statistics concerns data; their collection, analysis, and interpretation. It has following two types:
Descriptive statistics concerns the summarization of data. Also, we have a data set. We would like to describe the data set in many ways. Basically, this entails calculating numbers from the data, called descriptive measures.

For Example:

percentages, sums, averages, and so forth.
Inferential statistics do more. There is an inference associated with the data set. Also, a conclusion is drawn about the population from which the data originated.
Learn more about R statistics.

Q.22. Explain types of statistical data?

Whenever we are working with statistics it’s very important to recognize the different types of data:
1. numerical (discrete and continuous),
2. categorical, and
3. Ordinal.
Data are the actual pieces of information that you collect through your study.
Most data fall into one of two groups: numerical or categorical

1.Numerical Data– It contains data which have to mean as a measurement. Such as a person’s height, weight, IQ, or blood pressure.

Numerical data can be further broken into two types:
a. Discrete
b. Continuous.

a. Discrete data – Basically, it represents items that can be counted. Basically, they take on possible values that can be listed out. Moreover, the list of possible values may be fixed or it may go to infinity.

b. Continuous data – It represents measurements. Also, their possible values cannot be counted. Although, it can only be described using intervals on the real number line.

2.Categorical Data– We use it too represents characteristics.
Such as :

  • a person’s gender,
  • marital status,
  • hometown.

It can take on numerical values:
Such as :

  • “1” indicating male, and
  • “2” indicating female.

But those numbers don’t have mathematical meaning. You couldn’t add them together.

For Example: Qualitative data is another name for categorical data. Moreover, it is called as Yes/No.Also, there is one more data called Ordinal Data.
Let’s begin to learn this:

3.Ordinal data- Basically, it mixes both numerical and categorical data. The data falls into the category, but the numbers that are placed on the categories must have to mean.
For example: We have to rate a restaurant on a scale from 0 to 4 stars gives ordinal data.
They are often treated as categorical. Although, we have to order the groups whenever it requires creating graphs and charts.

5. Advanced Data Science Interview Questions and Answers

These are little advanced Data Science Interview Questions for Experienced, How ever Freshers can also refer these interview questions for data science for advanced knowledge.

Q.23. What are distance measures in R statistics?
Distance Measures (Similarity, dissimilarity, correlation).
Basically, we can consider it as mathematical approaches. Also, it helps us to measure the distance between the objects. Further, we can use computing distance to compare the objects. Now, we can conclude three different standpoints on the basis of comparison such as:
1. Similarity
2. Dissimilarity
3. Correlation
Similarity– it is measure that ranges from 0 to 1 [0, 1] Dissimilarity- it is measured that range from 0 to INF [0, Infinity] Correlation– it is measures that range from +1 to -1 [+1, -1] Along with this Data Science Interview Questions, I have one more link to Data Science Interview Questions which will help you in interview preparation:

Data science Interview Questions

Q.24. What is correlation in R?
Basically, it is a type of technique. Also, we use it for investigating the relationship between two quantitative, continuous variables.
Positive correlation – In this, both variables increase or decrease together.
Negative correlation – In this as one variable increases, so the other decreases.

Q.25. What is term Pearson’s Correlation Coefficient?
This is a statistical technique. Also, provides us a number. That tells how strongly or weekly the relationship between the objects. Basically, it is not a measure describing the distance. But a measure describes the bound between the objects. Also, it is used to represent correlation value by small letter ‘r’ and ‘r’ can range from -1 to +1.

  • If r is close to 0, it means there is no relationship between the objects.
  • Now, if r is positive, it means that as one object gets larger the other gets larger.
  • If r is negative that means one gets larger, the other gets smaller.

r value =

  • +.70 or higher Very strong positive relationship
  • +.40 to +.69 Strong positive relationship
  • +.30 to +.39 Moderate positive relationship
  • +.20 to +.29 weak positive relationship
  • +.01 to +.19 No or negligible relationship

0 No relationship

  • -.01 to -.19 No or negligible relationship
  • -.20 to -.29 weak negative relationship
  • -.30 to -.39 Moderate negative relationship
  • -.40 to -.69 Strong negative relationship
  • -.70 or higher Very strong negative relationship

In today’s world, there are several methods for computing correlation measures ‘r’. Also, out of which Pearson’s correlation coefficient has commonly used a method.

Q.26. Name Methods to Calculate Distance Measure?

  • Euclidean distance
  • Taxicab or Manhattan distance
  • Minkowski
  • Cosine similarity
  • Mahalanobis distance
  • Pearson’s Correlation Coefficient(discussed in above paragraph)

Q.27. Explain calculate distance methods briefly?

a. Euclidean distance – Basically, it is a classical method. Also, it helps to compute a distance between two objects A and B in Euclidean space. In this geometry, we can find the distance between points by traveling along the lines. Basically, the lines must be connected through the points. Inherently in the calculation, you use the Pythagorean Theorem to compute the distance.

b.Taxicab or Manhattan distance – It is like a Euclidean distance. Although, there is only one difference. Moreover, we can calculate the distance by traversing. Also, we have to do traversing the vertical & horizontal line in the grid-based system.

c. Minkowski – Basically, this distance is a metric on Euclidean space. Also, we can consider it as s a generalized of Euclidean and Manhattan distance.

d. Cosine Similarity – Basically, it is a measure that calculates the cosine of the angle between two vectors. Basically, this metric is a measurement of orientation and not size. Also, we can use it as a comparison between documents the angle between them.

e. Mahalanobis distance – Basically we use it to measure a distance between the two groups of object. Also, we can graphically represent an idea of distance measure. Although, it helps in better understanding. Basically, we can use this type of distance measure. Also, it is helpful for classification and clustering.

Q.28. What is meant by lattice?

It is a powerful and elegant high-level data visualization system. That is being inspired by Trellis graphics. Although, it is being designed with an emphasis on multivariate data. Also, it allows easy conditioning to produce “small multiple” plots.

You can follow this link for further R interview questions and answers:
Data Science Interview Question-Answer

Q.29. What is meant by lattice graphs?
The lattice package was written by Deepayan Sarkar. Also, he provides better defaults. It also provides the ability to display multivariate relationships. And trying to improve on-base R graphics. Moreover, this package supports the creation of trellis graphs –
1. basically the graphs that display a variable or
2. also, the relationship between variables, conditioned on one or
3. more other variables.
The typical format is:
graph_type(formula, data=)
Basically, first we will select graph_type from the listed below. Then this formula specifies the variable(s) to display and any conditioning variable.

Read more about graphs in detail.

Q.30. What are independent graphics subsystems?
a. Basically, traditional graphics available in R from the beginning. That are a rich collection of tools that are not very flexible.
b. Grid graphics recent (2000) Low-level tool, flexible
c. Grid forms the basis of two high-level graphics systems:
Lattice: based on Trellis graphics (Cleveland) ggplot2: inspired by “Grammar of Graphics”(Wilkinson)

So, this was all in Data Science Interview Questions and Answers.

6. Conclusion

As a result, this Data Science Interview Questions contains all major topics for interviews in Data science like data scientist interview questions, data analytics interview questions, data architect interview questions, python data science interview questions, r data science interview questions etc. I hope this Data Science Interview Questions will help you in the interview preparation. Furthermore, if you feel any query in this Data Science Interview Questions, you can freely ask in comment box.
Learn more interview questions on Data Science.

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