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40+ Data Science Interview Questions and Answers
Table of Content
 What does “Central Tendency” mean?
 Name the different measures of data used to evaluate the Central Tendency?
 Define Mean?
 What is the Median calculation?
 How can you define Mode?
 What disadvantage(s) does Mean have. Name one?
 Measure of Dispersion mean in statistics mean?
 Mention all the measures used to analyze Dispersion?
 Define the term Variance?
 Compare Standard Deviation and Variance?
 What are the disadvantages of Variance?
 Define Range. How to evaluate it?
 How can you define Skewness?
 Mention the thumb rules used for the interpretation of the Skewness?
 What do you understand by Kurtosis?
 What are the thumb rules to interpret Kurtosis?
 What does Right Skewed mean?
 Left (or) Negative Skewed data mean?
 Central tendency measure which changes with any single value in the data is
 Variable X has a median of 50, The distribution of the data is positively skewed. Which of the following statement is true?
 Can the measure of dispersion ‘Standard deviation’ be negative?
 Does Standard deviation get influenced by Outliers?
 What are Measurement Levels?
 What does Nominal type in measurement levels mean?
 What is the ordinal measurement level?
 What does Interval measurement level represent?
 What is the Ratio?
 What is the Factor variable?
 What is Random Variable?
 What is Probability?
 What Is Conditional Probability?
 What are Independent Events?
 Multiplication theorem on probability?
 Addition theorem on probability?
 What is Population?
 What is the Sampling Frame in the SRS sampling technique?
 What is Sampling Funnel?
 What is Excepted value?
 Variance of a probability distribution?
 What is Descriptive Statistics?
 What is Inferential statistics?
 What is Sample?
 What are the different types of Sampling methods?
 What Is the Moment?
 What Is Covariance?
 Describe Inferential Statistics with an example?
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If your ambition is to become a Professional Data Scientist, then you should have proficient knowledge of technical skills. If you are choosing to do a Data Science course in any of the training institutes make sure that they provide you with handson experience with realtime projects. This certainly helps you to know indepth about the concepts, applications of statistical tools and boosts your confidence.
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To crack the interview is not easy, as the interviewer will not ask specific questions, he can raise questions from any topic. So we suggest the candidates appearing for Data Scientist interviews should be thorough with the programming languages, statistics, and data modeling concepts. Apart from these, communication skills also play a major role in the selection process.
Here are the important interview questions that every candidate should prepare for it. This will effectively guide you and help you to brush up your knowledge. These questions are designed by the industry experts covering all the important topics. All the very best!!!!
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What does “Central Tendency” mean?
The Mass/Concentration of the data is termed as the Central Tendency.

Name the different measures of data used to evaluate the Central Tendency?
Mean, Median, and Mode are the three measures that are used for calculating the Central Tendency.

Define Mean?
The average of all the data points in a data is called as mean.

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What is the Median calculation?
Median is the middlemost value of all the data points after sorting.

How can you define Mode?
The mode is known to be the most frequently occurring value, commonly used for working on categorical data.

What disadvantage(s) does Mean have. Name one?
The biggest disadvantage is that the mean gets influenced by the outliers (also known as extreme values).

Measure of Dispersion mean in statistics mean?
Dispersion is the term used for the spread of data.

Mention all the measures used to analyze Dispersion?
The measured used for the analysis of Dispersion of data are Variance, Standard Deviation, and Range.

Define the term Variance?
Variance can be explained as the measure of the spread of the data from the center. It is calculated as the average squared values of the distance of each data point from the mean of the data.

Compare Standard Deviation and Variance?
Variance calculates the spread of the data from the mean, but for the calculation distance, square values are considered, hence the units get squared. To bring the units back to the original level we apply square root on top of variance, this value is known as Standard Deviation.

What are the disadvantages of Variance?
In Variance, we calculate the square for distance, but along with the distance, the unit also gets squared, so to get back units we use standard deviation.

Define Range. How to evaluate it?
Maximum – Minimum is the range for any given data. It represents the limits of the spread for the data. Range = (Max – Min)

How can you define Skewness?
Skewness talks about the symmetry, concentration of data being more at one place than the other. The asymmetric distribution of the data points in data is known as Skewness.

Mention the thumb rules used for the interpretation of the Skewness?
The data is said to be highly skewed, when it is between > +1 & < 1, moderately skewed, when it is between 0.5 & +1 / 0.5 & 1, and approximately symmetric when it is between 0.5 & 0.5.

What do you understand by Kurtosis?
Kurtosis defines the peakedness of the distribution in the dataset.
For Symmetric distributions, if the curve has a wider peak and thinner tails, it implies negative Kurtosis.
For Symmetric distributions, if the curve has a narrow peak and wider tails, it indicates positive Kurtosis.

What are the thumb rules to interpret Kurtosis?
If k=3, it follows a normal distribution
If k>3, it is called Leptokurtic distribution
Its central peak higher and sharper and its tails are longer and fatter.
If K<3, it is called Platykurtic distribution
The central peak is wide compared to a normal distribution and lowers as well, and the tails are shorter and thinner.

What does Right Skewed mean?
Skewness is a measure that represents the mass of the data distributed towards the right side from the centre. A histogram on the data will also enable us to understand the existence of extreme value in the dataset

Left (or) Negative Skewed data mean?
Skewness is a measure that represents the mass of the data distributed towards the left side from the centre. A histogram on the data will also enable us to understand the existence of extreme value in the dataset

Central tendency measure which changes with any single value in the data is
Mean – Mean is calculated as the sum of the data points over the total number. The sum gets altered with a change in any single value in the data.

Variable X has a median of 50, The distribution of the data is positively skewed. Which of the following statement is true?
 Mean is greater than Median
 The mode is less than Median
 Mean = Median
 Mean is lesser than the Median

Can the measure of dispersion ‘Standard deviation’ be negative?
The squared distance from mean to each data point is used in calculating standard deviation. As we are squaring the values, we cannot get a negative value.

Does Standard deviation get influenced by Outliers?
The distance of the data points from the centre would be affected by outliers. Yes, standard deviation would get effected.

What are Measurement Levels?
Measurement levels are a way to interpret the calculations that can be applied on the data for extracting the information. There are 4 levels of measurements that we can learn: Nominal, Ordinal, Interval, Ratio.

What does Nominal type in measurement levels mean?
Name of Categories (There is no natural order among categories) There is no inherent order.
Eg: Color names, Gender

What is the ordinal measurement level?
Categories that have Particular order (Inherent order).
Eg: Shirt size : S, M, L, XL, XXL.

What does Interval measurement level represent?
The Interval level is a numeric measure of the data. This numeric measure will explain the relative value of a data point in the data set. The values will always lie in a defined boundary. Hence these values are said to be a measure of local scale.
E.g.:  Temperature, and Date.

What is the Ratio?
Ratio data is very much like the interval data – the values must be numerical where the difference between points is standardized and quite meaningful. Whereas, in order for data to be considered as the ratio data, it must have a true zero value, which means ratio data cannot have negative values.
Eg:  Height, Weight

What is the Factor variable?
The Factor variable is nothing, but it has limited values (or) labels.
Eg: Month(Jan, Feb, …., Dec)  Only 12 values for Month variable.

What is Random Variable?
The values which vary randomly. For example, if any experiment (flipping of a coin, or rolling of a die) has the outcome bounded to be from a given set of values, and is not fixed, the result will change every time the experiment is conducted. Such an outcome is termed as Random Variable

What is Probability?
No of Interested Events/Total no of events.

What Is Conditional Probability?
It can be defined as the probability of a conditional event.
P(AB) = P(AB)/P(B) (or) P(BA) = P(AB)/P(A)
P(AB) Prob of A when B has already occurred.
P(BA) Prob of B when A has already occurred.

What are Independent Events?
There is no dependency between the events.

Multiplication theorem on probability?
P(AB) = P(A).P(B)

Addition theorem on probability?
P(AUB) = P(A) + P(B)  P(AB)

What is Population?
All the data in the universe that satisfy criteria.

What is the Sampling Frame in the SRS sampling technique?
Select favourable data from the population.

What is Sampling Funnel?
It is the process of choosing a subset of the data from population. The flow will be from population > Sampling Frame > SRS > Sample.

What is Excepted value?
Mean of distribution or Average of values when the distribution is given. It can be understood as the average outcome for an experiment that is conducted for an infinite time.

Variance of a probability distribution?
Descriptive statistics is a process of analysing the business data by applying statistical calculations and plots to derive summary. Descriptive Statistics methods include displaying, organizing, and describing the data.

What is Descriptive Statistics?
Descriptive statistics is a process of analysing the business data by applying statistical calculations and plots to derive summary. Descriptive Statistics methods include displaying, organizing, and describing the data.

What is Inferential statistics?
Inferential Statistics can be seen as the procedure that allow researchers to make inferences about a population based on findings from a sample.

What is Sample?
In Statistics, a sample is a set of or a portion of collected or processed data from a statistical population by a structured and defined procedure, and the elements within the sample are known as sample points. When data is collected in a statistical study for only a portion or subset of all elements of interest, we are using a Sample.

What are the different types of Sampling methods?
Cluster Sampling: In the Cluster sampling method the population will be divided into groups or clusters.

What Is the Moment?
The first moment is called the Mean which describes the center of the distribution. Mean is a representative value for the dataset which can be inferred as the characteristics of the entire dataset.
The Second moment describes the spread of the data from the center, which is calculated as Variance (or Standard Deviation)
The Third Moment also talks about the spread of the data only, the difference from variance is that it describes the shape of a distribution. We can know the focus in the data towards the left or right side from the center, we calculate the third moment as skewness.

What Is Covariance?
Covariance is a measure to understand how much two variables that change together.

Describe Inferential Statistics with an example?
Inferential statistics is a study of deriving conclusions on the entire population based on a sample (subset) of the data.
Example of Inferential Statistics: Suppose we have asked five classmates of the same grade or year about their marks. Based on this information, we can conclude the average marks of all students in their class.
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FAQs
Preparing for Data Science interview questions is unlike preparing for technical job interviews. Here are some key points to remember when you are going to attend an interview for the role of Data Scientist:
 Research the company and your role in it.
 Review your portfolio well and be updated on your past projects.
 Brush up on foundational concepts and practice technical skills
 Take mock interview sessions and online tests to know what you are expected to do.
Be prepared to impress prospective employees with your thorough knowledge of the field of Data Science. Here are a few popular interview questions:
 Difference between data analytics and Data Science.
 How is logistic regression done?
 Building of a decision tree and random forest model
 Difference between univariate, bivariate, and multivariate analysis.
 What are dimensionality reduction and its benefits?
You can find a list of other interview questions on the 360DigiTMG page. Go through the list and prepare accordingly to make your interview process even more accessible to crack.
Cracking a Data Science interview is no walk in the park. One has to possess indepth knowledge of all the concepts and have expertise in all technical aspects of the field. Yes, it is common for recruiters to ask questions based on canonical algorithms in the interview to check the individual's knowledge and presence of mind. But there are no limitations on the type of questions the interviewer can ask.
If you're stepping into the Data Science interview for freshers, remember that statistics is an essential field in learning Data Science. It is common for recruiters to test your knowledge on the same during interviews. Here are a few crucial statistical concepts that you must know before going for the interview:
 Central Limit Theorem
 Hypothesis testing
 Assumptions of Normality
 Outlier and inlier
 Importance of Statistics in Data Science
Data Science interviews can be tricky if you learn how to demonstrate your skills in various real data problems. To crack the interviews, candidates must familiarize themselves with popular coding algorithms, datasets, distributions, and other metrics. There are several Data Science interview questions, and PDF/docs available online that can give you a basic idea about these interviews
There are different kinds of openended interview questions. The most common ones are behavioral, anecdotal, and situational questions. Openended questions mostly have a "yes" or "no" answer, and there's no right or wrong answer. Phase your answers so that they highlight your personality and make you the perfect fit for the company culture.
Giving an interview is a nervewracking task, mainly for the datascience field because of its booming potential. Here are a few other websites that help you prepare better for the interview:
 Machine hack
 Glassdoor
 AlgoExpert
 Udacity
 Brilliant.org
 Leetcode
 Strata Scratch
Data Science is the study and analysis of Big Data. SQL comes into the picture when the need to extract data from a database comes up. Data scientists use SQL as their standard tool to create and test environments. It is a powerful tool that enables you to perform several functions efficiently. That is why Data Science interviews are incomplete without SQLrelated interview questions.
Fifteen days to a month is the maximum time required to prepare for a Data Science interview. Brush up on all the technical aspects, study common interview questions, give mock interviews and prepare your portfolio. Allocate the final week to practice sample questions that are challenging for you. Be prepared for questions related to Statistics, probability, data wrangling, and programming concepts.
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