40+ Data Science Interview Questions and Answers

Data Science is boosting across the world. Harvard university quoted that Data Scientist is the “sexiest job”. As per Glassdoor, Data Scientist ranks among the top 25 jobs in the world. The Data Scientists are considered as rock stars of the technology industry.

As many companies are leveraging massive data, to extract valuable insights that can improve their products, services, and in turn lead to revenue generation. Data Scientist is the one who can collect, analyze, and optimize the data. Many companies are looking for professional Data Scientists who can fill this need, but there is a lack of Data Scientists globally.

This is the era of Machine Learning, Big Data, Artificial Intelligence, and IoT. Data is considered to be the most treasured one which cannot be ignored. Where there is data, there will be a need for qualified Data Scientists.

Jobs in the field of Data Science are burgeoning, creating new opportunities. The various job titles include Data Scientist, Data Analyst, Senior Data Scientist, Analytics, Data Science, Machine Learning Engineer and many more

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Data Scientists earn lucrative salaries around the world. The average salary for a Data Scientist is ₹9,71,154 in India, $121859 in the United States, and in Malaysia is RM 60084 per year.

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 hands-on experience with real-time projects. This certainly helps you to know in-depth 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 middle-most 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(A|B) = P(AB)/P(B) (or) P(B|A) = P(AB)/P(A)

  P(A|B) Prob of A when B has already occurred.

  P(B|A) 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.