Top 100 Data Science Interview Questions and Answers

                                   Top 100 Data Science Interview Questions and Answers

 To help you to prepare for an interview I have prepared some of the questions to make your interview as simple.there are the most commonly used questions in Data Science.

You are a Data Science aspirant looking forward to becoming a Data Scientist then these questions are going to help you to crack your next Data Science interview and achieve your dream job.

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1. How would you create a taxonomy to identify key customer trends in unstructured data?

The best way to approach this question is to mention that it is good to check with the business owner and understand their objectives before categorizing the data. Having done this, it is always good to follow an iterative approach by pulling new data samples and improving the model accordingly by validating it for accuracy by soliciting feedback from the stakeholders of the business. This helps ensure that your model is producing actionable results improving over time.

2. Python or R – Which one would you prefer for text analytics?

The best possible answer for this would be Python because it has a Pandas library that provides easy to use data structures and high-performance data analysis tools.

3. Which technique is used to predict categorical responses?

The classification technique is used widely in mining for classifying data sets.

4. What are Recommender Systems?

A subclass of information filtering systems that are meant to predict the preferences or ratings that a user would give to a product. Recommender systems are widely used in movies, news, research articles, products, social tags, music, etc.

5. What is power analysis?

An experimental design technique for determining the effect of a given sample size.

6. What is Collaborative filtering?

The process of filtering used by most of the recommender systems to find patterns or information by collaborating viewpoints, various data sources, and multiple agents.

7. What is Machine Learning?

The simplest way to answer this question is – we give the data and equation to the machine. Ask the machine to look at the data and identify the coefficient values in an equation.
For example for the linear regression y=mx+c, we give the data for the variable x, y and the machine learns about the values of m and c from the data.

8. During analysis, how do you treat missing values?

The extent of the missing values is identified after identifying the variables with missing values. If any patterns are identified the analyst has to concentrate on them as it could lead to interesting and meaningful business insights. If there are no patterns identified, then the missing values can be substituted with mean or median values (imputation) or they can simply be ignored. There are various factors to be considered when answering this question Understand the problem statement, understand the data and then give the answer. Assigning a default value which can be the mean, minimum or maximum value. Getting into the data is important. If it is a categorical variable, the default value is assigned. The missing value is assigned a default value. If you have a distribution of data coming, for normal distribution give the mean value. Should we even treat missing values is another important point to consider. If 80% of the values for a variable are missing then you can answer that you would be dropping the variable instead of treating the missing values.

9. How can outlier values be treated?

Outlier values can be identified by using univariate or any other graphical analysis method. If the number of outlier values is few then they can be assessed individually but for a large number of outliers, the values can be substituted with either the 99th or the 1st percentile values. All extreme values are not outlier values.

 The most common ways to treat outlier values  are :

• To change the value and bring in within a range
• To just remove the value.

10. What is the goal of A/B Testing?

It is a statistical hypothesis testing for a randomized experiment with two variables A and B. The goal of A/B Testing is to identify any changes to the web page to maximize or increase the outcome of interest. An example of this could be identifying the click-through rate for a banner ad.

11. Why data cleaning plays a vital role in the analysis?

Cleaning data from multiple sources to transform it into a format that data analysts or data scientists can work with is a cumbersome process because – as the number of data sources increases, the time take to clean the data increases exponentially due to the number of sources and the volume of data generated in these sources. It might take up to 80% of the time for just cleaning data making it a critical part of the analysis task.

12. Differentiate between univariate, bivariate and multivariate analysis?

These are descriptive statistical analysis techniques which can be differentiated based on the number of variables involved at a given point of time. For example, the pie charts of sales based on territory involve only one variable and can be referred to as univariate analysis. If the analysis attempts to understand the difference between 2 variables at the time as in a scatterplot, then it is referred to as bivariate analysis. For example, analyzing the volume of sales and spending can be considered as an example of bivariate analysis. Analysis that deals with the study of more than two variables to understand the effect of variables on the responses is referred to as multivariate analysis.

13. What do you understand by the term Normal Distribution?

Data is usually distributed in different ways with a bias to the left or to the right or it can all be jumbled up. However, there are chances that data is distributed around a central value without any bias to the left or right and reaches normal distribution in the form of a bell-shaped curve. The random variables are distributed in the form of the asymmetrical bell-shaped curve.

14. What are Interpolation and Extrapolation?

Estimating a value from 2 known values from a list of values is Interpolation. Extrapolation is approximating a value by extending a known set of values or facts.

15. Are the expected value and mean value different?

They are not different but the terms are used in different contexts. Mean is generally referred to when talking about a probability distribution or sample population whereas expected value is generally referred to in a random variable context.

For Sampling Data: The mean value is the only value that comes from the sampling data. Expected Value is the mean of all the means i.e. the value that is built from multiple samples. The expected value is the population means.

For Distributions: Mean value and Expected value are the same irrespective of the distribution, under the condition that the distribution is in the same population

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