Top 15 Frequently Asked Data Science Interview Questions and Answers.

Posted by sravan cynixit on January 7th, 2020

If you are in search of Data Science interview questions, then this is the right place for you to alight. Preparing for an interview is definitely quite challenging and complicated. It is very problematic with respect to what data science interview questions you will be inquired about. Unquestionably, you have heard this saying a lot of times, that Data science is called the most hyped up job of the 21st century. The demand for data scientists has been growing drastically over the years due to the increased importance of big data For Data Science Certification

Data Science Interview Questions & Answers

Many predictions have been made for the role of a data scientist, and according to IBM’s predictions, the demand for this role will soar 28% by 2021. To give you the much of the time asked Data science interview questions, this article has been structured strikingly. We have segregated the most important interview questions based on their complexity and belonging. This article is the perfect guide for you as it contains all the questions you should expect; it will also help you to learn all the concepts required to pass a data science interview.

Q-1: What is Data Science, and why is it important?

The main section in this rundown is presumably one of the most fundamental ones. However, the majority of the interviewers never miss this question. To be very specific, data science is the study of data; a blend of machine learning theories or principles, different tools, algorithms are also involved in it. Data science also incorporates the development of different methods of recording, storing, and analyzing data to withdraw functional or practical information constructively. This brings us to the main goal of data science that is to use raw data to unearth concealed patterns.

Data Science is essential for improved marketing. To analyze their marketing strategies, companies make major use of data and thereby create better advertisements. By analyzing customers’ feedback or response, decisions can also be made.

Q-2: What is Linear Regression?

Linear Regression is a supervised learning algorithm where the score of a variable M is predicted statistically by using the score of a second variable N and thereby showing us the linear relationship between the independent and dependent variables. In this case, M is referred to as the criterion or dependent variable, and N is referred to as the predictor or independent variable.

The main purpose that linear regression serves in data science is to tell us how two variables are related to producing a certain outcome and how each of the variables has contributed to the final consequence. It does this by modeling and analyzing the relationships between the variables and therefore shows us how the dependent variable changes with respect to the independent variable.

Q-3: What are Interpolation and Extrapolation?

Let us move towards the next entry of Data Science interview questions. Well, interpolation is to approximate value from two values, which are chosen from a list of values, and extrapolating is estimating value by extending known facts or values beyond the scope of information that is already known.

So basically, the main difference between these two is that Interpolation is guessing data points that are in the range of the data that you already have. Extrapolation is guessing data points that are beyond the range of data set.

Q-4: What is a confusion matrix?

This is a very commonly asked data science interview question. To answer this question, your answer can be sentenced in this manner; that is, we use Confusion Matrix to estimate the enactment of a classification model, and this is done on a set of test data for which true values are known. This is a table that tabularizes the actual values and predicted values in a 2×2 matrix form.

True Positive: This represents all the accounts where the actual values, as well as the predicted values, are true.

True Negative: This represents all of those records where both the actual and predicted values are both false.

False Positive: Here, the actual values are false, but the predicted values are true.

False Negative: This represents all the records where the actual values are verifiable or true, and the predicted values are incorrect.

Q-5: What do you understand by a decision tree?

This is one of the top data science interview questions, and to answer this, having a general thought on this topic is very crucial. A decision tree is a supervised learning algorithm that uses a branching method to illustrate every possible outcome of a decision, and it can be used for both classification and regression models. Thereby, in this case, the dependent value can be both a numerical value and a categorical value.

There are three unique sorts of nodes. Here, each node denotes the test on an attribute, each edge node denotes the outcome of that attribute, and each leaf node holds the class label. For instance, we have a series of test conditions here, which gives the final decision according to the outcome.

Q-6: How is Data modeling different from Database design?

This could be the next important data science interview question, so you need to be prepared for this one. To demonstrate your knowledge of data modeling and database design, you need to know how to differentiate one from the other.

Now, in data modeling, data modeling techniques are applied in a very systematic manner. Usually, data modeling is considered to be the first step required to design a database. Based on the relationship between various data models, a conceptual model is created, and this involves moving in different stages, starting from the conceptual stage to the logical model to the physical schema.

Database design is the main process of designing a particular database by creating an output, which is nothing but a detailed logical data model of the database. But sometimes, this also includes physical design choices and storage parameters.

Q-7: What do you know about the term “Big Data”?

Do I even have to mention the importance of this particular interview question? This is probably the most hyped-up data analytics interview question and along with with with that a major one for your Big Data interview as well.

Big Data is a term that is associated with large and complex datasets, and therefore, it cannot be handled by a simple relational database. Hence, special tools and methods are required to handle such data and perform certain operations on them. Big data is a real life-changer for businessmen and companies as it allows them to understand their business better and take healthier business decisions from unstructured, raw data.

Q-8: How is Big Data analysis helpful in increasing business revenue?

A must-ask question for your Data scientist interview as well as your Big Data interviews. Nowadays, big data analytics are used by many companies, and this is helping them greatly in terms of earning additional revenue. Business companies can differentiate themselves from their competitors and other companies with the help of big data analysis, and this once again helps them to increase revenue.

The preferences and needs of customers are easily known with the help of big data analytics, and according to those preferences, new products are launched. Thus, by implementing this, it allows companies to encounter a significant rise in revenue by almost 5-20%.

Q-9: Will you optimize algorithms or code to make them run faster?

This is another most recent Data Science interview question that will likewise help you in your big data interview. The answer to this data science interview question should undoubtedly be a “Yes.” This is because no matter how efficient a model or data we use while doing a project, what matters is the real-world performance.

The interviewer wants to know whether you had any experience in optimizing code or algorithms. You do not have to be scared. To accomplish and impress the interviewers in the data science interview, you just have to be honest about your work.

Do not hesitate to tell them if you do not have any experience in optimizing any code in the past; only share your real experience, and you will be good to go. If you are a beginner, then the projects you have previously worked on will matter here, and if you are an experienced candidate, you can always share your involvement accordingly.

Q-10: What is A/B Testing?

A/B testing is a statistical hypothesis testing where it determines whether a new design brings improvement to a webpage, and it is also called “split testing.” As the name recommends, this is essentially a randomized investigation with two parameters A and B. This testing is also done to estimate population parameters based on sample statistics.

A comparison between two webpages can also be done with this method. This is done by taking many visitors and showing them two variants – A and B. the variant which gives a better conversion rate wins.

Q-11: What is the difference between variance and covariance?

This question serves as a primary role in data science interview questions as well as statistics interview questions, and so it is very important for you to know how to tactfully answer this. To simply put it in a few words, variance and covariance are just two mathematical terms, and they are used very frequently in statistics.

Some data analytics interview questions also tend to include this difference. The main dissimilarity is that variance works with the mean of numbers and refers to how spaced out numbers are concerning the mean whereas covariance, on the other hand, works with the change of two random variables concerning one another.

Q-12: What is the difference between the Do Index, Do While and the Do until loop? Give examples.

The chance of this question being asked to you in your data science and data analyst interview is extremely high. Now firstly, you have to be able to explain to the interviewer what you understand by a Do loop. The job of a Do loop is to execute a block of code recurrently based on a certain condition. The image will give you a general idea of the workflow.

Do Index loop: This uses an index variable as a start and stops value. Until the index value reaches its final value, the SAS statements get executed repeatedly.

Do While loop: This loop works by using a while condition. When the condition is true, this loop keeps executing the block of code until the condition becomes false and is no longer applicable, and the loop terminates.

Do Until Loop: This loop uses an until condition which executes a block of code when the condition is false and keeps executing it until the condition becomes true. A condition that is true causes the loop to get terminated. This is just the opposite of a do-while loop.

Q-13: What are the five V’s of Big Data?

The answer to this Data Science interview question would be a little detailed with a focus on different points. The five V’s of big data are as follows:

Volume: Volume represents the amount of data that is increasing at a high rate.

Velocity: Velocity determines the rate at which data grows in which social media plays a huge role.

Variety: Variety denotes the different data types or formats of data users such as text, audio, video, etc.

Veracity: Large volumes of information are hard to deal with, and subsequently, it brings inadequacy and irregularity. Veracity alludes to this evasion of accessible information, which emerges from the overwhelming volume of information.

Value: Value refers to the transformation of data into value. Business companies can generate revenue by turning these accessed big data into values.

Q-14: What is ACID property in a database?

In a database, the reliable processing of the data transactions in the system is ensured using this property. Atomicity, Consistency, Isolation, and Durability is what ACID denotes and represents.

Atomicity: This alludes to the exchanges which are either totally effective or have flopped totally. For this situation, a solitary activity is alluded to as an exchange. In this manner, regardless of whether a solitary exchange fizzles, at that point, the whole exchange is influenced.

Consistency: This feature ensures that all the validation rules are met by the data, and this makes sure that without completing its state, the transaction never leaves the database system.

Isolation: This function allows transactions to be independent of each other as it keeps the transactions separated from each other until they are completed.

Durability: This ensures the submitted exchanges are rarely lost and in this manner, ensures that regardless of whether there is an unusual end like a power misfortune or crash, the server can recuperate from it.

Q-15: What is Normalization? Explain different types of Normalization with advantages

Standardization is the way toward sorting out information which maintains a strategic distance from duplication and repetition. It comprises of numerous progressive levels called normal forms, and every normal form relies upon the past one. They are:

  1. First Normal Form (1NF): No repeating groups within the rows
  2. Second Normal Form (2NF): Every non-key (supporting) column value is dependent on the whole primary key.
  3. Third Normal Form (3NF): Solely depends on the primary key and no other supporting column.
  4. Boyce- Codd Normal Form (BCNF): This is the advanced version of 3NF.

Some advantages are:

  • More compact database
  • Allows easy modification
  • Information found more quickly
  • Greater flexibility for queries
  • Security is easier to implement

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sravan cynixit

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sravan cynixit
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