Updated June 28, 2023

## Introduction to Data Science Interview Questions and Answers

Below is the list of 2023 Data Science Interview Questions that are mostly asked in an interview as follows:

### Part 1 – Data Science Interview Questions (Basic)

This first part covers basic Interview Questions and Answers.

#### Q1. What is Data Science?

**Answers:**

Data Science is an interdisciplinary field of different scientific methods, techniques, processes, and knowledge used to transform data of different types, such as structured, unstructured, and semi-structured data, into the required format or representation. Data Science concepts include different concepts such as statistics, regression, mathematics, computer science, algorithms, data structures, and information science, also including some subfields such as data mining, machine learning, and databases, etc.,

The Data Science concept has recently evolved to a greater extent in computing technology to perform data analysis on existing data where data growth is exponential over time. Data Science is the study of various data types, such as structured, semi-structured, and unstructured data in any form or format available to get information. Data Science consists of different technologies used to study data, such as data mining, data storing, data purging, data archival, data transformation, etc., to make it efficient and ordered. Data Science also includes concepts like Simulation, modeling, analytics, machine learning, computational mathematics, etc.,

#### Q2. What is the best Programming Language to use in Data Science?

**Answers: **

Data Science can be handled using programming languages like Python or R. These are the two most popular languages used by Data Scientists or Data Analysts. R and Python are open source, free to use, and came into existence in the 1990s. Python and R have different advantages depending on the applications and require a business goal. Python is better to use in repeated tasks or jobs and for data manipulations. In contrast, R programming can be used for querying or retrieving datasets and customized data analysis.

Mostly Python is preferred for all types of data science applications, whereas time R programming is preferred in the cases of high or complex data applications. Python is easier to learn and has less learning curve, whereas R has a deep learning curve. Python is mostly preferred in all cases, a general-purpose programming language, and can be found in many applications other than Data Science. R is mostly seen in the Data Science area, only used separately for data analysis in standalone servers or computing.

### Part 2 – Data Science Interview Questions (Advanced)

Let us now have a look at the advanced Interview Questions:

#### Q3. Why is data cleaning essential in Data Science?

**Answers: **

Data cleaning is more important in Data Science because the data analysis outcomes come from the existing data where useless or unimportant need to be cleaned periodically when not required. This ensures the data reliability & accuracy, and memory is freed up. Data cleaning reduces data redundancy and gives good results in data analysis where some large customer information exists and should be cleaned periodically. Businesses like e-commerce, retail, and government organizations contain large customer transaction information which is outdated and needs to be cleaned.

Depending on the amount or size of data, suitable tools or methods should be used to clean the data from the database or big data environment. Different types of data exist in a data source, such as dirty data, clean data, mixed clean and dirty data, and sample clean data. Modern data science applications rely on the machine learning model, where the learner learns from the existing data. So, the existing data should always be clean and well-maintained to get sophisticated and good outcomes during the system’s optimization.

#### Q4. What is a Linear Regression in Data Science?

**Answers: **

These are the frequently asked Data Science Interview Questions in an interview. Linear Regression is a technique used in supervised machine learning, the algorithmic process in Data Science. This method is used for predictive analysis.

Predictive analytics is an area within Statistical Sciences where the existing information will be extracted and processed to predict trends and outcomes. The subject’s core lies in analyzing the existing context to predict an unknown event.

The Linear Regression method’s process predicts a variable called the target variable by making the best relationship between the dependent variable and an independent variable. Here the dependent variable is the outcome variable and the response variable, whereas the independent variable is the predictor or explanatory variable.

For example, in real life, depending on the expenses incurred in this financial year or monthly expenses, the predictions happen by calculating the approximate upcoming months or financial year expenses. In this method, the implementation can be done using a Python programming technique where the most important method is used in the Machine Learning technique under the area of Data Science. Linear regression, also called Regression analysis, comes under the Statistical Sciences area, integrated with Data Science.

#### Q5. What is A/B testing in Data Science?

**Answers:**

A/B testing is also called Bucket Testing or Split Testing. This is the method of comparing and testing two versions of systems or applications against each other to determine which version of the application performs better. This is important when multiple versions are shown to the customers or end-users to achieve the goals. In Data Science, this A/B testing is used to know which variable out of the existing two variables to optimize or increase the outcome of the goal. A/B testing is also called the Design of an Experiment. This testing helps establish a cause-and-effect relationship between the independent and dependent variables.

This testing is also simply a combination of design experimentation or statistical inference. Significance, Randomization, and Multiple Comparisons are the key elements of A/B testing. The significance is the term for the significance of statistical tests conducted. Randomization is the core component of the experimental design, where the variables will be balanced. Multiple comparisons are a way of comparing more variables in the case of customer interests that causes more false positives resulting in the requirement of correction in the confidence level of a seller in e-commerce.

A/B testing is an important one in the area of Data Science in predicting the outcomes.

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