Most Asked Data Science Interview Questions -

Most Asked Data Science Interview Questions with Answers

Are you looking for Data Science Interview Questions? Are you preparing for Data Science interview? This is the right place you have come. Here, we will guide and help you to enhance your Data Science skills, and be prepare for job.

Here, we are providing a good collection of real-world Interview questions which are generally asked in big companies such as Google, Microsoft, Facebook, Oracle, and Amazon etc. Each question has a perfectly written answer. So, let’s start.

Que 1) What is Data Science?

Data Science is a specific field of computer science where we study of information and knowledge extracted from data. The huge data set where we apply processes and algorithms to extract information and insights may be a noisy, structured or unstructured data. In Data Science, we use various techniques from many technical and mathematical fields, including signal processing, mathematics, probability, machine learning, computer programming, statistics, data engineering, pattern matching, and data visualization, with the goal of extracting useful knowledge or meaningful insights from the data. Big data is an important aspect of data science as here the computer systems have to handle and deal with huge data.

The main reason behind the popularity of Data Science is that it extracts the kind of insights from the available data that has led to some major innovations in several products and companies. We can easily determine the taste of a particular customer by using these insights. This can be a big factor of a product succeeding in a particular market, etc. Data science is closely related to data mining, machine learning and big data.

Que 2) What do you understand by data scientist?

A person who study and implement the data science techniques is called a data scientist. Data scientists have to solve complicated data problems using mathematics, statistics, programming, and computer science. Although a data scientist is most likely to be an expert in only one or two of the above disciplines. A data scientist is responsible for creating programming code and combines it with statistical knowledge to extract insights from data.

Que 3) Why is Data Science called an interdisciplinary field?

Data Science is called an interdisciplinary field because it uses many technical and mathematical fields, such as signal processing, mathematics, statistics, probability, machine learning, computer programming, data engineering, pattern matching, and data visualization to achieve useful information and meaningful insights from the data.

An interdisciplinary field is a field that uses study or involvement of the combination of two or more academic disciplines into one activity.

For example: A research project: It includes knowledge from several other fields like sociology, psychology, economics, anthropology, etc. It is like creating something beyond boundaries.

What is the difference between Data Science and Data Analytics?

Following is the list of main difference between Data Science and Data Analytics:

Data Science	Data Analytics
Data Science is a broad technology that consists of many subsets such as Data Analytics, Data Engineering, Data Mining, Data Visualization, etc.	Data Analytics is a small subset of Data Science.
Data Science is mainly dealt with discovering meaningful insights from huge datasets and extracting the best possible solutions for respective businesses. It also makes the business easy by providing important insights.	Data Analytics is mainly depicts the precise details of retrieved insights.
In Data Science, we must require a good knowledge in advanced programming languages as well as statistics and data mining.	In Data Analytics, we just require knowledge of basic programming languages and database.
Data Science does not only focus on finding a good solutions but it also predicts the future by looking at the past patterns or insights.	The main focus of Data Analytics is to find and provide a good solution from the insights.
The Data Scientist’s job is to provide important insightful data visualizations from the raw data that are easy to understand.	The Data Analyst’s job is to analyze the data in order to make decisions.
Data Scientists design advanced data modeling processes using prototypes, ML algorithms, predictive models, and custom analysis.	Data Analysts transform the insights received from Data Scientists into easy business-savvy language so that both technical and non-technical persons of the organization can understand.
Data Science finds out new and unique questions that can lead to a great business innovation.	Data Analytics finds the solutions to these questions and determine how they can be used within an organization.

Que 4) What are the most important responsibilities of Data Scientists?

The most important responsibilities of Data Scientists are as follows:

They perform Exploratory Data Analysis on large datasets to extract insights or meaningful information.
They perform data mining by creating ETL pipelines.
They perform statistical analysis using ML algorithms like logistic regression, KNN, Random Forest, Decision Trees, etc.
They process, clean, and validate the integrity of data.
They build resourceful ML libraries and write code for automation.
They extract business insights using ML tools and algorithms.
They have to focus on new trends and identify them in data for making business predictions.

Data Science Interview Questions