가장 많이 묻는 면접 질문과 답변 & 온라인 테스트
면접 준비, 온라인 테스트, 튜토리얼, 라이브 연습을 위한 학습 플랫폼

집중 학습 경로, 모의고사, 면접 준비 콘텐츠로 실력을 키우세요.

WithoutBook은 주제별 면접 질문, 온라인 연습 테스트, 튜토리얼, 비교 가이드를 하나의 반응형 학습 공간으로 제공합니다.

Prepare Interview
/ 면접 주제 / Data Science
WithoutBook LIVE Mock Interviews Data Science Related interview subjects: 13

Interview Questions and Answers

Know the top Data Science interview questions and answers for freshers and experienced candidates to prepare for job interviews.

Total 23 questions Interview Questions and Answers

The Best LIVE Mock Interview - You should go through before interview

Know the top Data Science interview questions and answers for freshers and experienced candidates to prepare for job interviews.

Interview Questions and Answers

Search a question to view the answer.

Freshers / Beginner level questions & answers

Ques 1

What is Data Science?

Data science is a multidisciplinary field that uses scientific methods, processes, algorithms, and systems to extract insights and knowledge from structured and unstructured data. It combines expertise from various domains such as statistics, mathematics, computer science, and domain-specific knowledge to analyze and interpret complex data sets.

복습용 저장

복습용 저장

이 항목을 북마크하거나, 어렵게 표시하거나, 복습 세트에 넣을 수 있습니다.

내 학습 라이브러리 열기
도움이 되었나요?
Add Comment View Comments
Ques 2

What is the primary goal of Data Science?

The primary goal of data science is to uncover hidden patterns, correlations, and trends in data that can be used to make informed decisions and predictions. Data scientists use a variety of tools and techniques, including statistical analysis, machine learning, data visualization, and big data technologies, to extract meaningful information from large and diverse data sets.

복습용 저장

복습용 저장

이 항목을 북마크하거나, 어렵게 표시하거나, 복습 세트에 넣을 수 있습니다.

내 학습 라이브러리 열기
도움이 되었나요?
Add Comment View Comments
Ques 3

Please provide some examples of Data Science.

Data science examples in business include processes such as aggregating a customer's email address, credit card information, social media handles, and purchase identifications in order to identify trends in their behavior.

복습용 저장

복습용 저장

이 항목을 북마크하거나, 어렵게 표시하거나, 복습 세트에 넣을 수 있습니다.

내 학습 라이브러리 열기
도움이 되었나요?
Add Comment View Comments
Ques 4

Explain the term 'feature engineering' in the context of machine learning.

Feature engineering involves selecting, transforming, or creating new features from the raw data to improve the performance of machine learning models. It aims to highlight relevant information and reduce noise.

Example:

Creating a new feature 'days_since_last_purchase' for a customer churn prediction model.
복습용 저장

복습용 저장

이 항목을 북마크하거나, 어렵게 표시하거나, 복습 세트에 넣을 수 있습니다.

내 학습 라이브러리 열기
도움이 되었나요?
Add Comment View Comments
Ques 5

Explain the term 'one-hot encoding' and its application in machine learning.

One-hot encoding is a technique used to represent categorical variables as binary vectors. Each category is represented by a unique binary digit, and this encoding is valuable when working with algorithms that require numerical input.

Example:

Converting categorical variables like 'color' into binary vectors (e.g., red: [1, 0, 0], blue: [0, 1, 0], green: [0, 0, 1]).
복습용 저장

복습용 저장

이 항목을 북마크하거나, 어렵게 표시하거나, 복습 세트에 넣을 수 있습니다.

내 학습 라이브러리 열기
도움이 되었나요?
Add Comment View Comments

Most helpful rated by users:

Copyright © 2026, WithoutBook.