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WithoutBook LIVE 模拟面试 Data Science 相关面试主题: 13

面试题与答案

了解热门 Data Science 面试题与答案，帮助应届生和有经验的候选人为求职面试做好准备。

共 23 道题 面试题与答案

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了解热门 Data Science 面试题与答案，帮助应届生和有经验的候选人为求职面试做好准备。

面试题与答案

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应届生 / 初级级别面试题与答案

问题 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.

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问题 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.

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问题 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.

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问题 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.
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问题 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]).
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