Data Science MCQs

Data Science MCQs

The following Data Science MCQs have been compiled by our experts through research, in order to test your knowledge of the subject of Data Science. We encourage you to answer these multiple-choice questions to assess your proficiency.
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1: ____ means that an entity has a minimum cardinality of one.

A.   The entity is required in relationship

B.   The entity is not required in relationship

C.   Both

D.   None

2: A hypothesis is _______.

A.   A proposed solution to observed phenomena

B.   A question derived from observed phenomena

C.   A testable explanation for observed phenomena

D.   Generated as the final step in scientific inquiry

3: A natural key is also called a(n) ____ key.

A.   Surrogate

B.   Intelligent

C.   Secondary

D.   Defining

4: For bi analysis, data need to represent the proper ________, the proper level of detail.

A.   Graininess

B.   Opacity

C.   Coarseness

D.   Summarization

E.   Granularity

5: On network diagrams, the internet is frequently depicted as a ____.

A.   Block.

B.   Lightening bolt.

C.   Line.

D.   Cloud.

6: What is Data Science?

A.   The process of creating data visualizations

B.   The study of computer programming languages

C.   The interdisciplinary field that uses scientific methods, algorithms, and systems to extract knowledge and insights from data

D.   The process of collecting data from various sources

7: What is the primary goal of data cleaning in Data Science?

A.   To delete all data entries with missing values

B.   To reduce the size of the dataset

C.   To ensure data accuracy and remove errors and inconsistencies

D.   To convert categorical data into numerical format

8: Which programming language is widely used for data analysis and machine learning in Data Science?

A.   Java

B.   Python

C.   C++

D.   Ruby

9: What is a data scientist responsible for in a data-driven project?

A.   Building and managing databases

B.   Designing data visualizations

C.   Collecting and analyzing data to make data-driven decisions

D.   Creating user interfaces for data entry

10: What is the process of transforming raw data into a structured format for analysis in Data Science?

A.   Data visualization

B.   Data preprocessing

C.   Data modeling

D.   Data aggregation

11: What is the purpose of exploratory data analysis (EDA) in Data Science?

A.   To predict future trends in the data

B.   To clean and preprocess the data

C.   To understand the distribution and characteristics of the data

D.   To build machine learning models

12: Which technique is used to find patterns or relationships in large datasets without the need for specific hypotheses?

A.   Regression analysis

B.   Hypothesis testing

C.   Data clustering

D.   Time series analysis

13: What is the process of training a machine learning model on historical data and using it to make predictions on new data called?

A.   Data cleaning

B.   Feature engineering

C.   Model training

D.   Model validation

14: Which branch of mathematics is fundamental to many algorithms and techniques used in Data Science?

A.   Trigonometry

B.   Calculus

C.   Linear algebra

D.   Statistics

15: What is the term used for the process of drawing conclusions from data and making informed decisions based on the results of data analysis?

A.   Data visualization

B.   Data extraction

C.   Data inference

D.   Data exploration