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Data Science Foundations

Data Science

An introduction to the core concepts, tools, and practices of data science.

data statistics programming

8 Questions Medium Ages 13+ Sep 12, 2026

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About this Study Set

This study set covers Data Science through 8 practice questions. An introduction to the core concepts, tools, and practices of data science. Every question includes the correct answer so you can learn as you go — pick any format above to get started.

Questions & Answers

Browse all 8 questions from the Data Science Foundations study set below. Each question shows the correct answer — select a study format above to practice interactively.

1 Which programming language is most widely used in data science for its extensive library support and simple syntax?

A Java
B Python
C HTML
D C++

2 What is the primary purpose of a 'bar chart' in data visualization?

A To show the relationship between two continuous variables
B To represent parts of a whole as percentages
C To compare quantities across different categories
D To display trends over a specific time period

3 In a dataset, what is the term for the numerical value that appears most frequently?

A Mean
B Median
C Mode
D Range

4 Which library is the standard tool for data manipulation and analysis in Python?

A Pandas
B Pygame
C Tkinter
D Requests

5 What is the definition of a 'supervised' machine learning model?

A The model learns from unlabeled data without instructions
B The model is trained on a dataset containing input-output pairs
C The model requires no data to function
D The model is used only for image compression

6 What is the correct definition of the 'median' in a set of numbers?

A The average of all values
B The difference between the highest and lowest value
C The middle value when the numbers are ordered
D The total sum of all values

7 Which file format is most commonly used for storing structured data tables to be processed by data science applications?

A CSV
B EXE
C ISO
D DLL

8 In machine learning, what do we call the process of cleaning and preparing raw data before analysis?

A Data mining
B Data wrangling
C Data visualization
D Data caching

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