Everfi Data Science Foundation
Quiz
•
Instructional Technology
•
9th - 12th Grade
•
Practice Problem
•
Hard
brandi joice
Used 15+ times
FREE Resource
About this resource
This quiz comprehensively covers foundational concepts in data science, making it appropriate for grades 9-12 students in an introductory technology or statistics course. The questions systematically progress through the data science workflow, beginning with basic definitions and terminology, then advancing to data collection methods, data types (qualitative vs. quantitative, primary vs. secondary), data cleaning processes, and various analytical approaches. Students need to understand the distinction between descriptive analytics (observations based on existing data) and predictive analytics (forecasting future outcomes), along with mastering data visualization techniques including when to use specific chart types like histograms, scatter plots, line graphs, and pie charts. The quiz also requires comprehension of business applications, including how data scientists support decision-making through trend identification, probability assessment, and the creation of dashboards versus reports for different stakeholder needs. Created by Brandi Joice, an Instructional Technology teacher in the US who teaches grades 9-12. This assessment serves as an excellent tool for measuring student comprehension of data science fundamentals and can be effectively used as a pre-assessment to gauge prior knowledge, a formative assessment during instruction, or a summative evaluation following a data science unit. The repeated self-assessment questions embedded throughout make it particularly valuable for tracking student confidence and understanding as they progress through the material. Teachers can use this quiz for homework assignments, review sessions before exams, or as a warm-up activity to reinforce key concepts. The content aligns with Common Core Mathematical Practices MP4 (modeling with mathematics) and MP5 (using appropriate tools strategically), while also supporting CSTA Computer Science Standards 3A-DA-10 and 3A-DA-11, which focus on data collection, organization, and analysis for identifying patterns and making predictions.
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64 questions
Show all answers
1.
MULTIPLE CHOICE QUESTION
30 sec • Ungraded
Rate your understanding of Data Engineering.
1* I am super lost
2** I get some things but not others
3*** I feel pretty good; I just need more practice
4**** I feel super Confident
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
The practice of collecting data and finding insights from data is called ________________.
data collection
data visualization
data science
data analysis
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Data science refers to _______________.
the practice of collecting data and finding insights from data
presenting data in a visually appealing way
designing, setting up, and maintaining large collections of data called databases
gathering and measuring data, which can include surveys and other collection tools
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is data science?
A term synonymous with data collection
A term referring to the practice of collecting and finding insights from data
Both answers
Neither Answers
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Data collection, database management, and data visualization are the _______________.
steps a research analyst takes before starting their research
steps a data scientist takes before determining usable results
practices a data analyst must use to be credible
None of these answers
6.
MULTIPLE SELECT QUESTION
45 sec • 1 pt
Which of the following steps does a data scientist take before determining usable results?
data visualization
data collection
database management
Data deletion
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
A data scientist takes the following steps before determining usable results:
data collection, machine learning, and data visualization
data collection, machine learning, and database management
data collection, database management, and data visualization
data collection, machine learning, and data analysis
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