Isaac Merritt

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2022-43

May 9, 2022

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2022/EECS-2022-43.pdf

As Data Science increases in popularity, demand is increasing for accessible educational Data Science content. Though it is common to address this demand with educational content in text-based programming languages like Python, we have identified a different approach that has proven successful in other fields of computing in the past. Course content known as BJC (The Beauty and Joy of Computing) offers students the experience of learning how to program in the block-based language Snap!. BJC content is taught all over the country at the high school level, so our goal was to take a similar approach with Data Science content.

By adapting Data Science educational content into Snap! in the form of DASIS (Data Science in Snap!) and BJDS (The Beauty and Joy of Data Science), we have created new opportunities for high-school-age Data Science students. DASIS is a new programming library in Snap! that provides basic and advanced Data Science tools to Snap! users, and BJDS is a series of online modules that covers introductory Data Science Principles using the DASIS library. We have made Data Science more accessible not only by adapting it to be taught in a high school setting, but also by making it publicly available on the Internet without the need for any additional software. This means that any high school teacher or student can access introductory Data Science material with only an Internet connection.

Advisors: Joshua Hug

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BibTeX citation:

@mastersthesis{Merritt:EECS-2022-43,
    Author= {Merritt, Isaac},
    Title= {Data Science in Snap!: A Block-Based Approach to Data Science Education},
    School= {EECS Department, University of California, Berkeley},
    Year= {2022},
    Month= {May},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2022/EECS-2022-43.html},
    Number= {UCB/EECS-2022-43},
    Abstract= {As Data Science increases in popularity, demand is increasing for accessible educational Data Science content. Though it is common to address this demand with educational content in text-based programming languages like Python, we have identified a different approach that has proven successful in other fields of computing in the past. Course content known as BJC (The Beauty and Joy of Computing) offers students the experience of learning how to program in the block-based language Snap!. BJC content is taught all over the country at the high school level, so our goal was to take a similar approach with Data Science content.

By adapting Data Science educational content into Snap! in the form of DASIS (Data Science in Snap!) and BJDS (The Beauty and Joy of Data Science), we have created new opportunities for high-school-age Data Science students. DASIS is a new programming library in Snap! that provides basic and advanced Data Science tools to Snap! users, and BJDS is a series of online modules that covers introductory Data Science Principles using the DASIS library. We have made Data Science more accessible not only by adapting it to be taught in a high school setting, but also by making it publicly available on the Internet without the need for any additional software. This means that any high school teacher or student can access introductory Data Science material with only an Internet connection.},
}

EndNote citation:

%0 Thesis
%A Merritt, Isaac 
%T Data Science in Snap!: A Block-Based Approach to Data Science Education
%I EECS Department, University of California, Berkeley
%D 2022
%8 May 9
%@ UCB/EECS-2022-43
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2022/EECS-2022-43.html
%F Merritt:EECS-2022-43