A web-based learning platform is useful as it allows students with limited or no programming background to conduct in-depth hands-on practice in data science. Background: The need for data science coursework for non-computing majors has grown in recent years, given the demand in various disciplines. However, a substantial number of current data science courses are inappropriate for non-computing majors as they typically require a long chain of prerequisite courses in computer science and mathematics. Moreover, courses designed for computing majors do not match the preparation and interests of students majoring in other disciplines. Outcomes: This paper presents a platform for Learning Data Science (DSLP), a web-based platform, which assists in the teaching and learning of data science topics by students with limited or no coding experience, including those that have completed a high school AP Computer Science Principles (CSP) class or an equivalent CSP course increasingly offered in many colleges. Application Design: The platform helps students understand fundamental data science concepts and techniques, as well as provides them with an in-depth hands-on experience that goes beyond their coding capabilities. The platform offers various data visualization supports to help students understand data and analysis results. Students can use the platform to work on in-house datasets or their own data. This allows students to focus more on how to solve data science problems in various domains than how to write code. The platform also has several unique features that make it particularly helpful for teaching and learning data science topics such as code exemplification and sandbox, informative instructions, and progress monitoring. Findings: The platform has been used multiple times in data science courses for non-computing majors offered at the authors' institution. Preliminary student feedback indicated that the platform is effective in terms of improving student understanding and interest in the topics.

The presenters will demo a web-based Data Science Learning Platform (DSLP) that makes data science education accessible to students with limited or no programming background. The DSLP platform offers students with several benefits such as: (1) learn a web-based user interface to perform data science tasks without requiring coding, (2) explore popular Python data science libraries (e.g., Pandas, Matplotlib, Numpy, or Scikit-Learn) through real-time code exemplification to prepare them for advanced data science topics, (3) become familiar with the on-site user guide and helpful tips to make the platform easy to use, (4) write their own code within a sandbox, and (5) monitor their own progress by tracking their platform usage. The demo will walk through the steps of using the DSLP to perform various data science tasks and the participants will be able to try out the features mentioned above. The demo will also cover the design of course materials, including hands-on practices and lab assignments using the DSLP platform. The typical participants include instructors who are interested in teaching introductory-level data science to high school students or non-computing college majors with little or no programming background. Participants need to have a laptop with access to the Internet to attend the hands-on exercises workshop. The laptop should have a current web browser (e.g., Safari or Chrome) installed to access the web-based learning platform. This demo describes work supported by the National Science Foundation under Award 2021287.

Data science knowledge and skills have become indispensable to STEM and non-STEM disciplines alike. As a result, it has become crucial for students in non-computing majors to learn data science techniques, particularly in the context of their own disciplines. A majority of current university data science coursework, however, requires sufficient depth in programming and statistical skills related to managing, manipulating, and analyzing data, which reduces their usefulness for entry-level non-computing majors. This workshop presents a set of hands-on exercises to introduce data science to entry-level non-computing majors. The exercises cover the data science lifecycle, including data acquisition, preparation, model development and deployment, visualization, and storytelling. A freely-available web-based Data Science Learning Platform (DSLP) will be presented to show how to perform hands-on data science exercises with little or no coding background. The presenters will also share their experiences in using the DSLP tool in an entry-level data science course to non-computing majors at RIT. Both the tool and course materials will be shared with workshop participants. The typical workshop participant is a high school teacher or a college instructor interested in teaching data science at the introductory level. No prior programming or data science experience is needed, thus making the workshop materials usable by a wide audience. Participants need to have a laptop with access to the Internet to attend the hands-on exercises workshop. The laptop should have a current web browser (e.g., Safari or Chrome) installed to access the web-based learning platform. This work was supported by the National Science Foundation under Award 2021287.

Utilizing emojis to understand sentiments of online social media posts has received much attention recently. In this work, we perform an emoji-based sentiment analysis and classifier to gain insights into users' emotional reactions to marijuana-related posts on Facebook. We collected about 15,000 posts and 14 million users' reactions from the High Times Magazine Facebook page, a top ranking marijuana-related content provider with a longstanding monthly publication for advocation of cannabis legalization. We developed R scripts and utilized the Google Cloud Prediction API to estimate the sentiment of the posts from posted texts and emoji-based reactions. Our analysis revealed that “LIKE” and “LOVE” are the most frequently used reactions, and they are strongly correlated by a correlation coefficient of 0.82. Our analysis also showed that the correlation between number of comment and reactions are similar across all emoji types, except “SAD”. Interestingly, we found that words with negative sentiment occurred much more often than words with positive sentiment. Particularly, “weed” was used more than 800 times while that of “love” was used 310 times. Our study can be utilized as an alternative tool for revealing insights into users' opinions towards marijuana that may potentially benefit public health surveillance applications.