# Exploring hurricane datasets in the classroom

In August 2017, Hurricane Harvey evolved from a series of thunderstorms to one of the first major hurricane landfalls in the United States since early 2005. Right on the heels of Harvey, Hurricane Irma blasted through the Caribbean and onto the U.S. mainland, striking Florida in early September.

The National Oceanic and Atmospheric Administration (NOAA), which aims to understand and predict changes in weather, provides educational resources and datasets about hurricanes.

The dataset for 2005-2015 is available in our Common Online Data Analysis Platform (CODAP), a free and open-source web-based data analysis tool, geared toward middle and high school students.

Screenshot of NOAA hurricane data embedded in our Common Online Data Analysis Platform.

With all the current catastrophic news about hurricanes, students have lots of questions. Use the data to help them understand the history and characteristics of storms.

• To investigate the paths that hurricanes generally follow, use the slider to change the year from 2005 to 2015, and watch the data points on the map, which represent the general path of the storms.
• To determine the storm with the highest wind speed, click the top data point in the wind speed graph, which plots year against highest wind speed. Since data is linked across multiple representations, the data point is highlighted on the graph and in the table, so you can find the name and date of that particular storm (e.g., Wilma, October 15, 2005, with top wind speeds of 160 mph).
• To learn which year had the most or least number of storms, look at the storms per year graph. Notice an outlier in the data with year 2005, which had 15 storms during that season. (Note: This was the same year as Hurricane Katrina. Select KATRINA in the table and make sure the slider is set to 2005, then see the path of the hurricane graphed on the map.)
• To see a relationship between wind and pressure, click on the Graph button. Drag the Maximum Wind column header from the table to the vertical (y) axis until the axis turns yellow. Drag the Minimum Pressure to the horizontal (x) axis until the axis turns yellow. (Note: you may need to scroll to the far right of the Case Table to see these columns.)

Analyzing and interpreting data is one of the key science and engineering practices of the Next Generation Science Standards (NGSS), and representing and interpreting data are featured throughout the Common Core State Standards (CCSS) for mathematics. Students can use publicly available datasets from storms and other weather events to learn more about the world around them.

# New CODAP Website

## Check out our newly revamped CODAP website!

Our newly designed and upgraded CODAP website has a new and fresher look. Visit us at codap.concord.org

We hope you like our new CODAP website, which is designed to make it easier to find information about CODAP and data science education. In particular, we hope you like the design which, is more modern and is intended to be easy to navigate.

We wanted a new website to better collaborate with our CODAP community, including educators, software developers, partners, researchers, and curriculum developers. You are now able to post comments, CODAP questions, and share use cases on our CODAP forums. We check the forum every day to keep in touch with you about all the things happening related to what we’re most passionate about—helping our users work with each other in the emerging field of data science education!

In addition to our new website, we invite you to join us for our upcoming webinar series starting on May 30.

Our first speaker is Cliff Konold of SRRI Education. Cliff will lead a discussion entitled “Modeling as a core component of structuring data” based on an upcoming article (Konold, Finzer, & Kreetong, in press), which describes research on student understanding of and ability to organize complex data for analysis. We expect lively discussion with Cliff’s facilitation.

Please RSVP for our May 30 webinar using our Eventbrite page here.

We are continuing to update our help pages and example documents with useful information about our work and also hope to include useful information on data science education related issues, so please do check back with us for updates.

Please contact us at codap@concord.org to let us know what you think of our new website—all comments and feedback are welcome. Please also let us know if you cannot find something or would like to make any suggestions for new information or topics.

Many thanks for your ongoing support and we look forward to hearing from you!

# Data Science Education Meetup at Cyberlearning 2017

Thank you to the fantastic crowd at Cyberlearning 2017 who attended our Data Science Education Meetup at Mussel Bar and Grill on Tuesday night. The Meetup provided an opportunity for members of the Cyberlearning community to discuss innovative ways that we could implement data science education into research and curriculum development, to address NSF’s 10 Big Ideas for Future Investments strand in data science [PDF], and to enjoy some great food!

Attendees at the Data Science Education Meetup at Cyberlearning 2017

There were several strands of activity toward data science education at Cyberlearning 2017, and the Meetup provided an opportunity for us to share and reflect on the following:

• A roundtable session on Teaching Data Science, in which our own William Finzer facilitated a discussion on our Data Science Education Technology conference in Berkeley in February.
• Hearing about some of the challenges researchers face in implementing curriculum at the middle school and high school level, as well as some of the Cyberlearning projects (such as Data Science Games, CODAP, Impact Studio, Learning and Youth, and STEM Literacy Through Infographics), which are hoping to address these challenges.
• Participating in the Data Science Education for 21st Century Learning CL17 Working Sessions Strand. It was especially great to work with Sayamindu Dasgupta and Jesse Bemley to identify the important aspects necessary to move data science education forward.
• Identifying data science as part of computational thinking, as put forward by NSF Program Officer Arlene M. de Strulle on Wednesday morning.

Our Meetup was packed (we had to move to a larger set of tables, as a matter of fact!), and the evening was full of lively discussion about the impact and need for data science education. We thank everyone who attended and are especially grateful to our community for bringing in so much passion and energy for data science education.

Please check out our future Data Science Education Meetup dates and locations at concord.org/meetup. Or join us online through a series of monthly Data Science Education Webinars, starting in May. We have a great lineup of speakers, including Cliff Konold, Amelia McNamara, and Rob Gould. We’ll post dates and additional information at concord.org/meetup.

As always, please leave a comment or suggestion for future Meetups, community-building activities, or future innovations in data science education you’d like to see. We love hearing from you.