Girls Who Code - U of M (@GirlsWhoCodeUM) header
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Student Organization at @umich promoting diversity in Programming and STEM. Anti-Racism Resources: https://teapawt.net/t.co/JGHo4cW10m

Joined June 2020
Apply for our 2024 Girls Who Code Summer Experience! We will meet daily for a week to teach Python programming skills to high school learners. Check out the info in the attached flyer, and apply here! forms.gle/ZRgsz5Hn6vhFGxSGA
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Apply for our 2022-2023 academic year Girls Who Code Club! We meet weekly via Zoom to teach Python programming skills to high school learners. Check out the info in the attached flyer, and apply here! forms.gle/Vj4zYRBG6HDuSRk89
In our last #ThursdayTipsandTricks we showed how you can create lists with one line of code using list comprehensions in #Python. Well you can also create dictionaries and sets using a similar syntax! We think comprehensions are pretty handy:
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Did you know our student organization began in 2016? The 2017-2018 Girls Who Code Club was our first official programming. And turns out we joined Twitter a year ago today! #MyTwitterAnniversary
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Congratulations to our founding Executive Committee members that are graduating with their PhDs in 2021: Zena Lapp, Negar Farzaneh, Vy Nguyen, Brooke Wolford (@bnwolford), and Marlena Duda (@MLmarlena). #GirlsWhoCode
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Our final group examined different groups' awareness about knowledge of food from different cultures, and how it varied with factors like household income and education level.
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We also had a group look at the prevalence of different visual impairments in the US, and how it varied with different demographics. Interestingly, they saw an increase in eye diseases in North Dakota compared to other states! Along with many results about age, sex, and race.
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This group looked at trends in music over the last few decades, including how factors like danceability, speechiness, energy, and liveness of music affect its popularity. They saw a sharp change in many of these factors around 2010, when music consumption became mainly digitized!
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Another group found TWO datasets about vehicle crashes, one for general crashes, and one specific to Tesla crashes. They examined the locations, weather, driver demographics, and speed of various crashes to look at what had the greatest effect on survival.
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Our third group found a dataset all about Tuberculosis. They chronicled the process of cleaning their dataset to remove many duplicate entries, so they could eventually examine the prevalence of multidrug resistance of TB in different populations.
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