Teaching and Learning Data Science in Non-Data-Science Courses

In the Classroom
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Please reflect on and share something about your experience teaching and learning data science in college-level courses with a focus in another discipline, such as mathematics, health science, or psychology. Some starting points for reflection may include:

  • How is data used in your area? How do you think data science will transform how this discipline is learned and practice?
  • What successes did you (or your students) experience around incorporating data science concepts and techniques into the subject?
  • What was your favorite activity, lesson, or project that used data science?
  • What did you (or your students) struggle to learn or master? Why do you think that happened?
  • Did you feel like you “belonged” in the course? Was this feeling affected by your experiences with data science in the course? Why or why not?

Instructors, please do not publicly call out your students or other instructors by name; use “one of my students” or “another instructor” and people who know or need to know will figure it out. This forum is indexed publicly by search engines, and damage to someone’s reputation may extend far beyond this community. Similarly, students, please do not publicly call out your instructors or fellow students by name. You have no expectation of anonymity; please reserve sharp criticism for student evaluations.

Welcome to Community of Data Science Learners! :wave:
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  • How is data used in your area? How do you think data science will transform how this discipline is learned and practice? - I have utilized data from the National Health and Nutrition Examination Survey (NHANES) provided by the CDC. This dataset is extensive, featuring a wide range of observations and variables relevant to public health, which I incorporate into my teaching in statistics courses which is not primarily focus on the data science per se and not required for this course. NHANES data is not only voluminous but also richly detailed, covering various aspects such as nutrition, health behaviors, and physiological measurements. This makes it an invaluable resource for training students to handle real-world data. By engaging with this data, students can develop critical skills in data analysis, interpretation, and application. They learn to draw meaningful insights from complex datasets, a skill that is increasingly vital in public health research and policy-making.

  • What successes did you (or your students) experience around incorporating data science concepts and techniques into the subject? - Regardless of their initial proficiency in using the data, all students gain experience with large datasets relevant to their field, rather than just small data sets for statistical exercises. Although some students initially feel overwhelmed, I consider their exposure to real-world data science a significant success.

  • What was your favorite activity, lesson, or project that used data science? - My favorite activity is the final research project where students apply their skills to visualize and interpret data.

  • What did you (or your students) struggle to learn or master? Why do you think that happened? - Majority of our public health students initially lacked skills in data science, including coding and data wrangling, making it challenging for them to engage effectively with large datasets and perform necessary coding tasks.

  • Did you feel like you “belonged” in the course? Was this feeling affected by your experiences with data science in the course? Yes, i feel belonged in this course and most importantly I feel obligated to make sure our students learn how to handle large datasets with the necessary skills to extract valuable information. We are in the age of data, and it is crucial for our students to stay current with these skills to remain competitive in their future careers.

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