Fall 2022 DSC Courses
The Data Science Academy (DSA) has expanded its course list!
Dear colleagues,
Short version
Encourage your students to sign up for Fall 2022 one-credit Data Science Academy courses!
Long version
The NC State Data Science Academy is delighted to offer over 20 one-credit undergraduate courses this fall under the DSC (Data Science) prefix. Please feel free to copy and paste the blurb and button below and point to our Course Descriptions page for more details about each course.
We would appreciate your help getting the word out and encouraging your students to enroll. Many of the courses have no prerequisites while some only require some basic familiarity with programming. We hope to again attract students from all 10 colleges. Undergraduate students, graduate students, postdocs, staff and faculty are all welcome to enroll.
We also hope you’ll let us know which data science courses in your departments we can encourage the students in these introductory courses to take next. Our goal is to give them a taste of data science and send them to you for more!
Best regards,
Dr. Rachel Levy
Executive Director, Data Science Academy and Professor of Mathematics
Curious about data science
The NC State Data Science Academy (DSA) welcomes students to explore over 20 sections of our one-credit courses this fall that have the DSC (Data Science) prefix. Browse course descriptions on our website. Many of the sections have no prerequisites while others only require some basic statistics or programming concepts. We’d love to see students from every college – encourage your friends to sign up for a DSC class! You can take as many of the courses as you want.
No prerequisites: R/Python (6 sections), R for Data Science Analysis and Visualization, R for Social Sciences, Scientific Programming with Python (2 sections), Social Media: Data, Ethics, and Theory, Visualization: Tools and Techniques, Biomedical Data Sharing, Clustering Data Through Machine Learning, Data Communication, Data Science for Cybersecurity, Data Science for Social Good, Data Science for Sustainability, Design Thinking, Computational Thinking and Problem-Solving Through Data Science
Basic statistics (mean, median, mode, variance): Epidemiology: Data and Disparities
Basic programming (familiarity with Python): Data Wrangling and Web Scraping, EDA for Big Data, Machine Learning for Practitioners, Reproducibility, Containers and the Cloud.
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