STA 199

Course description: Learn to explore, visualize, and analyze data to understand natural phenomena, investigate patterns, model outcomes, and make predictions, and do so in a reproducible and shareable manner. Gain experience in data wrangling and munging, exploratory data analysis, predictive modeling, and data visualization, and effective communication of results. Work on problems and case studies inspired by and based on real-world questions and data. The course will focus on the R statistical computing language.

Course Information

• Instructor: Elizabeth Bersson
• TA: Shuo Wang
• Lecture: M/Tu/Thu 12:30 - 2:35 PM at Perkins LINK 087 (Classroom 3)
• Lab: M/Thu 2:45 - 4:00 PM at Perkins LINK 087 (Classroom 3)
• Office Hours:
  • M/Thu 5:00-6:00 PM at Old Chem 223B (EB)
  • Sunday 4:00-6:00 PM at Old Chem 203B (SW)

Links

• Canvas
• Github classroom
• Rstudio container
• Ed discussion board
• Gradescope

Textbooks

All textbooks for this course are freely available online.

• R for Data Science, 2e, Wickham, Çetinkaya-Rundel, Grolemund. O’Reilly, 2nd edition, 2023.

• Introduction to Modern Statistics, Çetinkaya-Rundel, Hardin. OpenIntro Inc., 2nd Edition, 2023.