CS533 F18 Info

Though the course has been taught before, this is my first time teaching it and I don't quite have the details worked out, so the information below is subject to change. 

(Undergraduates: if you want to take this course, please contact me.)

Course Goals and Learning Outcomes

  • The purpose of this course is for students to learn how to engage in the scientific process using data-centric concepts and methods and to think like a data scientist by critically analyzing their own work and the work of others.

Course Learning Outcomes It is my goal that after completing this course successfully, you will be able to:

  • Explore a data set to determine whether and how it might illuminate questions of interest.
  • Define and operationalize a research question such that a data analysis could produce meaningful knowledge.
  • Use best practices to carry out analyses in a documented, reproducible, and efficient fashion.
  • Present the results of a data analysis with appropriate visuals and written argument.
  • Identify weaknesses in a data analysis and assess their impact on the correctness and utility of the results.
  • Assess ethical implications of an analysis in terms of both classical human subject research ethics and contemporary concerns such as fairness and bias.
  • Understand the space of data science techniques and applications, and relate future learning to this framework.

Quizzes/In-class activities 10%
Assignments 50%
Midterm 20%
Final 20%

We are still considering which book to adopt. If you want to get started with Python and Data Science, I can recommend the online Foundations of Data Science Textbook by By Ani Adhikari and John DeNero.

The assignments (which are more like small-scale projects) require quite a bit of python programming. We'll spend a few lectures in the first part of the semester introducing Python. If a seminar comes up during the semester which seems relevant, I will assign it, but I can't tell that at the moment.