Craig Persiko's MATH 108 class

Math 108 Foundations of Data Science combines an introduction to inferential statistics with the fundamental skills and concepts of computer programming, using Python and Jupyter notebooks for hands-on experience in analyzing datasets. Additionally, the course investigates ethical issues surrounding Data Science such as algorithmic bias.
Math 108 is modeled after UC Berkeley's popular Data 8 course, and it articulates with it. So this course transfers to UC Berkeley in place of Data 8.
The only prerequisite knowledge I will assume is algebra: you need to be comfortable with order of operations, variables and functions from algebra. For example, you should know how to evaluate an expression like:
f(x) = 5 + 3 * x
when x=2, you should be able to calculate that f(2) = 11.
This is a 5-unit class, so I expect the average student to spend approximately 15 hours per week on the course material for this class, including class time, reading, homework, studying, etc.
The digital textbook Inferential Thinking will be offered to you at no additional cost thanks to Professors Ani Adhikari and John DeNero at UC Berkeley.

This class will meet face-to-face on campus: Mondays and Wednesdays 1:10 PM - 3:25 PM in MUB 255 (Ocean Campus). Our classroom will have computers available for each student to use during class.
This is the only fully face-to-face section of MATH 108 this semester. (The other sections allow you to be remote if you want to.) So please register for it only if you want to be on campus, and plan to be there for all (or most) class meetings. Class participation will be part of your grade.
Proof of vaccination against Covid-19 is required, via this link, before you can register for this or any class that meets face-to-face in the Spring.
Course Procedures and Information are detailed here. Inside that document, you'll find the Semester Schedule, including test dates and holidays, etc.