Syllabus

Please read the syllabus. It has important information about the course, including structure, assignments, grading, and office hours, among many others. Even Snoop Dog wants you to read the syllabus!

Welcome to STA 235H!

Our class meets on one of these four schedules (depending on your section) in UTC 1.130:

  • Section 1: Tue 10:00 AM – 12:00 PM
  • Section 2: Tue 12:00 PM – 2:00 PM
  • Section 3: Thu 10:00 AM – 12:00 PM
  • Section 4: Thu 12:00 PM – 2:00 PM

The objective of this course is for you to gain the tools you need to tackle real-world problems from a quantitative perspective. We will be covering topics on regression modelling, causal inference, and predictive modelling. You will have the opportunity to be exposed to an array of different real-world examples, get hands-on experience in working with data, and improve your R coding skills for data science.

Your success in this class is important to me. These are difficult times, and I am committed to making appropriate accommodations that meet your needs and that also allows you to complete the requirements of this course. My goal is for you to learn everything you were hoping to learn in this class.

If you feel you are falling behind or that there are any aspects of this class that are preventing you from learning, reach out to me or a teaching assistant as soon as possible, so we can work something out. We also encourage you to reach out to the student resources available through UT, many of which are listed on this syllabus.


RESOURCES FOR COVID-19

Here you can find additional resources and guidelines provided by the University to protect our community during the COVID-19 Pandemic:

Between Aug. 23 and Sept. 24, there will be a pop-up vaccine clinic in the Texas Union Eastwoods Room (UNB 2.102) every Mon (except Labor Day), Wed, and Fri from noon-4:00 p.m., no appointment needed.

For students that are presenting symptoms or have been in close contact with a COVID-19 confirmed case, the instruction team will make all necessary accommodations during their absence until they recover/learn more about their situation.






© Magdalena Bennett - licensed under Creative Commons.