Course overview

UNIVERSITY CATALOG COURSE DESCRIPTION

Data science for business applications at the intermediate level. Topics include advanced multiple regression, causal inference, and construction and validation of predictive models. Additional topics will be included if time allows it, such as models for time series, and models for text data analysis.

QUANTITATIVE REASONING

This course carries the Quantitative Reasoning flag. Quantitative Reasoning courses are designed to equip you with skills that are necessary for understanding the types of quantitative arguments you will regularly encounter in your adult and professional life. You should therefore expect a substantial portion of your grade to come from your use of quantitative skills to analyze real-world problems.

PRE-REQUISITES FOR THE COURSE

Statistics 301 or Statistics 301H, Mathematics 408Q or credit registration for 408D, 408L, or 408S.

COURSE FORMAT

This class meets in person once a week for two hours (see schedule according to your section in the first page). Attendance is not mandatory, but highly encouraged. You will also be required to bring a laptop to class, because we will be doing in-class coding exercises. If you do not have a laptop that you can bring to class, please contact the instructor so we can find an alternative solution.

Even though attendance is important, given the current worldwide context, I would encourage you to stay home if you are not feeling well. The instruction team will work with students that cannot attend due to health reasons to provide another form of virtual attendance (e.g. streaming or class videos). Please also check out the COVID-19 resources in this syllabus.

COMPUTER HARDWARE & SOFTWARE REQUIREMENTS

  • Laptop computer
  • Modern and updated operating system (MacOS or Windows)
  • Chrome (highly recommended), Safari, or Firefox web browser
  • Install the latest versions of R and RStudio software. These programs are free, require no registration, and will run on Macs, PCs, or Linux machines. Note that you need both programs; even though we’ll interact with RStudio rather than R directly, you still need a copy of R installed on your computer for RStudio to work.

For the optimal in-class experience, I suggest that you:

  • Close all unnecessary browser windows and tabs and programs.
  • Check that your computer is free of viruses, malware, and spyware (UT recommendations).

HOW TO SUCCEED IN THIS COURSE:

  1. Attend class each week (unless you are not feeling well) and be an active learner. If something is unclear, don’t be afraid to ask a question. Active participation in class enhances the learning experience for everyone!

  2. Slides will be available before each class, so you can use them to follow along. These slides, however, are not self-contained, so I recommend taking notes of the main concepts to complement your understanding.

  3. Asking questions in class is always a good thing! It helps clear the material not only for you, but probably for other students as well. However, if you require additional help, I encourage you to attend students’ office hours. These are designed to help you solve any questions outside the classroom, so take advantage of this instance. Please watch this video on understanding why office hours are important and how they usually work.

  4. Complete all readings and assignments by the suggested date on the course outline posted on the course website. Readings, homework, and other assignments will help you prepare for the midterm and final project, and identify potential questions you might have about the material (see num. 3). If you feel you are falling behind for any reason, please contact me or a TA as soon as possible. We can certainly work with you in finding a suitable solution if we have enough time to do so.

  5. Get an early start on the homework assignments, and follow the submission guidelines. Given the uncertainty of current times, this will also help you have a better handle on your work if there are other unforeseeable disruptions.






© Magdalena Bennett - licensed under Creative Commons.