Course description

This course is a gentle introduction to Data Science. The following will be discussed:

  1. Introduction to Data Science
  2. Introduction to Data Science tools: R and RStudio
  3. Data Visualization
  4. Data Wrangling
  5. Ethics in Data Science
  6. Statistical thinking in Data Science
  7. Regression modeling
  8. Machine Learning (dimension reduction, clustering, classification)
  9. Professional Reporting and reproducible analysis

Course Logistics


Statistics knowledge at the level of STAT 1000 or above.

No prior knowledge of programming required.

Required Textbook

Getting help

Office hours

There will be office hours held each week. The times and locations can be found on the course website.


We’ll be using Piazza as our online forum. Sign up at

Piazza can be a very successful medium for helpful, class-wide discussions, but without rules, discussions can also quickly get out of hand. Here are the rules for our Piazza group:

  1. Be considerate to others (respectful language, no sarcasm).
  2. When it comes to the questions about the homework, “What is wrong with this code?” is not an acceptable question. Questions must be sufficiently generalized/modified/abstracted out so that it is not possible to directly construct parts or all of the solutions from them.
  3. Read the existing posts before you create your own, as often somebody else will have already asked the same question that you want to ask (or a very similar one).
  4. Content deemed inappropriate by the above rules and otherwise will be taken down by the TAs or Professor.
  5. Questions should be placed in the right folder (e.g., hw1, lab1, general).
  6. Private questions on Piazza (an option for questions that only TA and Professor can see) are not explicitly disallowed, but are discouraged, because the TAs and Professor may not be able to answer private questions in a timely manner.
  7. Your participation will be rewarded.

Course material

  1. This website:
    • Syllabus
    • Schedule
    • Course material
    • Online resources
  2. Piazza:
    • Discussions
    • Course material
    • Homework assignment
    • Announcements
  3. Your Courseweb (Blackboard)
    • Reading and homework assignment
    • Assignment submission

Grading components

1. Homework

  • Homework or lab activities will be assigned weekly.
  • Some homework questions require coding in R.

2. Quiz

  • Four quizzes throughout the semester

3. Final Project

4. No exams.


University Policies:

Academic Integrity

Students in this course will be expected to comply with the University of Pittsburgh’s Policy on Academic Integrity. Any student suspected of violating this obligation for any reason during the semester will be required to participate in the procedural process, initiated at the instructor level, as outlined in the University Guidelines on Academic Integrity. This may include, but is not limited to, the confiscation of the examination of any individual suspected of violating University Policy. Furthermore, no student may bring any unauthorized materials to an exam, including dictionaries and programmable calculators.

Disability Services

If you have a disability for which you are or may be requesting an accommodation, you are encouraged to contact both your instructor and Disability Resources and Services (DRS), 140 William Pitt Union, (412) 648-7890,, (412) 228-5347 for P3 ASL users, as early as possible in the term. DRS will verify your disability and determine reasonable accommodations for this course.