- Instructor: Junshu Bao, Department of Statistics, University of Pittsburgh
- Email: jub69@pitt.edu
- Office: Posvar 1829
- Phone: 412-624-5185
- Office Hours: Thursday 10:00 AM - 1:00 PM (Also available by Appointment)
- TA: Wei Peng
- Email: wep15@pitt.edu
- Office hours: Tuesday and Thursday 1:00 - 2:00 PM
- Meeting times and locations:
- Lecture: Monday/Wednesday 11:00 - 11:50 at CL 232
- Recitation:
- 1025: Friday 11:00 - 11:50 WWPH 1201 or CL 244B (9/14, 9/28, 11/2, 11/30)
- 1030: Friday 12:00 - 12:50 WWPH 1201
Course description
This course is a gentle introduction to Data Science. The following will be discussed:
- Introduction to Data Science
- Introduction to Data Science tools: R and RStudio
- Data Visualization
- Data Wrangling
- Ethics in Data Science
- Statistical thinking in Data Science
- Regression modeling
- Machine Learning (dimension reduction, clustering, classification)
- Professional Reporting and reproducible analysis
Course Logistics
Prerequisites
Statistics knowledge at the level of STAT 1000 or above.
No prior knowledge of programming required.
Getting help
Office hours
There will be office hours held each week. The times and locations can be found on the course website.
Piazza
We’ll be using Piazza as our online forum. Sign up at https://piazza.com/pitt/fall2018/stat1261
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:
- Be considerate to others (respectful language, no sarcasm).
- 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.
- 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).
- Content deemed inappropriate by the above rules and otherwise will be taken down by the TAs or Professor.
- Questions should be placed in the right folder (e.g., hw1, lab1, general).
- 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.
- Your participation will be rewarded.
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.
Grading
- Homework & Lab 50%
- Quiz 20%
- Final Project 20%
- Participation 10% (assessed on attendance and paticipation on Piazza )
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, drsrecep@pitt.edu, (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.