| Professor: | Colin Rundel (cr173@stat.duke.edu) |
| 204 Old Chemistry | |
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| TA: | Christoph Hellmayr (ch.hellmayr@gmail.com) |
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| Classroom: | Perkins Link 071 (Classroom 5) |
| Mondays and Wednesdays, 4:40 pm - 5:55 pm | |
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| Lab: | Perkins Link 070 (Seminar Room 4) |
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| 644 Lab - Fridays, 1:25 pm - 2:40 pm | |
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| Exams: | Midterm 1 - due TBD |
| Midterm 2 - due TBD | |
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| Holidays: | MLK Day - January 16 |
| Spring Break - March 13 - 17 |
Your final grade will be comprised of the following.
| Homework | 50% |
| Midterms | 40% |
| Final Project | 10% |
The exact ranges for letter grades will be curved and cutoffs will be determined after the final exam. The more evidence there is that the class has mastered the material, the more generous the curve will be.
The goal of both the lectures and the labs is for them to be as interactive as possible. My role as instructor is to introduce you new tools and techniques, but it is up to you to take them and make use of them. Statistics and programming are both skills that are best learned by doing, so as much as possible you will be working on a variety of tasks and activities throughout each lecture / lab.
You will be regularly given homework assignments, roughly one per week. These assignments will be composed of both theoretical / statistical questions as well as applied computational questions. These assignments are to be completed individually, but you are encouraged to work together to complete the assignments. Each assignment will be hosted in a private github repository within the class’s organization. All work will be written up using Rmarkdown documents (templates will be provided) and turned in via this github repository. Grading will be based on completeness and correctness as well as overall effort.
You are individually responsible for all work turned in, taking with and collaborating with another student is fine but you should never be directly sharing code or solutions. See the academic integrity section below if you have any questions about what constitutes cheating and or plagiarism. Any instances of directly copying another student’s work will at the very least result in a 0 on the assignment for any involved students as well as any additional penalties deemed appropriate by the instructor.
You will form a team of between 3-5 students and you will jointly be responsible for the completion of an open ended final project of your choosing. The goal of this project is for your team to tackle an “interesting” problem using the tools and techniques covered in this class. Additional details on the project will be provided as the course progresses. You will give a 15 minute presentation on your final project during the scheduled final exam period for the class (Tuesday, May 2 2:00 PM - 5:00 PM).
There will be a two take home midterms that you are expected to complete individually. Each exam will ask you to complete a number of small theoretical and or computational tasks related to the material presented in class. The exams will be written to take between 2-5 hours. The exact structure and content of the exams will be discussed in more detail before they are assigned.
I will regularly send course announcements by email, make sure to check your email daily. Email is the easiest way to reach me outside of class, note that it is much more efficient to answer most questions in person.
Duke University is a community dedicated to scholarship, leadership, and service and to the principles of honesty, fairness, respect, and accountability. Citizens of this community commit to reflect upon and uphold these principles in all academic and non-academic endeavors, and to protect and promote a culture of integrity. Cheating on exams or plagiarism on homework assignments, lying about an illness or absence and other forms of academic dishonesty are a breach of trust with classmates and faculty, violate the Duke Community Standard, and will not be tolerated. Such incidences will result in a 0 grade for all parties involved. Additionally, there may be penalties to your final class grade along with being reported to the Undergraduate Conduct Board.
Please review the Academic Dishonesty policies here.
A note on sharing / reusing code - I am well aware that a huge volume of code is available on the web to solve any number of problems. Unless I explicitly tell you not to use something the course’s policy is that you may make use of any online resources (e.g. StackOverflow) but you must explicitly cite where you obtained any code you directly use (or use as inspiration). Any recycled code that is discovered and is not explicitly cited will be treated as plagiarism. The one exception to this rule is that you may not directly share code with another team in this class, you are welcome to discuss the problems together and ask for advice, but you may not send or make use of code from another team.
Students who miss a class due to a scheduled varsity trip, religious holiday or short-term illness should fill out an online NOVAP, RHoliday or short-term illness form respectively. Note that these excused absences do not excuse you from assigned homework, it is your responsibility to make alternative arrangements to turn in any assignments in a timely fashion.
Those with a personal emergency or bereavement should speak with your director of graduate studies or your academic dean.
Statistics for Spatio-Temporal Data - Cressie & Wikle
Wiley, 1st edition, 2013 (ISBN: 978-0471692744)
Hierarchical Modeling and Analysis for Spatial Data - Banerjee, Carlin, Gelfand
CRC Press, 2nd edition, 2014 (ISBN: 978-1439819173)
Time Series Analysis and Its Applications - Shumway & Stoffer
Springer, 3rd edition, 2011 (ISBN: 978-1-4419-7864-6)