|Thu, Jan 11||Introduction||Enrollment Survey|
|Fri, Jan 12||No Lab|
|Tue, Jan 16||Linear Models||Matrix Cookbook|
|Thu, Jan 18||Class canceled - Snow day|
|Fri, Jan 19||No Lab|
|Tue, Jan 23||Diagnostics and Model Evaluation|
|Thu, Jan 25||Generalized Linear Models (pt. 1)||
|Tue, Jan 30||Generalized Linear Models (pt. 2)||
|Thu, Feb 1||Random Effect Models|
|Tue, Feb 6||Discrete Time Series||
|Thu, Feb 8||Differencing and AR(1) models||
|Tue, Feb 13||ARMA models||
|Thu, Feb 15||ARIMA models||
|Tue, Feb 20||Model fitting and forecasting||
|Thu, Feb 22||Seasonal ARIMA||
|Tue, Feb 27||Midterm 1|
|Thu, Mar 1||Gaussian Process models (pt. 1)|
|Tue, Mar 6||Gaussian Process models (pt. 2)|
|Thu, Mar 8||Covariance Functions|
|Tue, Mar 13||No class - Spring recess|
|Thu, Mar 15||No class - Spring recess|
|Tue, Mar 20||GPs & GLMs + Spatial Data (pt. 1)|
|Thu, Mar 22||Spatial Data and Cartography (pt. 2)|
|Tue, Mar 27||Models for areal data (pt. 1)||
|Thu, Mar 29||Models for areal data (pt. 2)|
|Tue, Apr 3||GLMs for areal data + Point referenced data (pt. 1)|
|Thu, Apr 5||Point referenced data (pt. 2)|
|Tue, Apr 10||Computation and GP Models|
|Thu, Apr 12||Spatio-temporal models|
|Tue, Apr 17||Midterm 2|
|Thu, May 3||Project due by 11:59 pm|
Lectures & Lab:
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, data analysis, and programming are all 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. Attendance will not be taken during class but you are expected to attend all lecture and lab sessions and meaningfully contribute to in-class exercises and homework assignments.
Social Psych 126
- Lecture - Tuedays & Thurdays 10:05 pm - 11:20 pm
Social Sciences 119
- Lab - Fridays 8:30 am - 09:45 am
- Monday, January 15 - Martin Luther King, Jr. Day
- Monday, March 12 to Friday, March 16 - Spring Break
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' organization. All work should 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 your own team of 2-5 students and will be responsible for the completion of an open ended final project for this course, the goal of which is 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 10-20 minute presentation on your final project in class.
There will be up to two in class midterms that you will complete individually. Each exam will ask you to complete a small number of questions related to the material presented in the class. 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.
Late work policy:
- late, but same day: -10%
- late, next day: -20%
- 2 days or later: no credit
Your final grade will be comprised of the following.
- Homework: 50%
- Midterms: 30%
- Final Project: 20%
The exact ranges for letter grades will be curved and cutoffs will be determined at the end of the semester. The more evidence there is that the class has mastered the material, the more generous the curve will be.
There are no required textbooks for this course, the following textbooks are recommended for supplementary and reference purposes.
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, 4th edition, 2016 (ISBN: 978-3319524511)
Data Analysis Using Regression and Multilevel/Hierarchical Models - Gelman & Hill
Cambridge University Press, 1st edition, 2003 (ISBN: 978-0521686891)
Applied Bayesian Modelling - Congdon
Wiley, 2003 (ISBN: 978-0471486954)
- Prof. Rundel - 204 Old Chemistry - Wednesdays 11 - 1, or by appointment
- Phil White - 211A Old Chemistry - Tuesdays 9-10 and Thursdays 11:30-12:30
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