Awards
Duke Masters of Interdisciplinary Data Science (MIDS) Faculty Teaching Award
Awarded by the MIDS Graduating Class of 2023
Courses
Practical Data Science
Duke, Fall 2019 – Present
Duke Masters in Interdisciplinary Data Science (MIDS) fall semester course. Data Science is an intrinsically applied field, and yet all too often students are taught the advanced math and statistics behind data science tools, but are left to fend for themselves when it comes to learning the tools we use to do data science on a day-to-day basis or how to manage actual projects. This course is designed to fill that gap by providing students with strategies for collaboration and workflow management, along with extensive hands-on experience manipulating real (often messy, error ridden, and poorly documented) data using the a range of bread-and-butter data science tools (like the command line, git, python (especially numpy and pandas), jupyter notebooks, and more).
Role: Instructor, Creator
Fall 2019:
- Overall Rating of Course: 4.86/5
- Overall Rating of Professor: 4.93/5
Fall 2020:
- Overall Rating of Course: 4.76/5
- Overall Rating of Professor: 4.88/5
Fall 2021:
- Overall Rating of Course: 4.73/5
- Overall Rating of Professor: 4.90/5
Fall 2022:
- Overall Rating of Course: 4.39/5
- Overall Rating of Professor: 4.79/5
Unifying Data Science
Duke, Spring 2020 – Present
Duke Masters in Interdisciplinary Data Science (MIDS) Spring semester course. This course is focused on how to answer questions effectively using quantitative data. By the end of the course, students will be able to recognize different types of questions (e.g. descriptive, causal, and predictive questions), have an understanding of what methodological approaches are most appropriate for answering each type of question, be able to design and critically evaluate data analysis plans, and understand how to tailor their presentation of results to different audiences.
Role: Instructor, Creator
Spring 2020:
- Overall Rating of Course: 4.94/5
- Overall Rating of Professor: 5.00/5
Spring 2021:
- Overall Rating of Course: 4.28/5
- Overall Rating of Professor: 4.79/5
Spring 2022:
- Overall Rating of Course: 4.38/5
- Overall Rating of Professor: 4.63/5
Astute readers will notice a decline in the scores received by the course in its second and third iterations. While the courses firmly receive average ratings in the category of “excellent” (above 4 out of 5), I have given a great deal of thought to this decline. A major factor, I believe, is that while students may have enjoyed the first iteration of the course, when I looked back on the class at the end of the semester, I concluded that I had not fully succeeded in helping my students achieve my learning goals. In particular, I felt that I had not been very successful in driving home the inherent uncertainty in both data science as a whole, and in causal inference—where the validity of all inferences are based on fundamentally untestable assumptions—in particular. Many of my students come from an engineering background, and I believe see data science as a way to rationalize the world around them into simple numbers, and I under-estimated their resistance to upsetting that view. In subsequent iterations of the course, I have re-structured sections on key concepts—especially in the portion of the course focused on causal inference—in ways that I think have done a much better job of challenging and unsettling students. As a result, I actually think students in the two later iterations of the course actually better achieved my learning goals, and thus I view those iterations as greater successes, despite the decline in course ratings. Moving forward, I will continue to work on improving the course in an effort to both continue to unsettle students where necessary but also help them to see and value that learning goal as reflected in their reviews and thus how they view the success of the course.
Computational Methods for Social Scientists (cm4ss) Boot Camp
Duke, Summer 2021 and Summer 2022
Designed and developed boot camp for incoming social science graduate students. Attended by all incoming Duke Political Science Masters and PhD students, as well as some sociology and Nicholas School incoming graduate students. Focused on developing familiarity with basic functional of R, including basic data types, data manipulation, and plotting.
Role: Instructor, Creator
Computational Methods Boot Camp
Vanderbilt, Fall 2018
Boot camp to provide all incoming Vanderbilt Political Science graduate students with a solid foundation in computational methods before the start of the academic year. Focused on developing familiarity with basic functional of R, including basic data types, data manipulation, and plotting.
Role: Instructor, Creator
POLISCI 344: Politics and Geography
Stanford, Fall 2015
Graduate class on the role of geography in topics in political economy, including development, political representation, voting, redistribution, regional autonomy movements, fiscal competition, and federalism.
Role: Co-Instructor; wrote new curriculum for GIS in R (previously taught in ArcGIS)
- Average Learning Quality Score: 5.0 / 5.0
- Average Instructional Effectiveness Score: 4.8 / 5.0
POLISCI 241S / ANTHRO 130D / URBANST 134: Spatial Approaches to Social Science
Role: Lab Assistant; Extensive out-of-class support for student projects (primary learning goals are met through student projects).
- Average Learning Quality Score: 4.3 / 5.0
- Average Instructional Effectiveness Score: 4.5 / 5.0
Select Student Comments from Evaluations
- I cannot emphasize enough how lovely Nick was as a professor. In my time in grad school he created a more compassionate, and mentoring environment than any other professor I have had. I felt comfortable talking to Nick about questions and he was so encouraging to everyone in the class. (Practical Data Science, 2021)
- Nick is honestly one of the best professors. He genuinely enjoys the subject and cares whether I understand. On top of that, he gets excited and wants all of us to learn more about this subject.Thank you, Nick. (Practical Data Science, 2021)
- Professor Eubank showed genuine interest in my development as data scientist throughout the term. I will never forget him teaching how to properly create indicator variables during his office hours. He also encouraged me during inclass exercise on object-oriented programming. I am grateful for the care he showed me. (Practical Data Science, 2021)
- There are many professors who are knowledgable but not the best at teaching but you excel at it. And besides that (and maybe even more importantly) you’re a great human that a student at Duke can expect to receive honest support from, especially in these times of crisis. (Practical Data Science, 2021)
- Nick is so perceptive to how everyone is feeling. He was flexible with his assignments and the class schedule. He effectively incorporated our feedback from the mid-semester survey into the class. He is a very effective educator and I’m looking forward to having him again next semester. (Practical Data Science, 2021)
- The mix between homework readings, in class examples, and breakout rooms was AMAZING. Such a great way to get hands-on practicing the material. I also appreciated working on exercises in partners. I had no coding experience and I found that most of my partners had taken at least one programming class, so working with them helped me pick things up quicker. ALSO the TA’s were so kind and helpful in the breakout rooms! (cm4ss, 2021)
- Definitely a great program for a core class. Plus, definitely a class non-MIDS students with a certain level of tech skills can and should take. I learned a lot from the MIDS students and as an MPP student feel like they also learned a lot from my perspective on our class content. I think the class really benefits from this diversity. I feel empowered in my coding skills now and am looking forward to applying them more in my public policy work! (Unifying Data Science, 2021)
- Professor Eubank is an excellent instructor. Beyond his understanding of python and its implementation within data science, he genuinely cares about his students and their learning. I feel that his class should be a part of the core curriculum rather than an elective. As a 2nd year who’s been through the extent of the core curriculum, it would have been heavily beneficial to the second semester core classes. (Practical Data Science, 2020)
- Nicholas Eubank was one of, if not the, best professor I’ve ever had. Not only was he very knowledgable, readily available, and incredibly thoughtful about the structure of the course, but he also understands and applies the pedagogy of learning and best educational practices to really help his students understand and apply the material. He also cares for his students’ well being. Whatever you are paying him, it isn’t enough! (Practical Data Science 2020)
- This course was well laid out, well-taught, and took me from no knowledge to a decent working knowledge of programs and concepts, which is a major accomplishment. Some of the assignments were very difficult, but I understood what the professor was going for. (Practical Data Science, 2020)
- Definition of what a Duke Professor should be (Practical Data Science, 2020)
- Nick, this class was one of the best classes I took at MIDS. I think that your method of group work and cold call discussion really forced me to learn the material that I honestly don’t have the discipline to learn on my own just by reading. You were also very organized which is something I always appreciate. Your communication of the material was superb. Every word you said held value and you always spoke very coherently and cogently. Frankly, there are only a handful of classes that I took at MIDS that I would strongly recommend to future students and that I learned a great deal from. You did not overburden us with homework and readings–perfect amount. Your notes were clear, informative and not overly verbose. I also appreciated your flexibility in terms of surveying the class regularly to ensure that things were, in fact, running smoothly. It just shows that you actually care about how we learn, what we learn and our general interest. I hope that you have some role in future MIDS classes because your teaching style and material was invaluable. (Unifying Data Science, 2020)
- I really appreciated the usage of real world data in the class. Additionally, the fact that all of the exercises were created for this course was impressive. The exercises seemed to anticipate exactly where my hang ups would be with whatever we were working with. Have the opportunity to fail (with the expectation of failure) allowed me to learn from those mistakes. I now can write code without freaking out about it not working immediately. The course was effective in identifying where python deviates from how I think it should work and showing me the differences. (Practical Data Science, 2019)
- Nick was very enthusiastic & friendly throughout the R course. He clearly explained the basics of R and addressed the questions raised by students. He introduced very useful materials so that I can go over and continue studying R. I love his class! … Nick came to class early before the actual class began and he helped students who had difficulties with homeworks. He even stayed after class to help out students! (Computational Methods Boot Camp, 2018)
- The in-class exercises were incredibly helpful. Nick was exactly the right kind of combination of hands-off and instructional. He really facilitated my learning. … Nick was fun and excellent and I wish this section of math camp was longer! (Computational Methods Boot Camp, 2018)
- This TA was fantastic in teaching GIS analysis in R — his tutorials were incredibly well put-together, and the pace and content were both extremely appropriate for students’ needs. (Politics and Geography, 2015)
- Nick presents materials very clearly and answers questions very effectively. He is quick to address any issue that comes up while using R or ArcGIS because he is so good at and so familiar with the softwares. (Politics and Geography, 2015)
- Nick is the best TA I’ve had at Stanford. He’s extremely attentive in class while students work on labs. He tended to students’ questions and addressed students’ concerns consistently, effectively, and efficiently. Nick was much more in tune with students’ projects than the professor and helped students overcome technical challenges and conduct difficult quantitative analyses. The fact that everyone had their own projects, and therefore unique questions, speaks particularly highly to Nick’s ability to serve students with a broad scope of challenging questions. Every student I spoke to in the class felt similarly. (Spatial Approaches to Social Science, 2015)
- He was good at helping me find direction with my group project and helping bridge the gap between interesting thematic subject matter and the technical/methodological ways of designing and executing a project that explored these goals. He was really good at using simple, basic language to explain big concepts or ideas or to explain technological processes in ARCGIS. (Spatial Approaches to Social Science, 2015)
- Nick was the best TA I have had at Stanford! He was always well-prepared; he stayed in constant communication with the class so that we knew exactly what was expected; he was available whenever we needed him; and his knowledge of the material and his ability to share that with us were excellent. He was very friendly and easy to talk to (Spatial Approaches to Social Science, 2015)
- I came to the class entirely unfamiliar with ArcGis and fairly insecure about my ability to leverage software systems to conduct quantitative analysis. Nick explained answers to my questions clearly without being condescending. He made big challenges feel manageable. His comfort with the subject material clearly transferred in his ability to answer questions effectively and confidently. (Spatial Approaches to Social Science, 2015)