• Love working with data?
• Believe that models should be right only most of the time?
• Interested in working in a field where you collaborate with everyone
  and are never bored?
• In the movie Moneyball, did you identify more with Jonah Hill than Brad Pitt?

Then Statistics & Data Science is for you....
At Carnegie Mellon, you can major in Statistics, Statistics & Machine Learning, Economics-Statistics, Statistics and Neuroscience (track), and Mathematical Statistics (track).
Learn more here.

CMU Undergrad Statistics & Data Science in the News: link

Congratulations on Carnegie Mellon Math & Statistics being ranked #1 in the country
for two years in a row! (collegefactual.com)

Check out the latest episodes of the Tartan Data Science Cup!

Find out more about our Summer Undergraduate Research Experience in Statistics here.

Follow us @CMU_Stats

Related Press:

• Interview with Simply Statistics (Authors: Jeff Leek, Roger Peng, Rafa Irizarry; Johns Hopkins Biostatistics). Link . October 2012.
• "Maintaining Quality in the Face of Rapid Program Expansion" Invited article for AMSTATNEWS: The Membership Magazine of the American Statistical Association. August 2012, Issue #422, p.14-15. link

Awards

• American Statistical Association 2015 Waller Education Award
  national award for innovation in statistics education; see here for more details
• The William H. and Frances S. Ryan Award for Meritorious Teaching
  Carnegie Mellon University (2015)
• Elliott Dunlap Smith Award for Distinguished Teaching and Educational Service
  Dietrich College of Humanities & Social Sciences, Carnegie Mellon University (2013)

Presentations, Interviews, Panels

• U.S. Conference on Teaching Statistics Opening Session Speaker, May 2017
• TEDx CMU: Pivot Embracing Your Inner Data Scientist. April 2017. Link
• National Academy of Science, Engineering, and Medicine
  Workshop on "Envisioning the Data Science Discipline: The Undergraduate Perspective"
  If You Build It, They Will Come: Perspectives on an Undergraduate Statistics/Data Science Program. December 2016.
• NSF-Census Research Network (NCRN) Spring 2015 meeting, joint with the National Academy of Sciences, Building and Training the Next Generation of Survey Methodologists and Researchers. May 2015
• Joint Mathematical Meetings Choose-Your-Own Capstone Adventure: Providing Flexible Paths for Undergraduate Majors. January 2015.
• Joint Statistical Meetings. Choose-Your-Own Capstone Adventure: Providing Flexible Paths for Undergraduate Majors. August 2014.
• ENAR: International Biometric Society. Massive Online Open Statistics: Should We Be teaching Statistics to 100s of Thousands of Students? Invited Talk, March 2014.
• ASA Webinar. The Role and Variety of Undergraduate Statistics Capstones. with C Shalizi, December 2013.
• CMU Statistics "Online Teaching Tea". Going Online: Should we do it? How? Why? What do we gain? What do we lose?. An open discussion about experiences with online statistics education including MOOCs. Organizer, April 2013.
• Interview with Simply Statistics (Authors: Jeff Leek, Roger Peng, Rafa Irizarry; Johns Hopkins Biostatistics). Link . October 2012.
• Joint Statistical Meetings. Maintaining Quality in the Face of Rapid Program Expansion. Invited Talk, August 2012.

The Teaching Statistics group engages in a combination of research, pedagogy, and classroom training. Members are interested in updating and modernizing curriculum and assessment, pedagogical philosophy and development, best classroom practices, student engagement, and outreach to a diverse community.

Visit the group website to learn more.

Articles

• Nugent, R. "Maintaining Quality in the Face of Rapid Program Expansion" Invited article for AMSTATNEWS: The Membership Magazine of the American Statistical Association. August 2012, Issue #422, p.14-15. link

Chapters

• Ngamruengphong S, Nugent A, Nugent K, Nugent R (authorship in alphabetical order). Case 49: Prostate-Ca-Survival Case Files: Geriatric (LANGE Case Files), Toy, Dentino, Williams, Johnson (editors). McGraw-Hill Medical, 2014. Available here .
  
  
• Nugent R and Meila M. An Overview of Clustering Applied to Molecular Biology. Statistical Methods in Molecular Biology. Bang, Zhou, Epps, Mazumdar (editors). Springer/Humana Press, 2010. Available here .

National Positions & Service

• National Academies of Science, Engineering, and Medicine
  Committee on Envisioning the Data Science Discipline: The Undergraduate Discipline
• American Statistical Association Section on Statistical Education
  Executive Committee
• National Science Foundation Division of Graduate Education Review Panelist

For a full list of pedagogical service and program development activities at the national, university, and departmental levels, please see CV .

• NSF: DUE/IUSE
  Supercharging the Data Science Classroom: Giving students agency and reach
  with new, interactive technologies
  PI with C Genovese (PI), P Burckhardt (Investigator)
  $2,000,000, five years; submitted December 2017
  
  
• SIGKDD Impact Program
  The Carnegie Mellon Data Science Experience: Why take a course in Data Science when you can Experience it?
  PI with Key Personnel G Weinberg and P Burckhardt, Collaborators W Alba and C Genovese; $50,000, one year. Submitted December 2017.
  
  
• Carnegie Mellon ProSEED/Crosswalk
  What is Statistics? An Interactive Platform that Engages and Educates the Non-Statistician
  co-PI with Paige Houser (PI) and Howard Seltman (co-PI); $2500; Summer 2015
  
  
• NSF: Research Training Groups in the Mathematical Sciences
  Statistics and Machine Learning for Scientific Inference
  co-PI with R Kass (PI), W Eddy (PI); $2,250,979, May 2011 - June 2016
  
  Provides support during the academic year and the summer for undergraduates to work on research projects; students work in a vertically integrated environment (research group with professor, graduate students, other undergraduate students); students gain skills in research, writing, oral presentations and defense of work
  
  For information on academic year positions, contact Rebecca Nugent (rnugent@stat.cmu.edu)
  
  For information on our summer program (Summer Undergraduate Research Experience), contact Bill Eddy (bill@stat.cmu.edu) or Margie Smykla (mk74@stat.cmu.edu).
  
  Minorities and underrepresented populations are encouraged to apply.
  
  
• NSF: The NSF-Census Research Network Supplement, September 2016-August 2017
  Census Research Node: Data Integration, Online Data Collection, and Privacy Protection for Census 2020; co-PI with S Fienberg (PI), W Eddy (PI), A Acquisti (co-PI), $650,000
  
  
• NSF: The NSF-Census Research Network
  Census Research Node: Data Integration, Online Data Collection, and Privacy Protection for Census 2020; co-PI with S Fienberg (PI), W Eddy (PI), A Acquisti (co-PI
  $3,000,000, Sept 2011 - Sept 2016
  
  For general information on our CMU node, see here.
  
  I oversee the education component of this grant, including curriculum development of classes and modules related to the node's research and alignment of student research projects. Examples include:
  
  
  • Data Matching Methods and Their Uses, a graduate and advanced undergraduate course developed and co-taught by Fienberg and Nugent
  • Graphics and Visualization modules on visualing United States Census information
  • Record Linkage modules on identifying casualties in civil wars
  
  Information about the educational activities under this grant can be directly viewed here.

• Villanova Center for Statistics Education Workshop, May 2017.
  Classification and Clustering: The Basics, The Next Level
  
  
• Park City Math Institute Undergraduate Summer School (PCMI 2016), July 2016.
  Visualizing and Learning the Structure in Data
  lecturer and author of materials for month-long program; more info here
  
  
• ASA Conference on Statistical Practice (CSP 2015), February 2015.
  An Overview of Clustering: Finding and Extracting Group Structure in High-Dimensional Data; co-presenter and author of materials; more info here
  
  
• 7th International Conf. on Educational Data Mining (EDM 2014), June 2014.
  An Overview of Clustering: Finding Group Structure in Educational Research Data
  presenter and author of materials; more info here
  
  
• Math Camp, Center for Statistics & Social Sciences, University of Washington
  Designed for social science first year graduate students, review prior to statistics courses
  Handouts, Practice Problems/Solutions
  Disclaimer: wrote material in 2004, 2005; current version may be slightly altered

An research problem library for undergraduate and master's statistics curricula.
Each entry consists of background information, a cleaned, documented data set,
and examples of corresponding exams, projects, questions, etc.
Contributions are welcome!

In progress. Check back soon!

Current

Carnegie Mellon, Dept of Statistics

• 36-200: Reasoning with Data
  (an Intro to Data Science course for students in Humanities and Social Sciences)

Past Courses

Carnegie Mellon, Tepper School Computational Finance Master's program:

• 46-921: Probability
• 46-923: Statistical Inference

Carnegie Mellon, Dept of Statistics graduate courses:

• 36-729: Unsupervised Learning
• 36-721: Statistical Graphics & Visualization
  Handbook of Data Visualization: Chen, Hardle, Unwin (eds)
• 36-491/691/791: Data Matching Methods and Their Uses
• 36-492/692/792: Topic Detection and Document Clustering: What on Earth were They Talking about at Enron before It Imploded?

Carnegie Mellon, Dept of Statistics undergraduate courses:

• 36-490: Undergraduate Research
• 36-462: Topics in Statistics: Statistical Learning
  Finding Groups in Data: An Introduction to Cluster Analysis: Kaufman, Rousseeuw
  The Elements of Statistical Learning: Hastie, Tibshirani, Friedman
  
• 36-401: Modern Regression
  Applied Linear Regression Methods: Kutner, et al. McGraw-Hill, 4th Ed. 2004.
• 36-315: Statistical Graphics and Visualization
  Graphics for Statistics and Data Analysis with R: Keen
  Interactive and Dynamic Graphics for Data Analysis: Cook and Swayne
  
• 36-303: Sampling, Survey, and Society
  Sampling: Design and Analysis: Lohr
• 36-226: Introduction to Statistical Inference
  Mathematical Statistics with Applications: Wackerly, et al
  
• 36-149: Freshmen Statistics Seminar
  Networks: Where do they come from? What do they tell us?

China Education Association for International Exchange (Beijing)

• Summer China Program: Calculus II, Intro to Statistics

University of Washington, Dept of Statistics

• Statistics: Freedman, Pisani, and Purves
  Lecture Notes, Practice Problems, Practice Exams
  
• Introduction to the R Language
  Handouts, Example Problems/Solutions
  Material has a social science flavor (from a prior class)
  
• University of Washington Statistics Tutor & Study Center
  Co-founder/student director of program providing free statistics tutoring
  staffed by graduate students