Jeffrey Holt headshot
JH

Jeffrey J Holt

Professor
Unit: College of Arts and Sciences
Department: Department of Statistics
Office location and address
112 Halsey Hall
148 Amphitheater Way
Charlottesville, Virginia 22904
AS-STAT Updating the WEBWORK National Problem Library
Source: U.S. NSF - Directorate For Ed. & Human Resources
September 01, 2012 – August 31, 2016
AS-MATH MSP Institutes - Math Specialists in Middle School
Source: Virginia Commonwealth University
August 01, 2009 – July 31, 2015
STAT 1400: Forensic Science and Statistics
Credits: 3
This course provides an introduction to statistical analysis in the context of forensic science. Statistical topics covered include probability distributions, hypothesis testing, confidence intervals, measures of association, and regression. Applications drawn from forensics include analysis of fingerprints, DNA, and particle evidence. No prior knowledge of statistics or forensic science is required.
COLA 1500: College Advising Seminars
Credits: 1
COLA courses are 1-credit seminars capped at 18 first-year students, all of whom are assigned to the instructor as advisees. They are topically focused on an area identified by the faculty member; they also include a significant advising component centered on undergraduate issues (e.g., choosing a major, study abroad opportunities, undergraduate research, etc.). For detailed descriptions see http://college.as.virginia.edu/COLA
XHOS 1559: New Class in Xhosa
Credits: 1–4
This course provides the opportunity to offer a new topic in the subject area of Xhosa language or literature.
STAT 1559: New Course in Statistics
Credits: 1–4
This course provides the opportunity to offer a new topic in the subject area of statistics.
STAT 3250: Data Analysis with Python
Credits: 3
This course provides an introduction to data analysis using the Python programming language. Topics include using an intergrated development environment; data analysis packages numpy, pandas and scipy; data loading, storage, cleaning, merging, transformation, and aggregation; data plotting and visualization.
MATH 3350: Applied Linear Algebra
Credits: 3
Topics will include systems of linear equations, matrix operations and inverses, vector spaces and subspaces, determinants, eigenvalues and eigenvectors, matrix factorizations, inner products and orthogonality, and linear transformations. Emphasis will be on applications, with computer software integrated throughout the course. The target audience for MATH 3350 is non-math majors from disciplines that apply tools from linear algebra. Credit is not given for both MATH 3350 and 3351.
LASE 3559: New Course in the Liberal Arts
Credits: 1–6
This course provides the opportunity to explore a range of topics in the liberal arts and sciences.
STAT 3559: New Course in Statistics
Credits: 1–4
This course provides the opportunity to offer a new topic in the subject area of Statistics.
MATH 4140: Mathematics of Derivative Securities
Credits: 3
This class introduces students to the mathematics used in pricing derivative securities. Topics include a review of the relevant probability theory of conditional expectation and martingales/the elements of financial markets and derivatives/pricing contingent claims in the binomial & the finite market model/(time permitting) the Black-Scholes model. Prerequisites: MATH 3100 or APMA 3100. Students should have a knowledge of matrix algebra.
STAT 4559: New Course in Statistics
Credits: 1–4
This course provides the opportunity to offer a new topic in the subject area of Statistics.
STAT 4993: Independent Study
Credits: 1–4
Reading and study programs in areas of interest to individual students. For students interested in topics not covered in regular courses. Students must obtain a faculty advisor to approve and direct the program.
STAT 4996: Capstone
Credits: 3
Students will work in teams on a capstone project. The project will involve significant data preparation and analysis of data, preparation of a comprehensive project report, and presentation of results. Many projects will come from external clients who have data analysis challenges.
STAT 5180: Design and Analysis of Sample Surveys
Credits: 3
This course covers the main designs and estimation techniques used in sample surveys: simple random sampling, stratification, cluster sampling, double sampling, post-stratification, ratio estimation, and non response and other non sampling errors. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using R statistical software. Prerequisites: STAT 3120.
STAT 5993: Directed Reading
Credits: 1–3
Research into current statistical problems under faculty supervision.
STAT 6430: Statistical Computing for Data Science
Credits: 3
An introduction to statistical programming, including data manipulation and cleaning, importing and exporting data, managing missing values, data frames, functions, lists, matrices, writing functions, and the use of packages. Efficient programming practices and methods of summarizing and visualizing data are emphasized throughout. SAS and R are the primary computational tools. Prerequisite: A previous statistics course and permission of instructor.
STAT 8120: Topics in Statistics
Credits: 3
Study of topics in statistics that are currently the subject of active research.
STAT 9993: Directed Reading
Credits: 1–9
Research into current statistical problems under faculty supervision.
STAT 9999: Non-Topical Research
Credits: 1–12
For doctoral research, taken under the supervision of a dissertation director.