BDA 200TElements of Data Science3
DASC/SOC 205S Data, Technology, Society 3
- Subject
- BDA
- Credits (min)
- 3
- Credits (max)
- 3
- Type
- course
- Edition
- 2025-2026-undergraduate
- Source
- catalog.odu.edu
Eastern Virginia Medical School · Courses
BDA
22 courses with the subject BDA, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
BDA 401/501 Programming Languages for Data Science (3
- Subject
- BDA
- Credits (min)
- 3
- Credits (max)
- 3
- Type
- course
- Edition
- 2025-2026-undergraduate
- Source
- catalog.odu.edu
BDA 411/511 Introduction to Machine Learning ()3
An introductory course on machine learning. Machine Learning is the science of discovering pattern and structure and making predictions in data sets. It lies at the interface of mathematics, statistics and computer science. The course gives an elementary summary of modern machine learning tools. Topics include regression, classification, regularization, resampling methods, and unsupervised learning. Students enrolled are expected to have some ability to write computer programs, some knowledge of probability, statistics and linear algebra.
- Subject
- BDA
- Credits (min)
- 3
- Credits (max)
- 3
- Type
- course
- Edition
- 2025-2026-undergraduate
- Source
- catalog.odu.edu
- Prerequisite
- MATH 312, MATH 316, and STAT 330 or STAT 331
BDA 431Modern Statistical Methods for Big Data3
- Subject
- BDA
- Credits (min)
- 3
- Credits (max)
- 3
- Type
- course
- Edition
- 2025-2026-undergraduate
- Source
- catalog.odu.edu
BDA 432/532 Introduction to Optimization in Data Science (3
- Subject
- BDA
- Credits (min)
- 3
- Credits (max)
- 3
- Type
- course
- Edition
- 2025-2026-undergraduate
- Source
- catalog.odu.edu
BDA 450Senior Project in Big Data Analytics I3
At least three of the following courses: 9
- Subject
- BDA
- Credits (min)
- 3
- Credits (max)
- 3
- Type
- course
- Edition
- 2025-2026-undergraduate
- Source
- catalog.odu.edu
BDA 451Senior Project in Big Data Analytics II ()3
This course allows the student to pursue an in-depth exploration of a project initiated in BDA 450. The course involves written and oral presentations for students to improve communication and teamwork skills.
- Subject
- BDA
- Credits (min)
- 3
- Credits (max)
- 3
- Type
- course
- Edition
- 2025-2026-undergraduate
- Source
- catalog.odu.edu
- Prerequisite
- BDA 450 and permission of instructor
BDA 501Programming Languages for Data Science ()3
An introductory course on programming languages and tools which are relevant to data analytics. Each language or tool is introduced as a separate module and incorporates applications in mathematics and statistics. Examples of included programming languages and tools are MATLAB, Python, R and SAS. Additional languages and tools may be covered based on current trends in data analytics. Students will complete hands-on programming assignments throughout the course.
- Subject
- BDA
- Credits (min)
- 3
- Credits (max)
- 3
- Type
- course
- Edition
- 2025-2026-graduate
- Source
- catalog.odu.edu
- Prerequisite
- MATH 312, MATH 316 and STAT 330 or STAT 331
BDA 511Introduction to Machine Learning ()3
An introductory course on machine learning. Machine Learning is the science of discovering pattern and structure and making predictions in data sets. It lies at the interface of mathematics, statistics and computer science. The course gives an elementary summary of modern machine learning tools. Topics include regression, classification, regularization, resampling methods, and unsupervised learning. Students enrolled are expected to have some ability to write computer programs, some knowledge of probability, statistics and linear algebra.
- Subject
- BDA
- Credits (min)
- 3
- Credits (max)
- 3
- Type
- course
- Edition
- 2025-2026-graduate
- Source
- catalog.odu.edu
- Prerequisite
- MATH 312, MATH 316, and STAT 330 or STAT 331
BDA 531Modern Statistical Methods for Big Data3
- Subject
- BDA
- Credits (min)
- 3
- Credits (max)
- 3
- Type
- course
- Edition
- 2025-2026-graduate
- Source
- catalog.odu.edu
BDA 532Introduction to Optimization in Data Science (3
- Subject
- BDA
- Credits (min)
- 3
- Credits (max)
- 3
- Type
- course
- Edition
- 2025-2026-graduate
- Source
- catalog.odu.edu
BDA 611Mathematical Foundations of Machine Learning (3
- Subject
- BDA
- Credits (min)
- 3
- Credits (max)
- 3
- Type
- course
- Edition
- 2025-2026-graduate
- Source
- catalog.odu.edu
BDA 620Large-Scale Optimization ()3
This course will introduce optimization methods for large-scale problems by exploiting special structures including convexity and sparsity. Topics include introduction to convexity, gradient-related methods, dual methods, sparse optimization methods and nonconvex optimization methods. Students enrolled are expected to have some knowledge of linear algebra, optimization, probability, and analysis.
- Subject
- BDA
- Credits (min)
- 3
- Credits (max)
- 3
- Type
- course
- Edition
- 2025-2026-graduate
- Source
- catalog.odu.edu
- Prerequisite
- MATH 518 and STAT 330 or 331
BDA 632Computational Data Analytics Project ()3
Under the guidance of a faculty member in the Department of Mathematics and Statistics, the student will undertake a significant computational data analysis problem. A written report and/or public presentation of results will be required.
- Subject
- BDA
- Credits (min)
- 3
- Credits (max)
- 3
- Type
- course
- Edition
- 2025-2026-graduate
- Source
- catalog.odu.edu
- Prerequisite
- Permission of graduate program director
BDA 640Genomic Data Science ()3
Introductory discussion on central dogma of molecular biology, concepts of transcription, translation, gene regulation, and the need for high throughput methods. Other topics covered are Introduction to R and Bioconductor, Advanced microarray data analysis, NGS data analysis using edgeR in Bioconductor, Network Biology, sequence, pathway informatics, SNPs, GWAS, informatics for genome variants. instructor
- Subject
- BDA
- Credits (min)
- 3
- Credits (max)
- 3
- Type
- course
- Edition
- 2025-2026-graduate
- Source
- catalog.odu.edu
- Prerequisite
- BDA 511, BDA 531, and STAT 505 or permission of the
BDA 697Topics in Big Data Science ()3
Advanced study of selected topics.
- Subject
- BDA
- Credits (min)
- 3
- Credits (max)
- 3
- Type
- course
- Edition
- 2025-2026-graduate
- Source
- catalog.odu.edu
BDA 721High-Dimensional Statistics ()3
Techniques for obtaining basic tail bounds and concentration inequalities, uniform laws of large numbers, Rademacher complexity of a set, covering and packing in metric spaces, and metric entropy. Also, high dimensional random matrices described in a non-asymptotic framework, with a focus on the estimation of sparse and structured covariance matrix, are studied. The sparse linear regression models and the principal component analysis in the unstructured and sparse setting will be covered. Pre- or corequisite: STAT 727, STAT 728, MATH 616, and MATH 618
- Subject
- BDA
- Credits (min)
- 3
- Credits (max)
- 3
- Type
- course
- Edition
- 2025-2026-graduate
- Source
- catalog.odu.edu
BDA 731Applied Functional Data Analysis ()3
An introduction to the statistical analysis of sample curves or functions. Topics include smoothing, registration, functional principal component analysis, scalar-on-function regression, and functional response models. All these techniques will be applied using the statistical software R.
- Subject
- BDA
- Credits (min)
- 3
- Credits (max)
- 3
- Type
- course
- Edition
- 2025-2026-graduate
- Source
- catalog.odu.edu
- Prerequisite
- STAT 725 or STAT 825
BDA 745Transform Methods for Data Science ()3
Various transform methods from the data domain to coefficients of the data in certain discrete bases are studied. Transforms studied include FFT, DCT, wavelet transforms and framelet transform. Both theory and applications of these transforms are covered.
- Subject
- BDA
- Credits (min)
- 3
- Credits (max)
- 3
- Type
- course
- Edition
- 2025-2026-graduate
- Source
- catalog.odu.edu
- Prerequisite
- MATH 518 and MATH 616
BDA 821High-Dimensional Statistics ()3
Techniques for obtaining basic tail bounds and concentration inequalities, uniform laws of large numbers, Rademacher complexity of a set, covering and packing in metric spaces, and metric entropy. Also, high dimensional random matrices described in a non-asymptotic framework, with a focus on the estimation of sparse and structured covariance matrix are studied. The sparse linear regression models and the principal component analysis in the unstructured and sparse setting will be covered.
- Subject
- BDA
- Credits (min)
- 3
- Credits (max)
- 3
- Type
- course
- Edition
- 2025-2026-graduate
- Source
- catalog.odu.edu
- Prerequisite
- STAT 727, STAT 728, MATH 616, and MATH 618
BDA 831Applied Functional Data Analysis ()3
An introduction to the statistical analysis of sample curves or functions. Topics include smoothing, registration, functional principal component analysis, scalar-on-function regression, functional response models. All these techniques will be applied using the statistical software R.
- Subject
- BDA
- Credits (min)
- 3
- Credits (max)
- 3
- Type
- course
- Edition
- 2025-2026-graduate
- Source
- catalog.odu.edu
- Prerequisite
- STAT 725 or STAT 825
BDA 845Transform Methods for Data Science ()3
Various transform methods from the data domain to coefficients of the data in certain discrete bases are studied. Transforms studied include FFT, DCT, wavelet transforms and framelet transform. Both theory and applications of these transforms are covered.
- Subject
- BDA
- Credits (min)
- 3
- Credits (max)
- 3
- Type
- course
- Edition
- 2025-2026-graduate
- Source
- catalog.odu.edu
- Prerequisite
- MATH 518 and MATH 616
Source: Eastern Virginia Medical School's catalog, linked per course · table learning_unit · CourseShelf publish 59