Programs in Data Sciences in the CSI Doctoral Program
The Computational Sciences and Informatics (CSI) PhD program is a
multidisciplinary program that addresses the role of computation in science,
technology, engineering, and mathematics (STEM). The program emphasizes
computational techniques for modeling and simulation of scientific and
engineering phenomena, as well as methods for extracting useful knowledge
from massive amounts of data, often in diverse structures and locations.
Within the CSI PhD program, there are
various specializations focusing on specific aspects of computational and data
sciences. Some of these areas are more concerned with numerical analysis, modeling,
and simulation, and others are more concerned with data science; that is,
with the theory and methods of data analysis.
The advancement of science is increasingly being driven by data. While
observational data has always been integral to the development of science,
a major change in recent years is the massive quantities of data available to
the scientist.
Data Science as an academic discipline is built on mathematics, statistics,
computer science, and inductive logic.
Courses in Data Sciences
The academic program in Data Sciences
requires background in probability and statistics at the
advanced-undergraduate/beginning-graduate level. (This background is covered
in the two GMU courses STAT 544 and STAT 554.)
Many of the courses in data science require a
calculus-level course in probability and statistics, so CSI 672, in addition to
some general knowledge of applied statistics, is a prerequisite for most of
the other courses listed below.
- CSI 672 - Statistical Inference
- CSI 674 - Bayesian Inference and Decision Theory
- CSI 676 - Regression Analysis
- CSI 678 - Times Series Analysis and Forecasting
- CSI 771 - Computational Statistics
- CSI 772 - Statistical Learning
- CSI 773 - Statistical Graphics and Data Exploration
- CSI 775 - Computational Models of Probabilistic Reasoning
- CSI 777 - Principles of Knowledge Mining
- CSI 779 - Topics in Computational Statistics
- CSI 873 - Computational Learning and Discovery
- CSI 876 - Measure and Linear Spaces
- CSI 877 - Geometric Methods in Statistics
- CSI 971 - Probability Theory
- CSI 972 - Mathematical Statistics I
- CSI 973 - Mathematical Statistics II
- CSI 976 - Statistical Inference for Stochastic Processes
- CSI 978 - Statistical Analysis of Signals
- CSI 979 - Advanced Topics in Computational Statistics
Some of these courses are taught every year, others are taught on a
two-year rotation, and still others, such as CSI 779, 976, 978, and 979,
are taught only when there is sufficient demand.