DSCI5300 Introduction to Data Science - 3 s.h.
An introduction to the methods of data science through a combination of computational exploration, visualization, and theory. Students will learn scientific computing basics, topics in numerical linear algebra, mathematical probability, statistics, and social and political issues raised by data science. Prerequisites: Prior courses in statistics, calculus and basic programming.
DSCI5320 Practical Applications of Data Science - 3 s.h.
Exploratory data analysis is introduced along with fundamental considerations for data analysis on real data sets. Classical models and techniques for classification are included. Methods of data visualization are introduced. Prerequisites: DSCI5300
DSCI5330 Extracting and Transforming Data - 3 s.h.
Students will learn skills of data acquisition, methods, of data cleaning, imputing data, data storage and other important issues required to producing usable data sets. Code books, data standards, and markdown files will be introduced as well as the concept of the data lake. Prerequisites: DSCI5300.
DSCI5340 Probability and Statistical Inference - 3 s.h.
This course covers the fundamentals of probability theory and statistical inference used in data science. Students will be introduction to statistical modeling including linear regression models, and generalized linear regression models. Prerequisites DSCI5320.
DSCI5350 Basics of Computer Algorithms and Databases - 3 s.h.
An introduction to computer systems, architecture and programming for data science. Coverage includes data structures, algorithms, analysis of algorithms, algorithmic complexity, programming using test-driven design, use of debuggers and profilers, code organization, and version control. Additional topics include data science web applications, SQL, and distributed computing. Prerequisites DSCI5320.
DSCI5360 Regression and Time Series Modeling - 3 s.h.
A modern introduction to inferential methods for regression analysis and statistical learning, with an emphasis on application in practical settings in the context of learning relationships from observed data. Topics will include application of linear regression, general linear models, variable selection and dimension reduction, and approaches to nonlinear regression. Extensions to other data structures such as longitudinal data and the fundamentals of causal inference will also be introduced and applied. Prerequisites: DSCI5340.
DSCI5370 Machine Learning - 3 s.h.
The course covers the most often used methods of the machine learning in a practical context. Methods such as ridge and lasso regression, cross-validation, support vector machines, decision trees, and ensemble methods, PCA, gradient descent, stochastic gradient descent, and block coordinate descent. Prerequisites: DSCI5350.
DSCI5400 Data Mining - 3 s.h.
This course will provide students with an understanding of the field of data mining and knowledge discovery in data. Students will become familiar with the foundations of data mining through exploring real-world use cases and cutting edge research in data mining published in academic journals and conferences from various perspectives. Students will also gain hands on experience with data mining tools combined with machine learning and visualization functions. Prerequisite: DSCI5340 and DCSI5350 or permission of instructor.
DSCI5420 Artificial Intelligence in Practice - 3 s.h.
This course will cover fundamental concepts of artificial intelligence including algorithms and tools as well their real-world applications. Topics include intelligent agents, knowledge reasoning, learning, and AI problem solving in vision, language, robotics, medicine, etc. Special emphasis is placed on how AI technologies transform businesses and our day-to-day lives by influencing society’s values. Prerequisite: DSCI5340 and DCSI5350 or permission of instructor.
DSCI5440 Big Data Analytics - 3 s.h.
This course provides students with an understanding of the field of large scale data analytics using a high performance, cloud computing data analytics framework. Students will analyze public datasets, network data, and non-structured, steaming dataset. Students will work on real-world cases, learn how to process the data to find valuable insights, and present solutions or suggestions for these cases. Students are encouraged to utilize free and commonly used Open Source Big Data framework and NoSQL database tools. Prerequisite: DSCI5320 or DSCI5330 or permission of instructor.
DSCI5500 Air Pollution and Health Analytics - 3 s.h.
This introductory course will address the relations between air pollution and human health and the environment in the context of statistical and regression analysis. Specific areas include air toxic monitoring, particulate matter and indoor air pollution. Prerequisite: DSCI5340 and DCSI5350 or permission of instructor.
DSCI5520 Water Quality and Nutrients - 3 s.h.
This introductory course will address the relationship between water pollution and nutrients in the context of statistical and regression analysis. Specific areas include urban and rural fertilizer application, soil partitioning and moisture, remote sensing and imaging. Prerequisite: DSCI5340 and DCSI5350 or permission of instructor.
DSCI5540 Chemical Emissions Modeling - 3 s.h.
This course will examine the data science behind the chemical emissions models used to predict episodes of photochemical smog, acid deposition, algal blooms, and other environmental events. The goal of the course is to develop augmented modeling methods for application to regional issues. Prerequisite: DSCI5500 and DCSI5520 or permission of instructor.
DSCI5700 Internship - 3 s.h.
Students work in conjunction with a supervisor in industry on current problem of importance. The student will gain experience with real – world problems, client presentations, and written data communications. The supervisor, student, and faculty advisor will construct a project plan with expected accomplishments. The supervisor will provide written feedback on the student including an assessment of how the student performed in meeting the expected accomplishments. The faculty member will be responsible for assigning the final grade. To receive credit the project must take 8 weeks to complete. May be repeated once for Emphasis credit with permission. Prerequisite: DCSI5400 or permission of the instructor.
DSCI6000 Data Science Capstone - 3 s.h.
Students work with a practicum supervisor in industry or an academic researcher and address a real-world data problem that exercises the skills developed in the program. Students will submit a proposal, weekly status reports, and a final paper and presentation. To receive credit the project must entail at least 115 hours of work and typically takes 8 weeks to complete. Prerequisites: DCSI5370 and completion of 9 semester hours of Emphasis Course work, or permission of the instructor.