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Data Science and Analytics, BS Courses

The following are courses in the Data Science & Analytics, BS program.

Required Courses

DA 301: Introduction to Data Science I

3 Credits
As data becomes increasingly important to the modern world, there is an increasing need for scientists educated in the process of collecting, coding, cleaning, analyzing, and presenting those data. This course will introduce you to the basic concepts of data analysis; the uses of data in a variety of fields; how to ask research questions; the process of identifying data sources; data collection, cleaning, coding, and manipulation; and how to present data. At the conclusion of the course, you will have a firm foundation to begin working with a variety of data in different scenarios.

DA 302: Introduction to Data Science II

3 Credits
Building from the concepts introduced in Introduction to Data Science I, this course will provide you with the opportunity to develop more advanced programming skills. You will learn the basics of creating visualizations in R, how to perform different data wrangling procedures, and how to work with relational databases and different types of variables. You will also develop skills needed to program, model, and use R to communicate findings from the data you analyze. At the end of the class, you will be prepared to work on projects using the R software and will have a foundation for learning even more advanced programming in R.

DA 303: Calculus

3 Credits
Course description coming soon.

DA 304: Programming in R

3 Credits
Course description coming soon.

DA 305: Data Visualization

3 Credits
Course description coming soon.

DA 306: Techniques for Business Data Analytics

3 Credits
Course description coming soon.

DA 307: Database Design

3 Credits
Course description coming soon.

DA 401: Data Structure/Algorithms

3 Credits
Algorithms are an essential tool in data analysis. Additionally, for data analysis to be conducted, the data should be structured in an efficient way. This course covers the essential elements of creating algorithms and the methods used to structure data. You will learn about growth functions, divide and conquer strategies, probabilistic analyses, sorting, basic data structures, hash tables, search trees, and dynamic programming. Throughout the course, you will apply topics covered using both real and simulated data.

DA 402: Regression Analysis

3 Credits
Course description coming soon.

DA 403: Design and Analysis of Experiments

3 Credits
Course description coming soon.

DA 405: Data Mining

3 Credits
Course description coming soon.

DA 406: Data Governance

3 Credits
Course description coming soon.

DA 407: Advanced Statistics/Statistical Modeling

3 Credits
Course description coming soon.

DA 408: Modeling and Predictive Analysis

3 Credits
Course description coming soon.

DA 450: Data Science Capstone

3 Credits
Course description coming soon.

Elective Courses

DA 308: Introduction to Epidemiology

3 Credits
Epidemiology is the foundational science of public health. Epidemiological research helps us understand how diseases occur in certain groups of people and why, in order to identify the determinants of health at the population level. In this course, you will be introduced to the basics of epidemiology. Topics covered will include measures of disease occurrence; measures of association; study designs; the roles of bias, random error, confounding, and effect modification in epidemiological studies; and screening for disease. At the conclusion of the class, you will be able to read and interpret epidemiological studies and explain how an epidemiological study could be designed to answer a particular health question.

DA 309: Introduction to Biostatistics

3 Credits
Biostatistics is a central field for understanding and analyzing health and clinical data. This course will introduce you to the basics of data analysis for health studies. Specific topics will include probability, error, descriptive statistics, confidence intervals, hypothesis testing, power size calculations, and different statistical models. At the conclusion of the course, you will be prepared for learning more advanced biostatistical skills and conducting analyses of health data.

DA 310: Use of Big Data

3 Credits
Course description coming soon.

DA 311: SAS Programming and Data Analysis

3 Credits
Course description coming soon.

DA 313: Programming with Python

3 Credits
Course description coming soon.

DA 314: Survival Analysis

3 Credits
Course description coming soon.

DA 315: Applied Multivariate Analysis

3 Credits
Course description coming soon.

DA 316: Categorical Data Analysis

3 Credits
Course description coming soon.

DA 317: Mathematical Modeling

3 Credits
Course description coming soon.

DA 318: Time Series Data and Forecasting

3 Credits
Course description coming soon.

DA 319: Stochastic Modeling

3 Credits
Course description coming soon.

DA 320: Numerical Analysis

3 Credits
Course description coming soon.

DA 321: Applications in Information Security

3 Credits
Course description coming soon.

DA 410: Artificial Intelligence

3 Credits
Course description coming soon.

DA 412: Advanced SAS Programming

3 Credits
Course description coming soon.

DA 413: Statistics for Clinical Trials

3 Credits
Course description coming soon.

DA 414: Applied Machine Learning

3 Credits
Course description coming soon.