Courses

These courses satisfy requirements in the Master of Science in Data Science program. All courses assume that students satisfy the program prerequisites. The program outcomes are explicitly supported by these courses. Each course indicates at the end of its description which program outcomes are learning outcomes for the course. For example, (ABG) would indicate that the course supports outcomes A, B, and G from the list below. Program learning outcomes are listed below.

MATH 527 MATH 546 MATH 547 CPSC 507 NURS 670
CPSC 529 ENWR 517 BUAD 546 DSCI 501 DSCI 502
DSCI 511 DSCI 525 DSCI 612 DSCI 595 DSCI 599

Software & Programming Languages

The following software programs and programming languages will be utilized throughout the program curriculum:

 

MATH 527 Applied linear algebra (3 credits)
An application-focused approach to linear algebra in a variety of fields. Topics include matrices, gaussian elimination, vector spaces, determinants, inner products, orthogonality, least squares solution, eigenvalue problems, Gram-Schmidt process, matrix decomposition/factorization, methods of dimension reduction such as singular value decomposition or principal component analysis, quadratic forms, pseudo-inverses, Markov processes, data/image processing, and other advanced topics pertinent to data analysis. (BCF)

MATH 546 Applied statistics I (3 credits)
An introduction to the foundations and applications of statistics. Topics include basic concepts of data collection sampling and experimental design, descriptive analysis and graphical displays of data, probability concepts and expectations, normal and binomial distributions, sampling distributions and the Central Limit Theorem, confidence intervals and hypothesis testing, likelihood-based statistics, ANOVA, correlation and simple linear regression. (CF)

MATH 547 Applied statistics II (3 credits)
An application-focused approach to regression analysis and related techniques. Topics include simple and multiple linear regression, weighted and generalized least squares estimators, polynomial regression, exponential regression, model selection, categorical variables and ANOVA, logistic regression, principal component analysis, time series analysis, and other applications of statistics as relevant. Prerequisite: MATH 546 (BCF)

CPSC 507 Algorithms and data structures (3 credits)
A problem-solving approach to learning computer programming. Topics include variables, data types, conditional statements, loops, arrays, recursion, principles of software engineering, object-oriented programming, data structures, algorithms, and the use of standard libraries available in a variety of programming languages. The course will use commercially common programming languages and integrated development environments (IDEs). (BG)

NURS 670 Data analytics and outcomes improvement (3 credits)*
This course is designed to provide the DNP student with an opportunity to examine the lifecycle of data and the use of data analytics to measure healthcare delivery and improve patient outcomes. Transformation of healthcare outcomes that arise from changes in health care delivery systems will be driven by insights from existing large data sets that optimize clinical, financial, operational, and behavioral perspectives. Students will examine the process by which the DNP gains insight from data and the role of analytics in supporting a data-driven healthcare system as a component of healthcare reform.  Students will explore the application of data to value-based innovation projects that maximize the use of data for quality improvement, cost effective, and sustainable change in healthcare delivery systems. The use of the Internet in healthcare settings, ethical and legal issues associated with working with large data sets, and the focus on the individual patient as the center of evidence-based practice in nursing are emphasized.  *Note:  This course is part of the DNP program, but is also offered for data science students.

CPSC 529 Database systems (3 credits)
Basic concepts of databases. Topics include conceptual data modeling, database design and normalization, and database implementation. Use of SQL for data definition, manipulation, and query processing. While primary emphasis will be on the relational model and traditional RDBMS, discussion will also include a survey of techniques for handling non-relational data models, massive datasets, and unstructured data, including data warehousing, in-memory databases, NewSQL, NoSQL, and Hadoop. (ABG)

ENWR 517 Professional and technical writing (3 credits)
To be effective, data scientists and professional writers alike must assess their audience's information needs and develop strategies to meet those needs, so there is a seamless continuity between the analysis of data and communication about that analysis. This course engages with the full range of written, visual, and verbal communication, formal and informal that surrounds and enables data analysis in the professional workplace. Students will engage in collaborative writing exercises, grant proposals, and more. (EGH)

BUAD 546 Project management (3 credits)
The course develops the competencies and skills for planning and controlling projects and understanding interpersonal issues that drive successful project outcomes. Focusing on the introduction of new products and processes, it examines the project management life cycle, defining project parameters, matrix management challenges, effective project management tools and techniques, and the role of a project manager. (CGH)

DSCI 501 Data mining (3 credits)
This course is about mining knowledge from data in order to gain useful insights and predictions. From theory to practice, this course investigates all stages of the knowledge discovery process, which includes data preprocessing, exploratory data analysis, prediction and discovery through regression and classification, clustering, association analysis, and anomaly detection, and postprocessing.  (CDEF)

DSCI 502 Data mining at scale (3 credits)
A second semester of data mining introducing tools and techniques necessary for mining large scale data sources. Prerequisite: DSCI 501 (ABCD)

DSCI 511 Data preprocessing and visualization (3 credits)
This course is an introduction to data visualization. It includes data preprocessing and focuses on specific tools and techniques necessary to visualize complex data. Data visualization topics covered include design principles, perception, color, statistical graphs, maps, trees and networks, data visualization tools, and other topics as appropriate. Visualization tools may include JavaScript D3 library, Python, and R, and commercially available software such as Tableau, etc. The course introduces the techniques necessary to successfully implement visualization projects using the programming languages studied.  (ADE)

DSCI 525 Research methods (3 credits)
An introduction to basic scientific and statistical research methods when dealing with measurements of human and corporate activity. Students read and evaluate current research and translate their ideas into viable research projects. Topics include scholarly writing and presentation, descriptive research methods, quasi-experimental and experimental design, ethical issues, and analytical methods. (ACD)

DSCI 612 Entrepreneurship for Data Scientists (0 credits)
This course gives the student an opportunity to transform innovative data-centered concepts and ideas into concrete novel and value-adding products and services and introduces the complex entrepreneurial skills necessary to bring these innovations to market. Special emphasis will be placed on business best practices in the areas of product development, organizational management, marketing, strategic, and financial planning allowing for ongoing business success.  In this project-centered class the student will create a business plan for a profit-driven or social entrepreneurship venture promoting their skills as data scientists, which they can then use to secure start-up funds.  (This course is a prerequisite for applying for the Data Science Entrepreneurship Grant.)

DSCI 595 Thesis (1-3 credits)
Thesis credit may be earned for significant work toward the writing of a master’s thesis. This thesis may be used to fulfill the culminating project requirement. (C)

DSCI 599 Practicum (1-6 credits)
The practicum is an opportunity to directly experience the work of a data scientist or data analytics professional. It consists of project-based learning on a significant and contributory business objective in conjunction with practicing professionals in one of many appropriate industries. (Repeatable for up to a total of 6 credits.) (H)

Progression Through the Degree
This program does not strictly follow a cohort model, although some parts of the program may be arranged by cohort. Many of the courses will be offered once per two-year period. This means that students entering the program in different years will necessarily take certain courses together. However, it is reasonable to expect that some students in the program would not complete the program in two years simply due to work obligations.


Program Learning Outcomes
The Master of Science in Data Science program is committed to providing graduates with the range and depth of expertise to be leaders in data-driven industries. Students who successfully complete the program will be challenged to demonstrate high levels of mathematical, analytical, technical, and professional skill and knowledge. As such, these are the core outcomes of the program. 

  1. The graduate analyzes very large, complex data sets in the context of real-world business problems.
  2. The graduate applies and fine-tunes computing resources for data analysis, including programming and industry-standard tool use.
  3. The graduate develops and implements data analysis strategies based on theoretical principles, ethical considerations, and detailed knowledge of the underlying data.
  4. The graduate generates actionable intelligence for decision making.
  5. The graduate clearly and professionally communicates nuanced analysis results to a diverse, varyingly technical audience.
  6. The graduate rigorously applies mathematical principles to the analysis of data.
  7. The graduate evaluates, implements, and assesses the application of technology solutions for data analysis.
  8. The graduate plans, directs, and evaluates the status of complex projects.