Online Master’s in Data Science Curriculum

Fundamental Courses (3 credits each)

CSCI 610: Theoretical Concepts in Computers and Computation

This course will introduce basic programming concepts (i.e., in Python and R), and techniques including data structures (vector, matrix, list, data frame, factor), basic and common operations/concepts (indexing, vectorization, split, subset), data input and output, control structures and functions. Other topics will include string operations (stringr package) and data manipulation techniques (dplyr, reshape2 packages). The course will also explore data mining, such as probability basics/data exploration, clustering, regression, classification, graphics, and debugging.

DTSC 615: Optimization Methods for Data Science

Basic concepts in optimization are introduced. Linear optimization (linear and integer programming) will be introduced including solution methods like simplex and the sensitivity analysis with applications to transportation, network optimization, and task assignments. Unconstrained and constrained non-linear optimization will be studied and solution methods using tools like Matlab/Excel will be discussed. Extensions to game theory and computational methods to solve static, dynamic games will be provided. Decision theory algorithms and statistical data analysis tools (Z-test, t-test, F-test, Bayesian algorithms and Neyman Pearson methods) will be studied. Linear and non-linear regression techniques will be explored.

DTSC 620: Statistics for Data Science

This course presents a range of methods in descriptive statistics, frequentist statistics, Bayesian statistics, hypothesis testing, and regression analysis. Topics includes point estimation, confidence interval estimation, nonparametric model estimation, parametric model estimation, Bayesian parametric models, Bayesian estimators, parametric testing, nonparametric testing, simple and multiple linear regression models, logistic regression model.

DTSC 701: Introduction to Big Data

Prerequisite: DTSC 610

This course provides an overview of big data applications ranging from data acquisition, storage, management, transfer, to analytics, with focus on the state-of-the-art technologies, tools, and platforms that constitute big-data computing solutions. Real-life big data applications and workflows are introduced as well as use cases to illustrate the development, deployment, and execution of a wide spectrum of emerging big-data solutions.

DTSC 710: Machine Learning

Prerequisite: DTSC 615

In this course, students will learn important machine learning (ML) and data mining concepts and algorithms. Emphasis is on basic ideas and intuitions behind ML methods and their applications in activity recognition, and anomaly detection. This course will cover core ML topics such as classification, clustering, feature selection, Bayesian networks, and feature extraction. Classroom teaching will be augmented with experiments

Electives (3 credits each, select 4 of the following)

Advanced Data Science Electives

CSCI 657: Introduction to Data Mining

This course introduces the concepts, techniques, and applications of data mining. Topics include data preprocessing, clustering, data warehouse and Online Analytical Processing (OLAP) technology, cluster and social network analysis, data classification and prediction, multimedia and web mining.

DTSC 740: Deep Learning

Prerequisites: DTSC 620, DTSC 710

This course presents a range of topics from basic neural networks, convolutional and recurrent network structures, deep unsupervised and reinforcement learning, and applications to problem domains like speech recognition and computer vision.

DTSC 630: Data Visualization

This course is designed to provide an introduction to the fundamental principles of designing and building effective data visualizations. Students will learn about data visualization principles rooted in graphic design, psychology and cognitive science, and how to use these principles in conjunction with state-of-the-art technology to create effective visualizations for any domain. Students who have taken this course will not only understand the current state-of-the-art in data visualization but they will be capable of extending it.

Cybersecurity Specialization Electives

CSCI 654: Principles of Information Security

In this course, students will study the issues involved in structuring information systems to meet enterprise requirements including security and public policy regulations. Topics include the building blocks of an information system, emphasizing the security and administration aspects of each, as well as life-cycle considerations, and risk management. The course will also include a special project or paper as required and specified by the instructor and the SoECS graduate committee.

CSCI 662: Information System Security Engineering and Admin

This course introduces students to a range of contemporary, applications oriented, and advanced technical aspects of information security and assurance. Topics covered in this course are: the need and planning for security, information security maintenance, security technology, cryptography, and physical security. The course will also cover security policies, and legal and ethical issues. The course will also include a special project or paper as required and specified by the instructor and the SoECS graduate committee.

INCS 615: Network Security and Perimeter Protection

In this course, students are introduced to the design of secure computer networks. Exploitation of weaknesses in the design of network infrastructure and security flaws in network protocols are presented and discussed. Network operation systems and network architectures are reviewed, together with the respective security related issues. Issues related to the security of content and applications such as emails, DNS, web servers are also addressed. Security techniques including intrusion detection, forensics, cryptography, authentication, and access control are analyzed. Security issues in IPSEC, SSL/ TLS, and the SSH protocol are presented.

Students must choose either Thesis Track or Non-Thesis/Project Track (below)

Thesis Track (3 credits each)

DTSC 890: MS Thesis I
Consult with the program chair/program advisor on your thesis.

DTSC 891: MS Thesis II
Consult with the program chair/program advisor on your thesis.

Consult with the program chair/program advisor on any electives.

Non-Thesis/Project Track (3 credits each)

DTSC 870: MS Project I

In this course, students carry out independent research in a significant technical area of data science. The student is to investigate a technical area, research it, advance it in some way if possible, and report on the learning and advancements made. A written report is required that summarizes the findings and any advancements made to the technology.

Consult with the program chair/program advisor on any electives to complete the project track.

Total Credits: 30

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Admissions Deadlines

Early Decision Deadline
May 12
Fall 2023
Priority Deadline
July 14
Fall 2023
Final Deadline
August 11
Fall 2023
Start Date
September 6
Fall 2023