The MS in data science online courses focus on two aspects of the field: using theoretical models in the collection, design, and analysis of quantitative and qualitative data, and the real-world process of creating and implementing solutions based on that data.
This 30-credit-hour program requires 15 credit hours of core courses, 12 credit hours in a specialization and a 3-credit-hour capstone project. The program is designed so it can be completed in as few as five continuous semesters of study (about two years).
Core Data Science Classes
DSC 501 Introduction to Data Science (3 credit hours)
DSC 501 serves as the introductory course to the program. What is data? What is a data scientist? Coursework quickly moves to the data analysis process (CRISP-DM, KDD). Probability and statistics are covered as an introduction to DSC 503: Statistical Methods. Students explore and analyze data with Excel, SPSS and other data analysis tools like Alteryx Designer. Bias, ethics and social responsivity are covered as we discuss contemporary issues in data science.
DSC 503 Statistical Methods (3 credit hours)
Data scientists are often expected to act as experts on statistical knowledge. This class enables the understanding the mathematical and conceptual foundations of data analysis using SPSS and Alteryx Designer will be utilized. Students will utilize datasets as they explore data and employ analysis of variance (ANOVA), regression, and Bayesian inference. DSC 503 is designed to follow and build upon DSC 501.
DSC 607 Data Mining (3 credit hours)
Data is the key ingredient for data analysis, and students will apply the data analysis process to explore and analyze data. Students in DSC 607 will learn about different types of data and requirements for clean and relatable data, and will reflect on the bias, ethical use and social responsibility of the data. Students will derive quantitative and qualitative findings and knowledge from data. Software utilized for data analysis are Excel, SPSS, and Alteryx Designer. R/R Studio is introduced.
DSC 609 Machine Learning (3 credit hours)
In DSC 609 students utilize the data mining skills from DSC 607 to develop applications to enable machine learning, sometimes called artificial intelligence. Students will expand their skillset with SPSS and Alteryx Designer. Python is introduced.
DSC 611 Data Visualization (3 credit hours)
Data scientists generate almost all value-added utility for an organization when they convey meaning to stakeholders. Data visualization explores the mechanisms to effectively communicate findings using Tableau, Excel, and PowerPoint to tell “data’s story.” DSC 611 is the final core course before students can take specialization courses.
Culminating Experience (choose one)
DSC 680 Capstone (3 credit hours)
The capstone project will apply all aspects of the student’s curriculum on a project or practicum approved by the Program Director and mentored by a faculty member.
DSC 690: Thesis (3 credit hours)
The thesis provides students the option to complete a research thesis under the guidance of a committee. The research must be original in nature and approved by the committee. This option is for students who have special interests or foresee continuing their education with a doctorate.
Business Analytics Specialization Courses
ECN 610 Managerial Economics (3 credits)
This course covers economic forces and how they relate to profitability and growth of a firm and to economic thinking. Topics include principles of microeconomics and how they apply to managerial decision-making.
FIN 601 Advanced Financial Management (3 credits)
Students learn about corporate financial analysis, working capital management, and capital budgeting issues. Coursework includes financial models and tools used to inform management about the long-term viability of the firm and to discover financial fraud.
MGT 610 Core Topics in Management (3 credits)
Coursework covers strategy and analysis of the value chain, macroeconomic issues in business, monetary theory and financial institutions, and financial statement analysis.
BUS 621 Financial Fluency I (1.5 credits)
This is Part I of a course that helps prospective leaders understand the application of accounting and finance concepts related to the interpretation and application of financial data to decision making.
BUS 622 Financial Fluency II (1.5 credits)
In Part II of the course, students learn about accounting and finance concepts related to the interpretation and application of financial data to decision making. Prerequisite: BUS 621.
Cybersecurity Specialization Courses
CYB 605 Principles of Cybersecurity (3 credits)
This course provides an advanced view of cybersecurity. Students review the impact of cybersecurity on institutions, privacy, business and government applications, and examine the dimensions of networks, protocols, operating systems and associated applications.
CYB 606 Cyberspace and Cybersecurity
Coursework develops a foundational view of cybersecurity, with a high-level overview of the technical, operational, ethical and legal issues associated with the topic.
CYB 610 Cyber Intelligence (3 credits)
This course provides an overview of the history and evolution of cyber intelligence, covering areas that include passive and active measures, principles and processes, ethics and the evaluation of successes and failures.
CYB 615 Cyber Counterintelligence (3 credits)
This course provides an overview of the history and evolution of cyber counterintelligence, covering similar areas as CYB 610 but from a counterintelligence standpoint.
CYB 671 Open Source Cyber Surveillance (3 credits)
Students explore the responsible, legal and ethical use of data and information gathered from the use of unmanned, semiautonomous systems, Web data mining, social networks and other modern cyber systems.
CYB 674 Cyber Data Fusion (3 credits)
This course will explore collection, fusion, integration and analysis problems selected from the following advanced surveillance technologies: acoustic, electromagnetic, sensors, special (magnetic, cryptologic, and computers) and human networks.
Financial Crime Specialization Courses
FCM 626 Financial Investigations* (3 credits)
Students learn about financial crime in the context of business operations; methods of detection; and methods of investigation, including analysis of financial documents, and the investigation process and techniques; and preparation of investigative case reports.
FCM 642 Advanced Fraud Analysis* (3 credits)
This course is designed to familiarize students with innovative analytic approaches used to perform complex fraud analysis. Topics include link analysis, data mining, advanced statistical tools, case management systems and expert system approaches such as neural network early-warning software.
BUS 631 Data-driven Decision-making I (1.5 credits)
In Part I of this course, students develop critical thinking and problem analysis skills for executive decision-making. Coursework addresses the use and analysis of quantitative data. Prior knowledge of introductory statistics and functional use of Excel are required.
BUS 632 Data-driven Decision-making II (1.5 credits)
In Part II, students delve deeper into critical thinking and problem analysis for executive decision-making and data-context relevance. Topics include the use and analysis of qualitative data. Functional use of Excel is required.
FCM 601 Proseminar in Financial Crime and Compliance Management (3 credits)
This course focuses on four thematic areas: management economic crime, technology and analytical skills. Background knowledge will be provided to prepare students for in-depth coursework in these areas. Students will be exposed to the learning and communications skills necessary to succeed in an independent study degree program.
*FCM 626 and 642 residency requirements waived with instructor's approval, for DSC students who complete DSC 501, 503, 607, and 609 courses before taking these courses.
Social Science Analytics Specialization Courses
ECN 575 Behavioral Economics (3 credit hours)
Behavioral economics questions the underlying assumptions of classical economics and incorporates theory from psychology, sociology, and other social sciences to better understand and predict how people make decisions. These insights, together with data collected from experiments, are used to develop strategies to address individual, business, organizational, and societal problems.
SOC 555 Community and Social Change (3 credit hours)
Analysis of challenges and opportunities facing American communities. Emphasis on communities in New York State.
SOC 563 Complex Organizations (3 credit hours)
The purpose of this course is to study complex organizations in modern society from the perspective of how those organizations make decisions. Students will explore organizations in the government, the military, business, health care and education in both the profit and non-profit sectors.
SOC 565 Demography (3 credit hours)
Demography is the study of human population size, growth, density and change. The major focus of the course is on fertility, mortality and migration patterns at the national level. A comparative perspective allows analysis of various demographic variables.
Get more details about the online MS in Data Science at Utica College. Request more information or call us today at 315.732.2640 or toll-free at 866.295.3106 to speak with a program manager.