Curriculum: Master of Science in Information Technology

Next Term Application Deadline

08.01.2025

Total Credits

Next Term Starts

08.25.2025

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Curriculum at a Glance

In today’s rapidly evolving world, companies demand qualified information technology professionals who can protect their data and create ingenious products. The courses at the University of Cincinnati emphasize the applied aspects of technology and are more focused on how to utilize IT solutions in the industry.

Our highly customizable program offers coursework in cybersecurity, IT infrastructure, mobile development, human-computer interaction, and more. Build the skills necessary to secure software applications, networks, and systems.

Our comprehensive curriculum prepares students for a fulfilling career in technology. Besides the 3 core classes required for all MS IT students, the curriculum is flexible to fit the goals of each individual. All students will select 15-18 credit hours of electives related to the fields they want to study. Students do have the ability to select up to 6 credits of non-IT elective coursework as there is a significant intersection between IT and fields such as Criminal Justice, Health Informatics, and Data Analytics.

Use your elective hours to earn a Certificate in Data-Driven Cybersecurity at no extra cost.

Capstone or Thesis Pathway

Students are given the option to select from either a capstone (IT final project) or thesis pathway, both of which allow students to systematically investigate an IT solution:

MS IT students pursuing the capstone track will complete their final project by taking a 3-credit course in their last semester.
MS IT thesis students partner closely with a faculty advisor on a 2-4 semester research project of publication quality, which is then later archived in the UC library.

Bridge Courses

Bridge Courses (12 credit hours) are required only for students who have an undergraduate GPA less than 3.0 or have no background in Information Technology. This 4-course sequence will provide incoming students with the background necessary to succeed in the MS IT program. As an added benefit, these courses can be used to receive a graduate-level Certificate in IT. Students should check their official offer letter to see which if any, bridge courses are required. Bridge courses must be completed with a B or above before beginning MS IT coursework. Bridge courses do not count towards the MS IT 30-hour requirement.

CURRICULUM AT A GLANCE

Bridge Certificate Core Courses Thesis Track Non-Thesis Track IT Electives Approved Electives Outside IT

*****Courses below may be required for students with low undergraduate GPA and/or no background in IT. Students should refer to their offer letter to determine which, if any, bridge courses are required.*****

12 credit hours

Course	Title / Description	Credit
IT6035C	Information Security and Assurance Course: IT6035C Credit: 3 An introduction to the various technical and administrative aspects of information security and assurance. This course provides the foundation for understanding the key issues associated with protecting information assets, determining the levels of protection and response to security incidents, and designing a consistent, reasonable information security system, with appropriate intrusion detection and reporting features.	3
IT6060C	Database Management with SQL Server Course: IT6060C Credit: 3 This is an introductory course to the technology of database design and implementation. Topics include, but are not limited to relational database design and implementation, query formulation with Structured Query Language, application development, etc. Enterprise database management system will be used.	3
IT6090C	Computer Programming with Java Course: IT6090C Credit: 3 This course introduces students to object-oriented computer programming and problem solving. Students will learn about the basic elements of a computer program such as data types, basic control structures, graphical user interface, event-driven programming, and program debugging. Hands-on active learning required.	3
IT6081C	System Administration Course: IT6081C Credit: 3 This course will provide the knowledge and hands-on skills necessary to manage a Local Area Network and its resources. Topics covered include directory services, server management, file and print services, and user/client administration in a heterogeneous operating system environment. Students will set up and manage a fully functioning computer network of systems. Hands-on active learning required.	3

All students in the MSIT program are required to take these 3 core classes.

9 credit hours

Course	Title / Description	Credit
IT7001	Information Technology Graduate Seminar Course: IT7001 Credit: 3 The IT graduate Seminar is designed to provide opportunities for professional development of graduate students, raise their awareness of various other issues that they may face in their professional careers, and provide opportunities to survey research seminars of their interest.	3
IT7010C	Information Technology Research Methods Course: IT7010C Credit: 3 This course focuses on scientific approaches to studying information technologies and writing thesis and other research reports.	3
IT7040C	Human Computer Interaction and Usability Course: IT7040C Credit: 3 This course is concerned with the design, evaluation, and implementation of interactive computing systems for human use and with the study of major phenomena surrounding them. The course considers the inherently multi- and interdisciplinary nature of HCI and situates various HCI issues in the organizational and societal contexts.	3

Students wishing to switch to thesis-track must identify a graduate faculty member to serve as an advisor for the thesis and then work with that faculty member to refine the thesis topic before completing Form A and turn the form into the SoIT Grad Office 2-3 semesters before graduating.

3 credit hours

Course	Title / Description	Credit
IT8020	Information Technology Thesis Research Course: IT8020 Credit: 3 Individual research under the supervision of IT faculty directs towards the completion of the MS thesis.	3

Students are automatically enrolled in the non-thesis track as this is the preferred track for students interested in a career as an IT professional. In this track, students are advised to take IT8010 IT Project no sooner than their final semester.

3 credit hours

Course	Title / Description	Credit
IT8010	Information Technology Project Course: IT8010 Credit: 3 Individual projects are under the supervision of program faculty for partial fulfillment of the Master of Science degree.	3

Any IT 7000+ level course

Minimum of 9 credit hours required

Course	Title / Description	Credit
IT7020C	Principles of Cybersecurity Course: IT7020C Credit: 3 This course introduces students to the field of cybersecurity by discussing the evolution of information security into cybersecurity, cybersecurity theory, and the relationship of cybersecurity to nations, businesses, society, and people. Students will be exposed to multiple cybersecurity technologies, processes, and procedures, learn how to analyze the threats, vulnerabilities and risks present in these environments, and develop appropriate strategies to mitigate potential cybersecurity problems.	3
IT7021C	Enterprise Security and Forensics Course: IT7021C Credit: 3 This course is designed to provide students with the advanced concepts needed to establish network security strategies to ensure adequate protection for the organization's environment and yet provide accessibility for its community.	3
IT7024C	Technologies for Mobile Applications Course: IT7024C Credit: 3 This class covers the technologies, devices, operating systems, and tools of mobile applications, as well as the mobile industry. Students will use tools to create apps for different mobile devices including smartphones and tablets. The scope of this course may be varied by the faculty.	3
IT7027C	IT Infrastructure Sustainability Course: IT7027C Credit: 3 This course introduces the planning, designing, and managing sustainable and resilient infrastructure systems and their interactions. It encompasses both built IT infrastructure and IT services that rely on integrated built and natural systems to provide corporate fundamental needs. The scope of this course may be varied by the faculty.	3
IT7028C	Advanced Storage Technologies Course: IT7028C Credit: 3 This course introduces storage technologies in an increasingly complex IT environment. It builds a strong understanding of advanced concepts in storage technologies. The course focuses on architectures, features and benefits of intelligent storage systems; networked storage technologies; long-term archiving solutions, the increasingly critical area of information security and the emerging field of storage virtualization technologies.	3
IT7029C	Advanced Systems Administration Course: IT7029C Credit: 3 Analyze and practice system administration processes for enterprise-level information systems. It includes advanced concepts in enterprise computing infrastructure analysis, deployment, management and troubleshooting. Topics include, but not limited, enterprise computing resource requirements analysis and design, application and server deployment, virtualization,security configurations, and performance analysis. The scope of this course may be varied by the faculty.	3
IT7030C	Games for Learning & Simulation Course: IT7030C Credit: 3 This course introduces students to the use of games to influence learning and simulation. Students will investigate and analyze different case studies. Students will use game technologies to apply learning theories to develop games for educational and simulation purposes.	3
IT7031C	Advanced Technologies for Game Development Course: IT7031C Credit: 3 This is an advanced course that explores differenttechnologies and platforms for the development of games. Students will compare different technologies and will select technologies to implement a full life cycle of game development. Students will collaboratively in groups using advanced team collaboration tools.	3
IT7071C	Machine Learning and Data Mining for IT Course: IT7071C Credit: 3 This course introduces machine learning and data mining techniques. The course focuses on using applied methods and software tools to discover hidden patterns or identify anomalies in the data generated in modern IT networks.	3
IT7075C	Data Driven Cybersecurity Course: IT7075C Credit: 3 In today enterprise networks the challenge of a cybersecurity analyst is not lack of data for analysis, but too many data from too many network sources. Visualizing and analyzing vast amount of data from cyber space is a critical skill for protecting data network. This course explores different types of network data from text-based log files to complex and proprietary binaries. Students will have hands-on practice using scripting languages and software tools to visualize and process network data and identify threats and malicious activities on networks. This course intends to convert cybersecurity challenges into data science challenges that can be solved using modern data science techniques such as modern data visualization and machine learning programming libraries.	3
IT8007	Social Network Analysis: Visualization and Hypothesis Testing Course: IT8007 Credit: 3 This is a graduate level course on the visualization and advanced analysis of social networks using computational tools. The course provides some basic foundational knowledge of the key theoretical concepts of social network analysis but is heavily on the methodological (e.g., how do we actually carry out research and test hypotheses on social networks) processes. The course begins with a definition of a social network and a review of key concepts from the underlying mathematical field of graph theory. The course proceeds to frame the field in terms of various research designs, data collection, and data management. After exploring research methods, the course moves on to multivariate techniques used in network analysis through dyadic concepts in network analysis and statistical techniques tailored to the special challenges of network data, such as non-independence of observations. Finally, The course covers types of data including affiliation matrices, large networks (Big Data), ego networks, and longitudinal data.	3

Some non-IT electives have enrollment restrictions. Registration approval is determined based on seat capacity, course offerings, etc. and approval is not guaranteed. Planning for an alternative elective is recommended.

A maximum of 6 credit hours

Course	Title / Description	Credit
CJ6012	Cybercrime Course: CJ6012 Credit: 3 This course is designed to provide master's level students with a broad introduction to the various types of criminal conduct associated with computers and the Internet. As a student in this class you will be exposed to techniques associatedwith digital forensics and will assess criminological theories of crime as they relate todigital crime and terrorism. Additionally, you will examine a number of the national and international laws and policies related to cybercrime including the diverse steps that have been taken to increase digital security around theglobe. Familiarity with computers and the Internetwill help you progress through the course, but expertise is not required nor expected.	3
CJ7070	Theory and Practice of Crime Prevention Course: CJ7070 Credit: 3 This course is designed to provide an exploration of the various approaches to reducing crime as well as the theories that inform those approaches. We will focus most fully on situational approaches to crime prevention, though we will also explore crime prevention through social development, community-based crime prevention. We will also examine how policing intersects with these various approaches to crime prevention.	3
CS6033	Artificial Intelligence Course: CS6033 Credit: 3 The course will cover in detail the topics of state space search, game tree search, constraint satisfaction, logic based knowledge representation and reasoning, first order predicate calculus, uncertainty handling using Bayesian probability theory, and some applications of these techniques.Applications may be selected from the area s of automated planning, natural language processing, or machine learning.	3
CS6037	Machine Learning Course: CS6037 Credit: 3 The goal of this course is to introduce students to the field of Machine Learning. The course covers traditional machine learning algorithms, and their implementations along with discussions of concrete problems where these algorithms are suitable. Topics covered by course include: Concept Learning and the General-to-Specific Ordering Decision Tree Learning Artificial Neural Networks Evaluating Hypotheses/Bayesian Learning Computational Learning Theory Instance-Based Learning Genetic Algorithms Learning Sets of Rules Analytical Learning Combining Inductive and Analytical Learning Reinforcement Learning.	3
CS6052	Intelligent Data Analysis Course: CS6052 Credit: 3 This course will introduce students to the theoretical and practical aspects of the field of data mining. Algorithms for data mining will be covered and their relationships with statistics, mathematics, and algorithm design foundations will be explored in detail.	3
CS6053	Network Security Course: CS6053 Credit: 3 Treats current concerns, trends, and techniques to ensure security and safety of data on computers and over networks. There are three parts: 1. Basictools and assembly: Secret Key and Public Key block ciphers such as DES, 3DES, AES, RSA, Diffie-Hellman Key Exchange, zero-knowledge authentication, and Elliptic Curve Cryptography; hash algorithms such as SHA variants; stream ciphers such as RC4 variants; message integrity and authentication algorithms such as HMAC. Output Feedback Mode, One-time Pads, Cipher Block Chaining are discussed as the means to put many of these algorithms to practical use; 2. Systems using these tools: Kerberos, IPSec, Internet Key Exchange, SSL, PGP, Email Security. Certification authorities, certificates, and key distribution centers to support these systems. Vulnerabilities in protocols specified for these systems and ways they can be fixed; 3. Well known attacks and how to prevent them. This includes denial of service, side-channel, attacks that exploit existing network IP and TCP protocols, offline and online password attacks, stateless cookies. Students will form teams of three to produce systems written in Java that will compete in a contest.	3
CS6054	Information Retrieval Course: CS6054 Credit: 3 This is an introductory course to the field of information retrieval at the senior undergraduate and beginning graduate level. Topics include bag-of-words model and term frequency matrix, tf-idf vector space representation and cosine similarity, vector space-based and graph-theoretical ranking and clustering, latent semantics and latent topic models. Four programming projects with real-world document collections for indexing, ranking, and clustering are designed for both undergraduate and graduate students and an additional project is required for graduate students. This course also covers necessary mathematics in Bayesian statistics and machine learning.	3
CS6056	Security Vulnerability Assessment Course: CS6056 Credit: 3 1. Legals issues associated with disclosure of security vulnerabilities 2. Software and Operating System vulnerability 3. Software and Operating System vulnerability 4. Software and Operating System design and implementation 5. Language (mainly C) design issues 6. Network and protocol vulnerabilities 7. Network and protocol vulnerabilities 8. Network attacks 9. Intrusion and anomaly detection and prevention 10. Hardware and architecture vulnerabilities and attacks 11. Configuration vulnerabilities 12. User interfaces and human factors 13. Application security and detection of malfeasance	3
CS7081	Advanced Algorithms I Course: CS7081 Credit: 3 Advanced treatment of fundamental topics in algorithms that every graduate student should know and have some sophistication in. Knowledge and ability to apply the fundamental design strategies: the greedy method, divide-and-conquer,dynamic programming, to solve important problems in data encryption, efficient polynomial, integer,matrix multiplication, computing the Discrete Fourier transform, using the celebrated FFT algorithm, and so forth. In addition this course will introduce students to lower bound theory and NP-completeness.	3
HI7010	Health Informatics, Information Systems and Technology Course: HI7010 Credit: 3 This course introduces the discipline of health informatics and covers emerging trends. Various information systems, technologies and applications utilized in the context of health and healthcare are introduced. Their characteristics, strengths, challenges, purpose and impact are taught. Impact on patients, populations and healthcare providers is emphasized. Factors influencing adoption and use of various clinical and health information systems and technologies are taught. Key information technologies and systems such as electronic health records, health information exchanges, personal health records, public health information systems and mobile health technologies are introduced in this course. Topics such as telemedicine, interoperability and technical concepts are taught, and evaluation framework is introduced.	3
HI7071	Introduction to Healthcare Data Science Course: HI7071 Credit: 2 This course introduces the student to a variety of statistical methods, study design, and programming as essential skills in data science. Students practice techniques such as data cleaning, data wrangling, data exploration, analysis, visualization, and interpretation. Students use a variety of healthcare datasets in this course and are also prepared to discuss healthcare data standards and measures, best practices in data management, and trends in healthcare data science and management.	2
HI7072	Leveraging Analytics and Business Intelligence Tools for Healthcare Course: HI7072 Credit: 3 This course will introduce students to a variety of cutting edge analytics and business intelligence tools applicable to health or healthcare data. Both structured and unstructured data will be introduced in this course. The coursewill also address topics related to data governance and data quality and various other topics relevant to health data management. This course is predominately hands-on and students willcomplete a project to demonstrate skills acquired.Students will learn how other industries have applied similar or the same tools.	3
IS7034	Data Warehousing and Business Intelligence Course: IS7034 Credit: 2 This course is designed for the comprehensive learning of data warehousing technology for business intelligence. Data warehouses are used to store (archive) data from operational information systems. Data warehouses are useful in generating valuable control and decision-support business intelligence for many organizations in adjusting to their competitive business environment. This course will introduce students to the design, development and operation of data warehouses. Students will apply and integrate the data warehousing and business intelligence knowledge learned in this course in leading software packages.	2
BANA7046	Data Mining I Course: BANA7046 Credit: 2 This is a course in statistical data mining with emphasis on hands-on case study experiences using various data mining/machine learning methods and major software packages to analyze complex real world data. Topics include data preprocessing, k-nearest neighbors, generalized linear regression, subset and LASSO variable selection, model evaluation, cross validation, classification and regression trees.	2
BANA7047	Data Mining II Course: BANA7047 Credit: 2 This is a course in statistical data mining with emphasis on hands-on case study experiences using various data mining/machine learning methods and major software packages to analyze complex real world data. Topics include advanced trees: bagging, random forests, boosting; nonparametric smoothing methods; generalized additive models; data preprocessing/scaling; neural networks; deep learning; cluster analysis; association rules.	2
BANA6043	Statistical Computing Course: BANA6043 Credit: 2 This is a course on the use of computer tools for data management and analysis. The focus is on a few popular data management and statistical software packages such as SQL, SAS, SPSS, S Plus, R, and JMP although others may be considered. Data management and manipulation techniques including queries in SQL will be covered. Elementary analyses may include measures of location and spread, correlation, detection of outliers, table creation, graphical displays, comparison of groups, as well as specialized analyses.	2
BANA7038	Data Analysis Methods Course: BANA7038 Credit: 2 This course covers the fundamental concepts of applied data analysis methods. Various aspects of linear and logistic regression models are introduced, with emphasis on real data applications. Students are required to analyze data using major statistical software packages. BANA 7038 should not be taken for credit by MS-Business Analytics students.	2
IDT8020	Learning Sciences and Technology Course: IDT8020 Credit: 3 This course provides a fundamental understanding of learning sciences, an interdisciplinary field dedicated to studying learning and how learning may be facilitated in designed environments. Students will design a technology-based learning environment and conduct an evaluation of the embedded theories in an existing environment of their choice, such as digital media, games, or other innovative technologies.	3
EDST7010	Statistical Data Analysis I Course: EDST7010 Credit: 3 This course introduces students to the fundamental techniques of statistical data analysis that are commonly used in the social and behavioral sciences, such as descriptive statistics and data visualization; fundamental methods of inferential statistics, such as basic hypothesis testing, t-tests, and ANOVA; and the use of statistical software to support data analysis.	3
PH7010	Biostatistics for Public Health Course: PH7010 Credit: 3 Public health workers need to read and interpret public health and medical literature to keep abreast of the latest methods. This course will provide an introduction of basic concepts of statistics, methods of statistical analysis, and tools of statistical computation. The goal is to help students understand the language of statistics and the art of statistical investigation; perform basic statistical analysis of their own research; and read and evaluate analytical results in health and research articles.	3
PH7011	Statistical Computation and Software Course: PH7011 Credit: 1 Statistical computation is an essential part of modern statistical analysis. Many times common statistical models or tests can be performed using codes or procedures from a statistical software package. This course is designed to introduce three commonly used statistical software packages, SAS (including SAS/STAT, SAS/BASE, and SAS Enterprise), SPSS, and R. The goal is to provide basic knowledge of these software packages to users and help them understand how to acquire, input data from digital or hard copy data sources;inspect and manipulate the data in order to meet the requirements for statistical models as well as their computational procedures; perform analyses using right codes and procedures for specific models and tests; and interpret and present findings using outputs from the computation. After taking this course, students will have better understanding of pros and cons of different software packages and know how to use them to cross validate the outputs in order to appropriate results for certain analyses.	1
BE7061	Biostatistics in Research Course: BE7061 Credit: 3 Assessment of diagnostic tests vis-a-vis gold standard procedures. Quantitative markers and ROC (Receiver Operating Characteristic) curves. One-sample and two-sample t-tests. Non-parametric analogues. One-sample and two-sample proportions. Contingency tables and chi-squared tests. Odds andodds ratios. Analysis of Variance. Kruskal-Wallis test. Confidence intervals. Multiple comparisons. Sample size calculations. R software. Descriptive statistics using R. Graphics using R. Data analysis using R. Binary logistic Regression. Model based odds ratios. Conditional logistic regression for matched case-control studies. Multinomial logistic regression. Proportional oddsmodel. Poisson regression models. Multiple regression models and interactions.	3

FAQs

Is an MS IT the same as a master’s in computer science?

Although both computer science and IT degrees involve working with computers and software, and require a strong understanding of technology and its applications, they differ in their approach. Computer science is more theoretical and focuses on abstract concepts, while IT is more hands-on and applied in nature. Both degrees can lead to lucrative careers in the tech industry.
Is getting an master’s in information technology worth it?

Earning a Master of Science in Information Technology can equip you with up-to-date and relevant technology skills and knowledge. Pursuing an MS in IT can be an excellent way to advance your career and increase your earning potential. If the degree aligns with your interests, background, and career goals, then investing time and money to earn it can be worthwhile. According to the US Bureau of Labor Statistics, computer and information technology fields are expected to grow at a rate of 15% in the next ten years, and the median salary is $100,530, which is almost double the annual wages for all occupations.
What is the difference between an MS IT and a Master’s in Information Systems (IS) degree?

The Master of Science in Information Technology (MS IT) degree emphasizes the practical application of technology, while a Master’s in Information Systems (IS) Management focuses on the strategic use of information systems to support business objectives. Although both degrees cover similar topics, the MS IT degree has a stronger technical focus, while the IS degree has a stronger business focus.