Online Master of Science in Information Technology
The University of Cincinnati’s approach to information technology is built around three core principles: hands-on technical skills, problem-solving skills, and communication skills. Our students graduate with an understanding of technology, but more importantly, they understand people and organizations that allow them to create solutions that enhance lives.
Master of Science in Information Technology Program Overview
Our Master’s in Information Technology is designed as a flexible space for IT professionals to expand their knowledge. Whether you are just graduating with a Bachelor’s in IT, worked in IT for years, or are looking to change career paths, UC’s online MS IT program can help.
Learning about new technology and how it can solve the world’s most pressing issues is a driving force for those pursuing a Master of Science in Information Technology degree.
Students in the MS IT program are required to complete the 3 core IT classes that give you the skills needed to be successful in a future information technology career. These core courses will also provide you an overview of the IT landscape so that you can better choose which electives align with your interests.
Upon completion of your core and elective classes, you will be asked to complete a thesis or capstone project of your choice. These final courses will give you a chance to showcase your work and highlight the knowledge you gained while in the program.
Master of Science in Information Technology Program Highlights
High Quality Education
- Smaller class sizes designed to provide each student the opportunity to connect with their professors
- MS IT program faculty that are technology professionals who are leaders in the industry, boasting extensive real-world experience and academic expertise
- Various professional development opportunities - we facilitate networking through platforms like Discord, enhancing your overall learning experience
Flexibility
- 100% online
- Can be completed in less than two years
- Start in the fall, spring, or summer semester
- Financial aid & scholarship options available
Support from Application through Graduation
At UC, you’ll have a full support team behind you:
Enrollment Services Advisor: Your go-to resource during the application process
Student Success Coordinator: Helping you prepare for classes and stay on track
Access to Resources: Access to university resources that will support you through your program including online learning expectations and resources, health and wellness resources, and academic support
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.
| Course | Title / Description | Credit |
|---|---|---|
| IT 6035C | Information Security and Assurance 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 |
| IT 6060C | Database Management with SQL Server 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 |
| IT 6090C | Computer Programming with Java 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 |
| IT 6081C | System Administration 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 |
| Course | Title / Description | Credit |
|---|---|---|
| IT7001 | Information Technology Graduate Seminar 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 This course focuses on scientific approaches to studying information technologies and writing thesis and other research reports. |
3 |
| IT7040C | Human Computer Interaction and Usability 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.
| Course | Title / Description | Credit |
|---|---|---|
| IT8020 | Information Technology Thesis Research Individual research under the supervision of IT faculty directs towards the completion of the MS thesis. |
3 |
| Course | Title / Description | Credit |
|---|---|---|
IT Electives: Minimum of 9 Credit Hours Required Any IT 7000+ level course |
||
| IT7020C | Principles of Cybersecurity 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 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 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 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 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 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 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 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 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 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 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 |
Non-IT Electives: A Maximum of 6 Credit Hours Allowed 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. |
||
| CJ6012 | Cybercrime 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 |
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.
| Course | Title / Description | Credit |
|---|---|---|
| IT 8020 | Information Technology Project Individual projects are under the supervision of program faculty for partial fulfillment of the Master of Science degree. |
3 |
| Course | Title / Description | Credit |
|---|---|---|
IT Electives: Minimum of 9 Credit Hours Required Any IT 7000+ level course |
||
| IT7020C | Principles of Cybersecurity 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 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 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 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 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 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 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 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 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 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 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 |
Non-IT Electives: A Maximum of 6 Credit Hours Allowed 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. |
||
| CJ6012 | Cybercrime 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 |
Professionals who have a strong background in a computing discipline, hold IT certificates, and/or have work experience in the field can be admitted directly into the online Master in Information Technology program (the GRE is waived for qualified candidates).
The University’s MS IT program is not just for experienced industry professionals. Our program is open to individuals from a variety of backgrounds, though many already have experience in a computing discipline or hold an IT certificate. If prospective students do not have an IT bachelor’s degree or an IT background, they can still seek admittance by taking 12 credit hours of bridge courses to help them qualify for the program.
Our admissions office is happy to work with you to answer any questions you may have and help determine your eligibility. Contact us today!
Prerequisites
- A bachelor’s degree from a regionally accredited college/university.
- Overall minimum GPA of 3.0 on a 4.0 scale.
*May be required to share prior work experience if you do not have a background in IT or meet the 3.0 GPA requirement.
Admission Materials
- Resume or CV.
- Transcripts.
- Three Program-Specific Essay Questions (Answers not to exceed 500 words each).*
- Three Letters of Reference.
- Official GRE Score (Unless the student has a cumulative GPA of 3.0 or higher)
*Effective for Fall 2026 Applications: A personal statement will replace the three program-specific essay questions.
Automatic admission into the MSIT-DL program for undergraduate students who graduate from UC.
Any UC Alumni with a 3.0 undergraduate GPA and the following backgrounds are eligible for our fast-track admission process:
- UC alumni with undergrad or grad degrees in IT (BSIT and BS Cyber), CS, and IS.
- UC alumni with a bachelor’s degree who completed a minor in IT or undergrad/grad certificate in IT.
- UC alumni who have completed a bachelor’s and have an associate degree in IT or an associate in computer science.
The Fast Track application process removes the Statement of Purpose/Personal Statement, CV/Resume, and Letter of Recommendation requirements from the application for qualified applicants. Submission of transcripts as part of the application process is still required.
Holding a recognized technical industry certification gives your application an advantage. These certifications are not merely attendance-based; they require passing rigorous exams that validate your expertise in specific IT skills and knowledge. Include proof of your certification(s) in the “Additional Materials” section of your application to receive this benefit.
Examples of qualifying certifications:
- CompTIA A+ (general IT technical support)
- Cisco Certified Network Associate (CCNA) (networking)
- (ISC)² Certified Information Systems Security Professional (CISSP) (cybersecurity)
- Project Management Institute (PMI) Project Management Professional (PMP)
- Certified Associate in Project Management (CAPM) (project management)
- Microsoft Certified: Azure Administrator Associate (cloud computing)
The School of Information Technology offers various pathways for learning new job skills specific to IT through numerous bootcamps and workshops (some of which lead to industry certification). Learn more here.
Complete the online application and submit the application fee.
Standard Application Fees:
- $65.00 for domestic applicants to most degree programs
- $70.00 for international applicants to most degree programs
- $20.00 for domestic applicants to Graduate Certificates
- $25.00 for international applicants to Graduate Certificates
- Application fees are waived for Summer 2026 applications submitted by March 1st, 2026
- Application fees are waived for Fall 2026 applications submitted by July 1st, 2026
- Fee waivers are automatically applied for applicants who:
- are currently serving in the US armed forces
- are veterans of the US armed forces
All applicants are required to upload unofficial transcripts during the application process, showing all undergraduate and graduate course work completed, including degrees granted and dates of conferral.
Official transcripts are not required until the student has received and accepted an offer of admission from the university. Once the offer has been confirmed, the student must submit official transcripts.
Students who have received degrees from the University of Cincinnati do not need to submit official paper copies of their UC transcripts.
Transcripts can be submitted electronically or by mail. To see if your transcript(s) can be ordered electronically, visit the links below and search for your previous school(s).
- Parchment
- Please select “University of Cincinnati – Main Campus” as the recipient of your transcript.
- National Student Clearinghouse
- Please have your transcript sent directly to admissions@uc.edu.
If you do not see your past school(s) listed on either site, please contact the school(s) directly. Then, mail your sealed, unopened, official transcripts to:
Please mail sealed, unopened, official transcripts to:
University of Cincinnati
Office of Admissions
PO Box 210091
Cincinnati, Ohio 45221-0091
Current resume or curriculum vitae.
Three letters of reference from individuals who know you professionally or academically. Recommendation letters should be submitted on letterhead with a clear indication of what the relationship is between the recommender and applicant.
Three Program Specific Questions:
In response to a series of 3 essay questions, applicants will be required to discuss career goals, academic accomplishments, professional experiences and preparedness for graduate studies in IT (Applicants are encouraged to share a copy of any relevant industry certifications within the “Additional Materials” section of the application. We do not require a personal statement for the MSIT application; however, sharing one’s unique story and background can help to further strengthen the application. You can upload a statement under “Additional Materials.”):
- (Required question – Do not exceed 500 words.) Describe your career goals and explain how a master’s in information technology will help you accomplish these goals.
- (Required question – Do not exceed 500 words.) How has your past experience prepared you to be successful in this program? For example, what types of courses, industry experience, or industry-recognized certificates have you completed to build skills in information technology?
- (Required question – Do not exceed 500 words.) Our program requires prerequisite knowledge of four content areas, applicants without this prerequisite knowledge may be assigned 1-4 bridge classes in their first semester to be completed outside of the 30 credits required by the degree. If you have a background in these areas, please describe items in your transcript, industry recognized certificates, or work experience (over 6 months) in the following four areas: 1) Programming, 2) Information Security, 3) System Administration, 4) Database Management.
*Effective for Fall 2026 applications: A personal statement with replace the three essay questions. Please see below for more details.
*Effective beginning with Fall 2026 Applications.
Please submit a personal statement that explains your interest in the Master of Science in Information Technology (MSIT) program at the University of Cincinnati. To help you craft a compelling and authentic statement, consider the following guidance:
- Think of your personal statement as a conversation with the faculty members reviewing your application. This is your opportunity to present your motivations, experiences, and goals directly to them.
- Use your own voice. While it is fine to use tools to refine your writing, ensure your final statement reflects your genuine thoughts and experiences.
- Refer to the UC School of Information Technology’s definition of the IT discipline. Explain what draws you to the field of Information Technology and why you want to pursue an MSIT degree.
- Reflect on your academic background and professional experience, and how they relate to your interest in IT.
- Describe how the MSIT program aligns with your career goals and how it will help you achieve them.
GRE is waived for applicants with over a 3.0 GPA. Student required to take the GRE must receive a 150 in both of the sections (300 total).
For International students, an English proficiency test:
- TOEFL minimum Internet-based 80
- Paper-based 520
- Computer-based 190
- IELTS minimum 6.5
| Term | Application Deadline | Classes Start |
|---|---|---|
Summer 2026 Fall 2026 |
May 1, 2026 August 1, 2026 |
May 11, 2026 August 24, 2026 |
The University of Cincinnati's online course fees differ depending on the program. On average, students will accrue fewer fees than students attending on-campus classes.
The one fee applied across all UC Online programs is the distance learning fee. Students living outside the state of Ohio must also pay an additional “non-resident” fee to enroll in courses at UC Online. This fee is lower than the out-of-state fee for traditional on-campus programs.
To view tuition information and program costs, visit the Online Program Fees page.
Earn your master degree from the world renowned University of Cincinnati School of IT. Create a pathway that fits your needs and will help you achieve your career goals and aspirations. Our online program is ranked as one of the best schools for online learning at the master’s level by OnlineMastersDegrees.org. UC’s online Master of Science in Information Technology program was also ranked No. 10 by U.S. News and World Report.
Nationally Recognized Cybersecurity Program
The University of Cincinnati is one of only a few institutions in the country to have a National Security Agency (NSA)-sponsored National Centers of Academic Excellence in both cyber defense and cyber operations.
Our School of Information Technology received several grants from the NSA, National Science Foundation and Ohio state government to support its cybersecurity education program.
We are also home to the Ohio Cyber Range Institute, a virtual environment where participants can learn about cybersecurity and practice defending networks against threats of all kinds.
You can now earn a Certificate in Cybersecurity alongside your master’s degree at no additional cost. Learn more about our Data-Driven Cybersecurity Certificate.
I’ve been extremely pleased with my experience so far in the MS IT program here at the University of Cincinnati. The professors have been responsive and available when I’ve needed help, and have designed their courses in ways that encourage online interaction among students.
Andrew H. MSIT
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