Curriculum: MS Information Technology

December 01, 2022
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January 09, 2023
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curriculum icon 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 UC Online 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 MSIT 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:

IT Curriculum

 

  • MSIT students pursuing the capstone track will complete their final project by taking a 3-credit course in their last semester.
  • MSIT 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 MSIT 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 MSIT coursework.  Bridge courses do not count towards the MSIT 30-hour requirement.

 

Bridge Certificate

*****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
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Core Courses

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
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Thesis Track

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. Starting the thesis often does not correspond with the timing of enrolling in thesis credit hours, as it typically takes 3 semesters or more to write a publication-quality thesis. 6 credit hours of IT8020 may be split between the final two semesters or all 6 credit hours may be taken in the final semester when the student will defend their thesis.

6 credit hours
Course Title / Description Credit
IT8020
Information Technology Thesis Research
Course: IT8020
Credit: 1
Individual research under the supervision of IT faculty directs towards the completion of the MS thesis.
1
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Non-Thesis Track

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: 1
Individual projects are under the supervision of program faculty for partial fulfillment of the Master of Science degree.
1
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IT Electives

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
IT7022C
Software Application Security
Course: IT7022C
Credit: 3
This course will examine perspectives and techniques to decrease the security risk of the software. When threats and vulnerabilities are addressed early in the development cycle, securitycan be built in. The scope of this course may be varied by the faculty.
3
IT7023C
Technologies for Social Networking
Course: IT7023C
Credit: 3
Social Networking has become a popular method for communicating and sharing information in online groups via the Internet and other technologies. The purpose of this course is to introduce students to a variety of social networking development tools, including blogs, wikis, Twitter, social bookmarking, and cloud storage. Students will be provided information on each of the social networking tool, learn how to use them,and then work individually and collaboratively to formulate ideas for implementing social networking tools in order to facilitate working and the use of technology. The scope of this course may be varied by the faculty.
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
IT7025C
Usability for Mobile Applications
Course: IT7025C
Credit: 3
This course teaches students the concepts and strategies needed to create usable interfaces optimized for mobile applications. In this course,students examine the foundation of creating logical, intuitive, and clear interfaces. The course examines interaction principles, experiential, and gestural design patterns relating from usability, visualization, and functionality constructs associated with human factors that drive touch screen interfaces. 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
IT8002
History and Theories of Information Technology
Course: IT8002
Credit: 3
This course surveys a comprehensive history of Information Technology (IT) as a discipline and explores important theories in this field. In this course, we will use a socio-technical perspective to investigate the emergence and development of information technologies as well as varied theories to study the social, organizational, and technical implications of IT. Students will find that information technology is an interdisciplinary field that draws upon many other disciplines and covers all phases of the technology life cycle. They will develop a foundation in IT research that will prepare them for advanced studies in IT.
3
IT8003
Evidence-based Information Technology Practice
Course: IT8003
Credit: 3
This course will evaluate the accumulated data, evidence, and research in support of different information technology strategies, tools and techniques. It will provide a foundational overview of evidence-based practice (EBP) as well as research-supported strategies for implementing EBP in real world settings. The course will improve the learner’s understanding and use of EBP, to include the ability to ask relevant questions, find and interpret the evidence, and apply this evidence to the IT practice. The critical thinking abilities, knowledge and decision-making skills developed in this course are particularly valuable for information technology leaders, and emergent leaders.
3
IT8004
Foundations of Human-Centered Information Technology
Course: IT8004
Credit: 3
This course surveys the past and current theories, as well as the scope of issues and foundational topics relevant to human-centered practice of Information Technology: cognitive psychology, human factors, human-centered design methods, techniques, and evaluation.
3
IT8005
Secure Information Technology Practice
Course: IT8005
Credit: 3
This course will cover the current trends in the practice of secure information technology (IT). The learners will be introduced to real life scenarios demonstrating security implications for different IT decisions. The course will cover the evolution of IT solutions, the trustworthiness in an IT solution, and evaluating an IT solution with key system characteristics such as security, safety, reliability, resilience and privacy.
3
IT8006
Research Methods for Human-Centered Information Technology
Course: IT8006
Credit: 3
This course surveys a broad range of research methods employed in Human-Centered Computing. The course will cover a variety of human-centered design techniques (such as contextual design, scenario-based design, participatory design, etc.) for use in developing and evaluating socio-technical systems. This course is intended for students in the Information Technology graduate programs; especially Ph.D. students who are conducting empirical design research.
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
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Approved Electives Outside IT

A maximum of 6 credit hours
Course Title / Description Credit
CJ6012
Computer Criminology: Cybercrime and Digital Security
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
CJ8072
Seminar in White-Collar Crime
Course: CJ8072
Credit: 3
This course provides a detailed examination of white-collar crime. The primary goals are to provide an introduction to the field of white-collar crime and identify the main research issues and directions that currently dominate thisarea of study. The course reviews the history of the field and its relevance to mainstream criminology. It identifies the distinguishing charcteristics of white-collar crime. Selected forms of white-collar crime are investigated. Methods of controlling white-collar crime, including civil, administrative, criminal justice and situational crime prevention techniques, are explored.
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
CS6065
Introduction to Cloud Computing
Course: CS6065
Credit: 3
The course will cover the following topics: 1. Introduction and Historical perspectives 2. Cloud Models: IaaS, PaaS, SaaS. 3. Underlying technologies: Networking, Internet Architecture, Virtualization 4. Distributed System Concepts 5. MapReduce Paradigm & Hadoop 6. Cloud Applications & Architectures 7. Modern Case Studies in Hadoop and Cloud Applications 8. Designing Applications for the Cloud 9. Cloud Security 10. Public cloud offerings
3
CS6097
Introduction to Wireless and Mobile Networking
Course: CS6097
Credit: 3
Introduction to Wireless and Mobile Systems, Probability Theory, Statistics, and Traffic Theories, Mobile Radio Propagation, Channel Coding, Cellular Concept, Multiple Radio Access, Multiple Division Techniques, Channel Allocation, Mobile Communication Systems, Existing Wireless Systems, Network Protocols, Ad Hoc Networks, Sensor Networks, Wireless LANs and PANs, Recent Advances.
3
CS7035
Cryptography 1
Course: CS7035
Credit: 3
Conventional and public key cryptography. Digital signatures. Secure voting schemes. Provably secure cryptosystems. Protocols using zero-knowledge proofs. Using multiprecision arithmetic for implementing and attacking cryptosystems based on large integers--e.g. 300 digit numbers. The analysis and cracking of cryptosystem algorithms and protocols. Examples of cracked systems.
3
CS7038
Malware Analysis: Introduction to Reverse Engineering
Course: CS7038
Credit: 3
1.Introduction: Definition, Classification, Using Isolated Environments (VMs) - Students will learn the types of malware and the basics of the tools used for reverse engineering. 2. Intel x86 Architecture: Instruction Set, Memory, Calling Conventions - Students will perform a low level analysis of those instructions including memory and register usage, and calling conventions for subroutines and libraries. 3. C in Assembly: Variables, Stack Usage, Structs, Optimizations - the students will learn what common C programming constructs look like in compiled assembly code. 4.Intent: History, Identify Purpose, Network Analysis - These topics focus on how to identify the intent of a malware program. 5. Overview of Windows: APIs, Processes and Threads, Network - Students will learn techniques for identifying malware actions by looking at how the code utilizes the operating system's application programming interfaces, process handling, and network connectivity. 6. Anti-RE: Obfuscation, Packers, Detection of RE - Students will learn howmodern malware executables use different techniques to hide code.
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
CS8021
Pattern Recognition
Course: CS8021
Credit: 3
The topics covered will include Statistical Pattern Recognition - its basics and applications,algorithms for clustering and their analysis. A flavor of different types of clustering algorithms will be given and a few algorithms will be studied in great depth. Relevance of all the above techniques for pattern discovery, classifier design, and dimensionality reduction will be investigated. A number of examples from real-life datasets will be examined in depth during the class presentations and by students during their homework assignments.
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: 2-3
This course introduces the student to data science in healthcare. The course addresses both advanced database and introductory statistics and data science programming as essential skills in data science. Students increase their database and data science skills through the use of the programming language and advanced database-related assignments. Concepts such as data exploration, transformation and visualization are taught, along with how to address missing data and outliers, and how to conduct tests of assumptions and hypothesis testing. This course assumes that students have completed an introductory database course.
2-3
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
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