The primary objective of this course is to provide the experienced programmer with knowledge of and experience with fundamental data structures and algorithms used in software design and development. The secondary objective is to give a fast-paced introduction to the Java programming language. Students will write multiple programs in Java, both to become familiar with Java and to apply data structure concepts. Prerequisite: none

This is an introductory software development course, with focus on intermediate-level fundamental and foundational concepts. These concepts include abstract data types such as lists, stacks, queues, and trees/graphs, as well as some of their associated algorithms such as insertion, deletion, searching, sorting, and traversals. Canonical implementations as well as framework supplied implementation alternatives (such as the JDK or other framework alternatives) will be explored and used. To apply the lecture concepts, we will implement software using the Java programming language and explore some of the tools used by software developers. There are many types of tools to be considered, such as integrated development environments (IDEs e.g. eclipse), tools for managing software build, configuration, and version control (e.g. Ant/Maven, Git), tools for testing and debugging (e.g. JUnit, LogBack/Log4J/SLF4J) and other tools used by developers to understand the code they are working with (e.g. GrepCode). In addition, we will discuss intermediate concepts, issues, and techniques including an introduction to concurrency issues, and development practices such as refactoring, logging, and debugging. Prerequisite: SEIS 601 or an equivalent understanding of Foundational Software Development concepts and the ability to use and understand the Java programming language is required.

This is an introductory software development course, with focus on fundamental and foundational concepts. These concepts include general problem solving and algorithm creation techniques, data types, constants, variables and expressions, Boolean, control flow, and object-oriented concepts. Applying these concepts, we implement programs using the Python language. We will examine its use as both an interpreted and a compiled language, working with data types such as numbers, strings, lists, dictionaries, and sets. Students will learn how to apply Python in managing data. No previous programming experience in Python or any other programming language is required.

This is an introductory software development course, with focus on fundamental and foundational concepts. These concepts include general problem solving and algorithm creation techniques, data types, constants, variables and expressions, Boolean, control flow, and object-oriented concepts. Applying these concepts, we implement programs using the Python language. We will examine its use as both an interpreted and a compiled language, working with data types such as numbers, strings, lists, dictionaries, and sets. Students will learn how to apply Python in managing data. No previous programming experience in Python or any other programming language is required.

This is an introductory software development course, with focus on fundamental and foundational concepts. These concepts include general problem solving and algorithm creation techniques, data types, constants, variables and expressions, Boolean, control flow, and object-oriented concepts. Applying these concepts, we implement programs using the Python language. We will examine its use as both an interpreted and a compiled language, working with data types such as numbers, strings, lists, dictionaries, and sets. Students will learn how to apply Python in managing data. No previous programming experience in Python or any other programming language is required.

This is an introductory software development course, with focus on fundamental and foundational concepts. These concepts include general problem solving and algorithm creation techniques, data types, constants, variables and expressions, Boolean, control flow, and object-oriented concepts. Applying these concepts, we implement programs using the Python language. We will examine its use as both an interpreted and a compiled language, working with data types such as numbers, strings, lists, dictionaries, and sets. Students will learn how to apply Python in managing data. No previous programming experience in Python or any other programming language is required.

This is an introductory software development course, with focus on fundamental and foundational concepts. These concepts include general problem solving and algorithm creation techniques, data types, constants, variables and expressions, Boolean, control flow, and object-oriented concepts. Applying these concepts, we implement programs using the Python language. We will examine its use as both an interpreted and a compiled language, working with data types such as numbers, strings, lists, dictionaries, and sets. Students will learn how to apply Python in managing data. No previous programming experience in Python or any other programming language is required.

Teaches the fundamentals of written and oral communication as practiced by IT professionals. The course emphasizes product descriptions, instructions, informative and persuasive oral presentations, the role of graphics, and teamwork on projects. In addition, the course introduces managerial strategies and tactics, such as planning and evaluation, which are critical for meeting an intended audience's needs. Recently, the scope of this course was expanded to include communication issues related to business analysis and project management. After completing this course, students will be more confident about their ability to communicate effectively in the workplace. For MS Software students, this course must be completed before exceeding 12 credits in Software Engineering, Software Management, and Information Technology.

Teaches the fundamentals of written and oral communication as practiced by IT professionals. The course emphasizes product descriptions, instructions, informative and persuasive oral presentations, the role of graphics, and teamwork on projects. In addition, the course introduces managerial strategies and tactics, such as planning and evaluation, which are critical for meeting an intended audience's needs. Recently, the scope of this course was expanded to include communication issues related to business analysis and project management. After completing this course, students will be more confident about their ability to communicate effectively in the workplace. For MS Software students, this course must be completed before exceeding 12 credits in Software Engineering, Software Management, and Information Technology.

This is a survey course covering software engineering concepts, techniques, and methodologies. Topics covered include software engineering; software process and its difficulties; software life-cycle models; software metrics; project planning including cost estimation; design methodologies including structured design, and object-oriented design; software testing; and software maintenance. A brief review of data structures is included. Prerequisite: SEIS 601 or SEIS 603. SEIS 610 can be taken concurrently with SEIS 601 or SEIS 603.

This is a survey course covering software engineering concepts, techniques, and methodologies. Topics covered include software engineering; software process and its difficulties; software life-cycle models; software metrics; project planning including cost estimation; design methodologies including structured design, and object-oriented design; software testing; and software maintenance. A brief review of data structures is included. Prerequisite: SEIS 601 or SEIS 603. SEIS 610 can be taken concurrently with SEIS 601 or SEIS 603.

This is a survey course covering software engineering concepts, techniques, and methodologies. Topics covered include software engineering; software process and its difficulties; software life-cycle models; software metrics; project planning including cost estimation; design methodologies including structured design, and object-oriented design; software testing; and software maintenance. A brief review of data structures is included. Prerequisite: SEIS 601 or SEIS 603. SEIS 610 can be taken concurrently with SEIS 601 or SEIS 603.

Students gain a management perspective and a development process for planning, estimating, and controlling software development. They learn to develop a well-defined plan before beginning any software development effort; how to handle changes during the execution of the plan; how to incorporate quality criteria in the development cycle; and how to use methods to keep the project on track. Included in the course is the use of project management software and simulation software in the development and control of the project plan.(If credit is received for this course students cannot receive credit for SEIS621 [CSIS526].) Prerequisite: SEIS610

Students gain a management perspective and a development process for planning, estimating, and controlling software development. They learn to develop a well-defined plan before beginning any software development effort; how to handle changes during the execution of the plan; how to incorporate quality criteria in the development cycle; and how to use methods to keep the project on track. Included in the course is the use of project management software and simulation software in the development and control of the project plan.(If credit is received for this course students cannot receive credit for SEIS621 [CSIS526].) Prerequisite: SEIS610

This course builds on the project management process through the application of Software Quality Engineering concepts (Quality Assurance, Control and Testing). Students will work through a semester project in which they will think like a Software Quality Engineer. Practical tools and techniques will be applied toward the management and improvement of the quality of a software product and the development process. (If credit is received for this course, students cannot receive credit for SEIS 621 [CSIS526].) Prerequisite: SEIS625. SEIS 626 can be taken concurrently with SEIS 625.

This course builds on the project management process through the application of Software Quality Engineering concepts (Quality Assurance, Control and Testing). Students will work through a semester project in which they will think like a Software Quality Engineer. Practical tools and techniques will be applied toward the management and improvement of the quality of a software product and the development process. (If credit is received for this course, students cannot receive credit for SEIS 621 [CSIS526].) Prerequisite: SEIS625. SEIS 626 can be taken concurrently with SEIS 625.

This course focuses on database management system concepts, database design, and implementation. Conceptual data modeling using Entity Relationships (ER) is used to capture the requirements of a database design. Relational model concepts are introduced and mapping from ER to relational model is discussed. Logical database design (Normalization) and indexing strategies are also discussed to aide system performance. Relational Algebra and Structured Query Language (SQL) are used to work with a database. From a system perspective, the course focuses on query optimization and execution strategies, concurrency control, locking, deadlocks and database back-up and recovery concepts. Database security and authorization are also discussed. Students will use Oracle and/or SQL Server to design a database and complete an application using SQL as their project. Prerequisite: SEIS 610. SEIS 630 may be taken concurrently with SEIS610.

This course focuses on database management system concepts, database design, and implementation. Conceptual data modeling using Entity Relationships (ER) is used to capture the requirements of a database design. Relational model concepts are introduced and mapping from ER to relational model is discussed. Logical database design (Normalization) and indexing strategies are also discussed to aide system performance. Relational Algebra and Structured Query Language (SQL) are used to work with a database. From a system perspective, the course focuses on query optimization and execution strategies, concurrency control, locking, deadlocks and database back-up and recovery concepts. Database security and authorization are also discussed. Students will use Oracle and/or SQL Server to design a database and complete an application using SQL as their project. Prerequisite: SEIS 610. SEIS 630 may be taken concurrently with SEIS610.

This course focuses on database management system concepts, database design, and implementation. Conceptual data modeling using Entity Relationships (ER) is used to capture the requirements of a database design. Relational model concepts are introduced and mapping from ER to relational model is discussed. Logical database design (Normalization) and indexing strategies are also discussed to aide system performance. Relational Algebra and Structured Query Language (SQL) are used to work with a database. From a system perspective, the course focuses on query optimization and execution strategies, concurrency control, locking, deadlocks and database back-up and recovery concepts. Database security and authorization are also discussed. Students will use Oracle and/or SQL Server to design a database and complete an application using SQL as their project. Prerequisite: SEIS 610. SEIS 630 may be taken concurrently with SEIS610.

This course provides a broad introduction to the subject of data analysis by introducing common techniques that are essential for analyzing and deriving meaningful information from datasets. In particular, the course will focus on relevant methods for performing data collection, representation, transformation, and data-driven decision making. Students will also develop proficiency in the widely used R language which will be used throughout the course to reinforce the topics covered. Prerequisite: SEIS 601 or SEIS 603.

This course provides a broad introduction to the subject of data analysis by introducing common techniques that are essential for analyzing and deriving meaningful information from datasets. In particular, the course will focus on relevant methods for performing data collection, representation, transformation, and data-driven decision making. Students will also develop proficiency in the widely used R language which will be used throughout the course to reinforce the topics covered. Prerequisite: SEIS 601 or SEIS 603.

This course provides a broad introduction to the subject of data analysis by introducing common techniques that are essential for analyzing and deriving meaningful information from datasets. In particular, the course will focus on relevant methods for performing data collection, representation, transformation, and data-driven decision making. Students will also develop proficiency in the widely used R language which will be used throughout the course to reinforce the topics covered. Prerequisite: SEIS 601 or SEIS 603.

The course provides an introduction to concepts and techniques used in field of data analytics and visualization. Data analytics is defined to be the science of examining raw data with the purpose of discovering knowledge by analyzing current and historical facts. Insights discovered from the data are then communicated using data visualization. Topics covered in the course include predictive analytics, pattern discovery, and best practices for creating effective data visualizations. Through practical application of the above topics, students will also develop proficiency in using analytics tools.

The course provides an introduction to concepts and techniques used in field of data analytics and visualization. Data analytics is defined to be the science of examining raw data with the purpose of discovering knowledge by analyzing current and historical facts. Insights discovered from the data are then communicated using data visualization. Topics covered in the course include predictive analytics, pattern discovery, and best practices for creating effective data visualizations. Through practical application of the above topics, students will also develop proficiency in using analytics tools.

The course provides an introduction to concepts and techniques used in field of data analytics and visualization. Data analytics is defined to be the science of examining raw data with the purpose of discovering knowledge by analyzing current and historical facts. Insights discovered from the data are then communicated using data visualization. Topics covered in the course include predictive analytics, pattern discovery, and best practices for creating effective data visualizations. Through practical application of the above topics, students will also develop proficiency in using analytics tools.

This course covers basic object-oriented techniques for specifying, designing, and implementing software systems. Iterative development methodologies are emphasized. The Unified Modeling Language (UML) is used as a notational system for capturing the development process artifacts. Students will gain experience with a software tool for creating UML diagrams. Other topics include use cases, class discovery and domain modeling, responsibility-driven design, basic design patterns, software class design, converting designs to code, object-oriented testing, packaging, deployment, along with intermediate Java topics relevant to system implementation. This course also introduces ideas in functional and parallel programming. Students will work on an object-oriented team project, apply concepts and techniques to describe and create a working software system. Prerequisite: SEIS 602 and SEIS 610.

The objective of this course is to introduce the business analyst roles and responsibilities and knowledge areas such as enterprise analysis, requirements planning and measurement, requirements elicitation, requirements communication, requirements analysis and documentation, solution assessment and validation, business analysis fundamentals including tools and techniques. Prerequisite: SEIS 610. SEIS 636 may be taken concurrently with SEIS 610.

An introduction to the concepts and principles involved in operating systems design is provided. Topics in the course include computer-system structures, operating-systems structures, job and process scheduling, process synchronization, deadlock, memory management, virtual memory, file systems, input/output systems, distributed system structures, distributed file systems, protection, system security, and case studies of operating systems. Prerequisite: SEIS 610. This is a hybrid class which blends face-to-face seated time with online learning activities. Knowledge of a programming language is not required for this course, but programming intensive homework will be offered as an option.Seated Wednesdays classes are scheduled as follows: 1/31, 2/14, 2/28, 3/14, 4/4, 4/18, 5/2

This course will provide a practical overview of Enterprise Resource Planning, connecting the academic and even marketing elements with real-world, case-based issues as encountered by business and other organizations. ERP has become a critical strategic consideration for many companies, and the course will look at best-practice implementations at leading companies internationally. Course will examine best practice usage of ERP in a global distributed computing environment. In addition, it will look into trends relating to critical issues such as Cloud and Big Data. Professionals currently working in the IT organizations or future IT professionals will benefit from this course. Prerequisite: SEIS 610. SEIS 662 may be taken concurrently with SEIS 610.

This course will provide the foundation of information technology security, including authentication, authorization, access management, physical security, network security (firewalls, intrusion detection), application security (software and database), security regulations, and disaster recovery. We will explore social engineering and other human factors and the impact to security. There will be an emphasis on local area networking (LAN) and Internet architecture and protocols, including TCP/IP and the OSI layers. We study protocol details, the way they relate and interact with each other, and how they are applied in real systems. Prerequisite: SEIS610

This course will provide the foundation of information technology security, including authentication, authorization, access management, physical security, network security (firewalls, intrusion detection), application security (software and database), security regulations, and disaster recovery. We will explore social engineering and other human factors and the impact to security. There will be an emphasis on local area networking (LAN) and Internet architecture and protocols, including TCP/IP and the OSI layers. We study protocol details, the way they relate and interact with each other, and how they are applied in real systems. Prerequisite: SEIS610

This survey course covers IT delivery, operations, and management in both theory and practice, including Business and consumer needs for IT value, IT infrastructure, cloud, continuous integration/delivery, IT product and service management, work and task management, operations management, organization and culture, project management, process management, change and incident management, IT governance, risk, security, and compliance business continuity, enterprise information and e-records management, enterprise IT architecture and portfolio management, IT management frameworks including ITIL, COBIT, and IT4IT, Agile and Lean influences including Kanban, Scrum, and DevOps, Continuous improvement and IT. Prerequisite: SEIS 610

This course covers the engineering and design of IT infrastructure, focusing on cloud-scale distributed systems and modern DevOps practices. IT infrastructure deployment practices are rapidly changing as organizations build "Infrastructure as code" and adopt cloud computing platforms. We will examine the theory behind these modern practices and the real-world implementation challenges faced by IT organizations. While the lessons will cover a number of theoretical concepts, we will primarily learn by doing. Students will gain hands-on experience with several widely-adopted IT platforms including Github, AWS, and Docker. Prerequisite: SEIS 610

In order to build and maintain a successful data warehouse, it is important to understand all of its components and how they fit together. This course will cover data warehouse and data mart lifecycle phases while focusing on infrastructure, design, and management issues. The course project will provide an opportunity to for hands-on experience with some of the available tools and technologies. Topics include: differences between data warehouses and traditional database systems (OLTP), multidimensional analysis and design, building data warehouses using "cube" vs. RDBMS (Star schema, etc.), planning for data warehouses, extraction transformation and loading (ETL), online analytical processing (OLAP), data mining, quality and cleansing, common pitfalls to avoid when designing, implementing and maintaining data warehouse environments, and the impact of new technologies (data webhouse, clickstream, XML). Prerequisite: SEIS630

In order to build and maintain a successful data warehouse, it is important to understand all of its components and how they fit together. This course will cover data warehouse and data mart lifecycle phases while focusing on infrastructure, design, and management issues. The course project will provide an opportunity to for hands-on experience with some of the available tools and technologies. Topics include: differences between data warehouses and traditional database systems (OLTP), multidimensional analysis and design, building data warehouses using "cube" vs. RDBMS (Star schema, etc.), planning for data warehouses, extraction transformation and loading (ETL), online analytical processing (OLAP), data mining, quality and cleansing, common pitfalls to avoid when designing, implementing and maintaining data warehouse environments, and the impact of new technologies (data webhouse, clickstream, XML). Prerequisite: SEIS630

Database Administrators (DBA's) have to perform multiple functions within an organization. This class focuses on the issues that database administrators have to deal with in their everyday professional life. Responsibilities of a DBA are broken down by functions and each function is studied. These include: database system planning, database system installation and upgrading, database design (conceptual, logical, and physical), normalization (de-normalization), database loading and unloading, database change management, data availability, database security and access management, performance management (query processing, indexing, physical space planning, etc.), system performance, data integrity, data and storage management, data migration, data movement and distribution, database connectivity, fault tolerance (back ups and recovery) and disaster recovery planning. Students will use SQL Server and Oracle to design, implement and administer their database using these two commercial products. Although the course uses examples of these two two product functions, it is not a SQL Server or Oracle DBA certifcation course. Prerequisite: SEIS 630

To overcome data overloading problems, this course will discuss how to apply big data analytics to extract useful patterns from huge datasets and generate visual summary of data. This course will also demonstrate mining and analyzing big data on Amazon Cloud. Key topics of this course include: (1) mining association rule and market basket analysis, (2) classification and predictive analysis, (3) clustering and market segmentation, and (4) combining numeric analysis with text sentiment analysis. Real-world data will be used to illustrate the data mining concepts and their possible pitfalls. Sample case studies include: (1) predicting company’s credit ranking, (2) classifying cancer types from gene data, (3) analyzing sentiment from customer text reviews and numeric rankings, (4) finding associations among medical keywords from medical journal papers. Prerequisite: SEIS630 and programming experience.

To overcome data overloading problems, this course will discuss how to apply big data analytics to extract useful patterns from huge datasets and generate visual summary of data. This course will also demonstrate mining and analyzing big data on Amazon Cloud. Key topics of this course include: (1) mining association rule and market basket analysis, (2) classification and predictive analysis, (3) clustering and market segmentation, and (4) combining numeric analysis with text sentiment analysis. Real-world data will be used to illustrate the data mining concepts and their possible pitfalls. Sample case studies include: (1) predicting company’s credit ranking, (2) classifying cancer types from gene data, (3) analyzing sentiment from customer text reviews and numeric rankings, (4) finding associations among medical keywords from medical journal papers. Prerequisite: SEIS630 and programming experience.

This course covers emerging big data architectures, predominately Hadoop and related technologies that deal with large amounts of unstructured and semi-structured data. Topics include operating system, architecture, security, big data structure and storage. The primary applications discussed in this class focus on information retrieval, specifically text processing techniques and algorithms, such as parsing, stemming, compression, and string searching. Information retrieval is also a great case study for broader issues in building systems that scale and perform, so we discuss associated issues in data structures, algorithms, computational complexity, and measurement. Prerequisite: (SEIS 601 or SEIS 603) and SEIS 630.

This course covers emerging big data architectures, predominately Hadoop and related technologies that deal with large amounts of unstructured and semi-structured data. Topics include operating system, architecture, security, big data structure and storage. The primary applications discussed in this class focus on information retrieval, specifically text processing techniques and algorithms, such as parsing, stemming, compression, and string searching. Information retrieval is also a great case study for broader issues in building systems that scale and perform, so we discuss associated issues in data structures, algorithms, computational complexity, and measurement. Prerequisite: (SEIS 601 or SEIS 603) and SEIS 630.

This course covers the technical concepts of managing vast amount of unstructured, semi-structured and structured data, collectively called "Big Data". Due to the sheer volume of Big Data, traditional approaches to managing databases does not work well for Big data and does not perform as expected. A distributed architecture for both the file system and the operating system is needed. Some of the techniques used in managing Big Data have the origins in the research and the developments that have been going on for decades in the area of parallel processing and distributed database management systems. This course focuses on why big data sets must be distributed and the issues that distribution introduces. The basic concepts on which distributed data sets are handled are discussed first. Once a foundation is defined, software tools that we use to work with big data sets are studied to provide an in-depth analysis of the concepts introduced. Specifically, we will study the issues distributed data design, data fragmentation, data replication, distributed fault tolerance/recovery. We will also study the use of Hadoop, Pig, Hive, and HBase in dealing big data sets and use real life examples of how these open source software are used. Prerequisite: SEIS 630.

This course covers the technical concepts of managing vast amount of unstructured, semi-structured and structured data, collectively called "Big Data". Due to the sheer volume of Big Data, traditional approaches to managing databases does not work well for Big data and does not perform as expected. A distributed architecture for both the file system and the operating system is needed. Some of the techniques used in managing Big Data have the origins in the research and the developments that have been going on for decades in the area of parallel processing and distributed database management systems. This course focuses on why big data sets must be distributed and the issues that distribution introduces. The basic concepts on which distributed data sets are handled are discussed first. Once a foundation is defined, software tools that we use to work with big data sets are studied to provide an in-depth analysis of the concepts introduced. Specifically, we will study the issues distributed data design, data fragmentation, data replication, distributed fault tolerance/recovery. We will also study the use of Hadoop, Pig, Hive, and HBase in dealing big data sets and use real life examples of how these open source software are used. Prerequisite: SEIS 630.

This course covers the technical concepts of managing vast amount of unstructured, semi-structured and structured data, collectively called "Big Data". Due to the sheer volume of Big Data, traditional approaches to managing databases does not work well for Big data and does not perform as expected. A distributed architecture for both the file system and the operating system is needed. Some of the techniques used in managing Big Data have the origins in the research and the developments that have been going on for decades in the area of parallel processing and distributed database management systems. This course focuses on why big data sets must be distributed and the issues that distribution introduces. The basic concepts on which distributed data sets are handled are discussed first. Once a foundation is defined, software tools that we use to work with big data sets are studied to provide an in-depth analysis of the concepts introduced. Specifically, we will study the issues distributed data design, data fragmentation, data replication, distributed fault tolerance/recovery. We will also study the use of Hadoop, Pig, Hive, and HBase in dealing big data sets and use real life examples of how these open source software are used. Prerequisite: SEIS 630.

The students receive an introduction to real-time systems, including, real-time operating systems, real-time scheduling and concurrency control, reliability and fault tolerance in real-time systems, real-time communication and clock synchronization, and real-time system design methodology and pitfalls. Prerequisite: SEIS610

This course is designed for students to be exposed to technologies and best practices that help them understand high level concepts and the supporting technologies that make up the Internet of Things. Additionally, students will use their hands to build a prototype of a real product and put it into practice to collect and analyze data. This will give them the foundation to further explore creating their own product in the future or join an existing IoT focused company. Most importantly, at the end of the course students will be able to understand the broad concepts and speak intelligently on how the Internet of Things will have an impact on our lives today and in the future. Prerequisite: SEIS 601 or SEIS 602 or SEIS 603 or SEIS 635

Machine Learning and Deep Learning are different forms of artificial intelligence. Their capability in extracting complex patterns from big data have been significantly expanded recently. Topics in both learning processes discussed in this course include: (1) the fundamental learning methods used by machines, (2) problems, solutions, and advantages of deep learning and machine learning, (3) automatic self-learning from huge amounts of unprocessed raw business data, (4) transferring existing learning models for new business problems, (5) case study of predicting abnormal time-series events from business or healthcare data, and (6) scenario detection in business data and images / videos. Prerequisite: SEIS 631.

Machine Learning and Deep Learning are different forms of artificial intelligence. Their capability in extracting complex patterns from big data have been significantly expanded recently. Topics in both learning processes discussed in this course include: (1) the fundamental learning methods used by machines, (2) problems, solutions, and advantages of deep learning and machine learning, (3) automatic self-learning from huge amounts of unprocessed raw business data, (4) transferring existing learning models for new business problems, (5) case study of predicting abnormal time-series events from business or healthcare data, and (6) scenario detection in business data and images / videos. Prerequisite: SEIS 631.

Available to only MS and MSDD students. MS and MSDD students may choose to register for SEIS776-777 and complete a research or software development project under the supervision of a full-time GPS faculty member. Students cannot receive credit for SEIS776 without completing SEIS777. Prerequisite: SEIS625 and permission of the department.

Available to only MS and MSDD students. MS and MSDD Students may choose to register for SEIS776-777 and complete a research or software development project under the supervision of a full-time GPS faculty member. Students cannot receive credit for SEIS777 without completing the prerequisite SEIS776. Prerequisite: SEIS776

These internships are for students who do not have two years of software development experience prior to entering the program. These courses may be taken by MSS students, but will not count as part of the degree requirements. Prerequisite: permission of the department

This is an introductory software development course, with focus on fundamental and foundational concepts. These concepts include general problem solving and algorithm creation techniques, data types, constants, variables and expressions, Boolean, control flow, and object-oriented concepts. Applying these concepts, we implement programs using the Python language. We will examine its use as both an interpreted and a compiled language, working with data types such as numbers, strings, lists, dictionaries, and sets. Students will learn how to apply Python in managing data. No previous programming experience in Python or any other programming language is required.

This is an introductory software development course, with focus on fundamental and foundational concepts. These concepts include general problem solving and algorithm creation techniques, data types, constants, variables and expressions, Boolean, control flow, and object-oriented concepts. Applying these concepts, we implement programs using the Python language. We will examine its use as both an interpreted and a compiled language, working with data types such as numbers, strings, lists, dictionaries, and sets. Students will learn how to apply Python in managing data. No previous programming experience in Python or any other programming language is required.

Teaches the fundamentals of written and oral communication as practiced by IT professionals. The course emphasizes product descriptions, instructions, informative and persuasive oral presentations, the role of graphics, and teamwork on projects. In addition, the course introduces managerial strategies and tactics, such as planning and evaluation, which are critical for meeting an intended audience's needs. Recently, the scope of this course was expanded to include communication issues related to business analysis and project management. After completing this course, students will be more confident about their ability to communicate effectively in the workplace. For MS Software students, this course must be completed before exceeding 12 credits in Software Engineering, Software Management, and Information Technology.

This is a survey course covering software engineering concepts, techniques, and methodologies. Topics covered include software engineering; software process and its difficulties; software life-cycle models; software metrics; project planning including cost estimation; design methodologies including structured design, and object-oriented design; software testing; and software maintenance. A brief review of data structures is included. Prerequisite: SEIS 601 or SEIS 603. SEIS 610 can be taken concurrently with SEIS 601 or SEIS 603.

Students gain a management perspective and a development process for planning, estimating, and controlling software development. They learn to develop a well-defined plan before beginning any software development effort; how to handle changes during the execution of the plan; how to incorporate quality criteria in the development cycle; and how to use methods to keep the project on track. Included in the course is the use of project management software and simulation software in the development and control of the project plan.(If credit is received for this course students cannot receive credit for SEIS621 [CSIS526].) Prerequisite: SEIS610

This course focuses on database management system concepts, database design, and implementation. Conceptual data modeling using Entity Relationships (ER) is used to capture the requirements of a database design. Relational model concepts are introduced and mapping from ER to relational model is discussed. Logical database design (Normalization) and indexing strategies are also discussed to aide system performance. Relational Algebra and Structured Query Language (SQL) are used to work with a database. From a system perspective, the course focuses on query optimization and execution strategies, concurrency control, locking, deadlocks and database back-up and recovery concepts. Database security and authorization are also discussed. Students will use Oracle and/or SQL Server to design a database and complete an application using SQL as their project. Prerequisite: SEIS 610. SEIS 630 may be taken concurrently with SEIS610.

This course provides a broad introduction to the subject of data analysis by introducing common techniques that are essential for analyzing and deriving meaningful information from datasets. In particular, the course will focus on relevant methods for performing data collection, representation, transformation, and data-driven decision making. Students will also develop proficiency in the widely used R language which will be used throughout the course to reinforce the topics covered. Prerequisite: SEIS 601 or SEIS 603.

The course provides an introduction to concepts and techniques used in field of data analytics and visualization. Data analytics is defined to be the science of examining raw data with the purpose of discovering knowledge by analyzing current and historical facts. Insights discovered from the data are then communicated using data visualization. Topics covered in the course include predictive analytics, pattern discovery, and best practices for creating effective data visualizations. Through practical application of the above topics, students will also develop proficiency in using analytics tools.

The course provides an introduction to concepts and techniques used in field of data analytics and visualization. Data analytics is defined to be the science of examining raw data with the purpose of discovering knowledge by analyzing current and historical facts. Insights discovered from the data are then communicated using data visualization. Topics covered in the course include predictive analytics, pattern discovery, and best practices for creating effective data visualizations. Through practical application of the above topics, students will also develop proficiency in using analytics tools.

This course introduces the fundamentals of web application design, and development using open standards. Students will learn how to create interactive database- driven media rich web applications. Students will learn both the technical and design aspects of creating effective web applications using a variety of technologies and development tools (mostly open source tools where appropriate). The course culminates in a term project that brings together elements of design and technology into a functioning web application. This is an introductory course and no prior knowledge or experience of web design or web development is required. Prerequisite: (SEIS 601 or SEIS 603) and SEIS 610.

Machine Learning builds computational systems that learn from and adapt to the data presented to them. It has become one of the essential pillars in information technology today and provides a basis for several applications we use daily in diverse domains such as engineering, medicine, finance, and commerce. This course covers widely used supervised and unsupervised machine learning algorithms used in industry in technical depth, discussing both the theoretical underpinnings of machine learning techniques and providing hands-on experience in implementing them. Additionally, students will also learn to evaluate effectiveness and avoid common pitfalls in applying machine learning to a given problem. Prerequisite: Minimum grade of C- in SEIS 631

These internships are for students who do not have two years of software development experience prior to entering the program. These courses may be taken by MSS students, but will not count as part of the degree requirements. Prerequisite: permission of the department

The primary objective of this course is to provide the experienced programmer with knowledge of and experience with fundamental data structures and algorithms used in software design and development. The secondary objective is to give a fast-paced introduction to the Java programming language. Students will write multiple programs in Java, both to become familiar with Java and to apply data structure concepts. Prerequisite: none

This is an introductory software development course, with focus on intermediate-level fundamental and foundational concepts. These concepts include abstract data types such as lists, stacks, queues, and trees/graphs, as well as some of their associated algorithms such as insertion, deletion, searching, sorting, and traversals. Canonical implementations as well as framework supplied implementation alternatives (such as the JDK or other framework alternatives) will be explored and used. To apply the lecture concepts, we will implement software using the Java programming language and explore some of the tools used by software developers. There are many types of tools to be considered, such as integrated development environments (IDEs e.g. eclipse), tools for managing software build, configuration, and version control (e.g. Ant/Maven, Git), tools for testing and debugging (e.g. JUnit, LogBack/Log4J/SLF4J) and other tools used by developers to understand the code they are working with (e.g. GrepCode). In addition, we will discuss intermediate concepts, issues, and techniques including an introduction to concurrency issues, and development practices such as refactoring, logging, and debugging. Prerequisite: SEIS 601 or an equivalent understanding of Foundational Software Development concepts and the ability to use and understand the Java programming language is required.

This is an introductory software development course, with focus on fundamental and foundational concepts. These concepts include general problem solving and algorithm creation techniques, data types, constants, variables and expressions, Boolean, control flow, and object-oriented concepts. Applying these concepts, we implement programs using the Python language. We will examine its use as both an interpreted and a compiled language, working with data types such as numbers, strings, lists, dictionaries, and sets. Students will learn how to apply Python in managing data. No previous programming experience in Python or any other programming language is required.

This is an introductory software development course, with focus on fundamental and foundational concepts. These concepts include general problem solving and algorithm creation techniques, data types, constants, variables and expressions, Boolean, control flow, and object-oriented concepts. Applying these concepts, we implement programs using the Python language. We will examine its use as both an interpreted and a compiled language, working with data types such as numbers, strings, lists, dictionaries, and sets. Students will learn how to apply Python in managing data. No previous programming experience in Python or any other programming language is required.

This is an introductory software development course, with focus on fundamental and foundational concepts. These concepts include general problem solving and algorithm creation techniques, data types, constants, variables and expressions, Boolean, control flow, and object-oriented concepts. Applying these concepts, we implement programs using the Python language. We will examine its use as both an interpreted and a compiled language, working with data types such as numbers, strings, lists, dictionaries, and sets. Students will learn how to apply Python in managing data. No previous programming experience in Python or any other programming language is required.

Teaches the fundamentals of written and oral communication as practiced by IT professionals. The course emphasizes product descriptions, instructions, informative and persuasive oral presentations, the role of graphics, and teamwork on projects. In addition, the course introduces managerial strategies and tactics, such as planning and evaluation, which are critical for meeting an intended audience's needs. Recently, the scope of this course was expanded to include communication issues related to business analysis and project management. After completing this course, students will be more confident about their ability to communicate effectively in the workplace. For MS Software students, this course must be completed before exceeding 12 credits in Software Engineering, Software Management, and Information Technology.

Teaches the fundamentals of written and oral communication as practiced by IT professionals. The course emphasizes product descriptions, instructions, informative and persuasive oral presentations, the role of graphics, and teamwork on projects. In addition, the course introduces managerial strategies and tactics, such as planning and evaluation, which are critical for meeting an intended audience's needs. Recently, the scope of this course was expanded to include communication issues related to business analysis and project management. After completing this course, students will be more confident about their ability to communicate effectively in the workplace. For MS Software students, this course must be completed before exceeding 12 credits in Software Engineering, Software Management, and Information Technology.

This is a survey course covering software engineering concepts, techniques, and methodologies. Topics covered include software engineering; software process and its difficulties; software life-cycle models; software metrics; project planning including cost estimation; design methodologies including structured design, and object-oriented design; software testing; and software maintenance. A brief review of data structures is included. Prerequisite: SEIS 601 or SEIS 603. SEIS 610 can be taken concurrently with SEIS 601 or SEIS 603.

This is a survey course covering software engineering concepts, techniques, and methodologies. Topics covered include software engineering; software process and its difficulties; software life-cycle models; software metrics; project planning including cost estimation; design methodologies including structured design, and object-oriented design; software testing; and software maintenance. A brief review of data structures is included. Prerequisite: SEIS 601 or SEIS 603. SEIS 610 can be taken concurrently with SEIS 601 or SEIS 603.

This is a survey course covering software engineering concepts, techniques, and methodologies. Topics covered include software engineering; software process and its difficulties; software life-cycle models; software metrics; project planning including cost estimation; design methodologies including structured design, and object-oriented design; software testing; and software maintenance. A brief review of data structures is included. Prerequisite: SEIS 601 or SEIS 603. SEIS 610 can be taken concurrently with SEIS 601 or SEIS 603.

Students gain a management perspective and a development process for planning, estimating, and controlling software development. They learn to develop a well-defined plan before beginning any software development effort; how to handle changes during the execution of the plan; how to incorporate quality criteria in the development cycle; and how to use methods to keep the project on track. Included in the course is the use of project management software and simulation software in the development and control of the project plan.(If credit is received for this course students cannot receive credit for SEIS621 [CSIS526].) Prerequisite: SEIS610

This course builds on the project management process through the application of Software Quality Engineering concepts (Quality Assurance, Control and Testing). Students will work through a semester project in which they will think like a Software Quality Engineer. Practical tools and techniques will be applied toward the management and improvement of the quality of a software product and the development process. (If credit is received for this course, students cannot receive credit for SEIS 621 [CSIS526].) Prerequisite: SEIS625. SEIS 626 can be taken concurrently with SEIS 625.

This course presents a software planning and quality perspective that guides the selection of tools and application of techniques needed for the successful completion software development projects. A successful software project must manage many different, integrated activities. These activities include software development lifecycle tasks such as requirements gathering, software design, and code implementation. Many other activities also need to be planned and managed, such as project scope, schedule, and cost. In any successful software project, when issues arise (e.g. the requirements change, a defect in the software is discovered, scheduled activities do not go as planned, etc.) they need to be prioritized and appropriately addressed. To minimize the impact of software quality issues, software testing and quality improvement activities need to be planned, executed and coordinated. The purpose of this course is to learn the foundational concepts and practices needed to produce software that is completed on time, within budget, and with the necessary scope and quality required. While software development activities are covered in other courses, this course will focus more on the software planning and testing activities. Project management topics covered include: integration management, scope management, time management, cost management, and quality management from a software planning perspective. Software testing and quality topics covered include: testing terms and concepts, lower-level testing (e.g. unit and integration testing), higher-level testing (e.g. system and acceptance testing), test automation, continuous process improvement and quality assurance. Prerequisite: SEIS 610 AND SEIS 601/603

This course focuses on database management system concepts , database design, and implementation. Conceptual data modeling using Entity Relationships (ER) is used to capture the requirements of a database design. Relational model concepts are introduced and mapping from ER to relational model is discussed. Logical database design (Normalization) and indexing strategies are also discussed to aide system performance. Relational Algebra and Structured Query Language (SQL) are used to work with a database. From a system perspective, the course focuses on query optimization and execution strategies, concurrency control, locking, deadlocks and database back up and recovery concepts. Database security and authorization are also discussed. Students will use Oracle and/or SQL Server to design a database and complete an application using SQL as their project. Prerequisite: SEIS 610. SEIS 630 may be taken concurrently with SEIS610.

This course focuses on database management system concepts , database design, and implementation. Conceptual data modeling using Entity Relationships (ER) is used to capture the requirements of a database design. Relational model concepts are introduced and mapping from ER to relational model is discussed. Logical database design (Normalization) and indexing strategies are also discussed to aide system performance. Relational Algebra and Structured Query Language (SQL) are used to work with a database. From a system perspective, the course focuses on query optimization and execution strategies, concurrency control, locking, deadlocks and database back up and recovery concepts. Database security and authorization are also discussed. Students will use Oracle and/or SQL Server to design a database and complete an application using SQL as their project. Prerequisite: SEIS 610. SEIS 630 may be taken concurrently with SEIS610.

This course focuses on database management system concepts , database design, and implementation. Conceptual data modeling using Entity Relationships (ER) is used to capture the requirements of a database design. Relational model concepts are introduced and mapping from ER to relational model is discussed. Logical database design (Normalization) and indexing strategies are also discussed to aide system performance. Relational Algebra and Structured Query Language (SQL) are used to work with a database. From a system perspective, the course focuses on query optimization and execution strategies, concurrency control, locking, deadlocks and database back up and recovery concepts. Database security and authorization are also discussed. Students will use Oracle and/or SQL Server to design a database and complete an application using SQL as their project. Prerequisite: SEIS 610. SEIS 630 may be taken concurrently with SEIS610.

This course provides a broad introduction to the subject of data analysis by introducing common techniques that are essential for analyzing and deriving meaningful information from datasets. In particular, the course will focus on relevant methods for performing data collection, representation, transformation, and data-driven decision making. Students will also develop proficiency in the widely used R language which will be used throughout the course to reinforce the topics covered. Prerequisite: SEIS 601 or SEIS 603 (may be taken concurrently).

This course provides a broad introduction to the subject of data analysis by introducing common techniques that are essential for analyzing and deriving meaningful information from datasets. In particular, the course will focus on relevant methods for performing data collection, representation, transformation, and data-driven decision making. Students will also develop proficiency in the widely used R language which will be used throughout the course to reinforce the topics covered. Prerequisite: SEIS 601 or SEIS 603 (may be taken concurrently).

The course provides an introduction to concepts and techniques used in field of data analytics and visualization. Data analytics is defined to be the science of examining raw data with the purpose of discovering knowledge by analyzing current and historical facts. Insights discovered from the data are then communicated using data visualization. Topics covered in the course include predictive analytics, pattern discovery, and best practices for creating effective data visualizations. Through practical application of the above topics, students will also develop proficiency in using analytics tools.

The course provides an introduction to concepts and techniques used in field of data analytics and visualization. Data analytics is defined to be the science of examining raw data with the purpose of discovering knowledge by analyzing current and historical facts. Insights discovered from the data are then communicated using data visualization. Topics covered in the course include predictive analytics, pattern discovery, and best practices for creating effective data visualizations. Through practical application of the above topics, students will also develop proficiency in using analytics tools.

The course provides an introduction to concepts and techniques used in field of data analytics and visualization. Data analytics is defined to be the science of examining raw data with the purpose of discovering knowledge by analyzing current and historical facts. Insights discovered from the data are then communicated using data visualization. Topics covered in the course include predictive analytics, pattern discovery, and best practices for creating effective data visualizations. Through practical application of the above topics, students will also develop proficiency in using analytics tools.

The course provides an introduction to concepts and techniques used in field of data analytics and visualization. Data analytics is defined to be the science of examining raw data with the purpose of discovering knowledge by analyzing current and historical facts. Insights discovered from the data are then communicated using data visualization. Topics covered in the course include predictive analytics, pattern discovery, and best practices for creating effective data visualizations. Through practical application of the above topics, students will also develop proficiency in using analytics tools.

This course covers basic object-oriented techniques for specifying, designing, and implementing software systems. Iterative development methodologies are emphasized. The Unified Modeling Language (UML) is used as a notational system for capturing the development process artifacts. Students will gain experience with a software tool for creating UML diagrams. Other topics include use cases, class discovery and domain modeling, responsibility-driven design, basic design patterns, software class design, converting designs to code, object-oriented testing, packaging, deployment, along with intermediate Java topics relevant to system implementation. This course also introduces ideas in functional and parallel programming. Students will work on an object-oriented team project, apply concepts and techniques to describe and create a working software system. Prerequisite: SEIS 602 and SEIS 610.

The objective of this course is to introduce the business analyst roles and responsibilities and knowledge areas such as enterprise analysis, requirements planning and measurement, requirements elicitation, requirements communication, requirements analysis and documentation, solution assessment and validation, business analysis fundamentals including tools and techniques. Prerequisite: SEIS 610. SEIS 636 may be taken concurrently with SEIS 610.

An introduction to the concepts and principles involved in operating systems design is provided. Topics in the course include computer-system structures, operating-systems structures, job and process scheduling, process synchronization, deadlock, memory management, virtual memory, file systems, input/output systems, distributed system structures, distributed file systems, protection, system security, and case studies of operating systems. Prerequisite: SEIS610

The ability to identify legal issues being raised by computer technology and guidelines for their solution is a continuing requirement for competence in the field of software design and development. Consequently, the students are provided with an examination of a broad range of legal issues in technology including patent law, copyright law, trade secrets, trademarks, contracts, ownership issues in software development, and computer failures and related torts.

This course will provide a practical overview of Enterprise Resource Planning, connecting the academic and even marketing elements with real-world, case-based issues as encountered by business and other organizations. ERP has become a critical strategic consideration for many companies, and the course will look at best-practice implementations at leading companies internationally. Course will examine best practice usage of ERP in a global distributed computing environment. In addition, it will look into trends relating to critical issues such as Cloud and Big Data. Professionals currently working in the IT organizations or future IT professionals will benefit from this course. Prerequisite: Minimum grade of C- in SEIS 610

This course will provide the foundation of information technology security, including authentication, authorization, access management, physical security, network security (firewalls, intrusion detection), application security (software and database), security regulations, and disaster recovery. We will explore social engineering and other human factors and the impact to security. There will be an emphasis on local area networking (LAN) and Internet architecture and protocols, including TCP/IP and the OSI layers. We study protocol details, the way they relate and interact with each other, and how they are applied in real systems. Prerequisite: SEIS610

This course will provide the foundation of information technology security, including authentication, authorization, access management, physical security, network security (firewalls, intrusion detection), application security (software and database), security regulations, and disaster recovery. We will explore social engineering and other human factors and the impact to security. There will be an emphasis on local area networking (LAN) and Internet architecture and protocols, including TCP/IP and the OSI layers. We study protocol details, the way they relate and interact with each other, and how they are applied in real systems. Prerequisite: SEIS610

This survey course covers IT delivery, operations, and management in both theory and practice, including Business and consumer needs for IT value, IT infrastructure, cloud, continuous integration/delivery, IT product and service management, work and task management, operations management, organization and culture, project management, process management, change and incident management, IT governance, risk, security, and compliance business continuity, enterprise information and e-records management, enterprise IT architecture and portfolio management, IT management frameworks including ITIL, COBIT, and IT4IT, Agile and Lean influences including Kanban, Scrum, and DevOps, Continuous improvement and IT. Prerequisite: SEIS 610

This course covers the engineering and design of IT infrastructure, focusing on cloud-scale distributed systems and modern DevOps practices. IT infrastructure deployment practices are rapidly changing as organizations build "Infrastructure as code" and adopt cloud computing platforms. We will examine the theory behind these modern practices and the real-world implementation challenges faced by IT organizations. While the lessons will cover a number of theoretical concepts, we will primarily learn by doing. Students will gain hands-on experience with several widely-adopted IT platforms including Github, AWS, and Docker. Prerequisite: SEIS 610

This course covers the engineering and design of IT infrastructure, focusing on cloud-scale distributed systems and modern DevOps practices. IT infrastructure deployment practices are rapidly changing as organizations build "Infrastructure as code" and adopt cloud computing platforms. We will examine the theory behind these modern practices and the real-world implementation challenges faced by IT organizations. While the lessons will cover a number of theoretical concepts, we will primarily learn by doing. Students will gain hands-on experience with several widely-adopted IT platforms including Github, AWS, and Docker. Prerequisite: SEIS 610

This course covers the engineering and design of IT infrastructure, focusing on cloud-scale distributed systems and modern DevOps practices. IT infrastructure deployment practices are rapidly changing as organizations build "Infrastructure as code" and adopt cloud computing platforms. We will examine the theory behind these modern practices and the real-world implementation challenges faced by IT organizations. While the lessons will cover a number of theoretical concepts, we will primarily learn by doing. Students will gain hands-on experience with several widely-adopted IT platforms including Github, AWS, and Docker. Prerequisite: SEIS 610

This course covers both the engineering and human issues in computer security and the tension between them. The engineering issues include cryptography concepts, building blocks (conventional and public key, digital signatures, certificates, certificate authorities), algorithms, protocols (authentication, key distribution, SSL), biometrics, network security (firewalls, intrusion detection systems, wireless), email protection, malware (viruses, worms, trojans), and applications. This course emphasizes on the application security features of the Java programming platform. The human issues include social engineering, user password management, and computer crime. We also cover weekly current events in computer security. Prerequisites: SEIS 601 and SEIS 610.

This course explores the issues surrounding computers that have been used in connection with criminal or other improper activity, or that have been the direct target of a crime. While the focus of this course is on the computer science issues, the law enforcement perspective is also covered. Topics include disk file system structures (hiding and recovery techniques), networking and email considerations, forensic data collection, evidence preservation and authentication, collection and analysis tools, legal, and privacy issues.

In order to build and maintain a successful data warehouse, it is important to understand all of its components and how they fit together. This course will cover data warehouse and data mart lifecycle phases while focusing on infrastructure, design, and management issues. The course project will provide an opportunity to for hands-on experience with some of the available tools and technologies. Topics include: differences between data warehouses and traditional database systems (OLTP), multidimensional analysis and design, building data warehouses using "cube" vs. RDBMS (Star schema, etc.), planning for data warehouses, extraction transformation and loading (ETL), online analytical processing (OLAP), data mining, quality and cleansing, common pitfalls to avoid when designing, implementing and maintaining data warehouse environments, and the impact of new technologies (data webhouse, clickstream, XML). Prerequisite: SEIS630

In order to build and maintain a successful data warehouse, it is important to understand all of its components and how they fit together. This course will cover data warehouse and data mart lifecycle phases while focusing on infrastructure, design, and management issues. The course project will provide an opportunity to for hands-on experience with some of the available tools and technologies. Topics include: differences between data warehouses and traditional database systems (OLTP), multidimensional analysis and design, building data warehouses using "cube" vs. RDBMS (Star schema, etc.), planning for data warehouses, extraction transformation and loading (ETL), online analytical processing (OLAP), data mining, quality and cleansing, common pitfalls to avoid when designing, implementing and maintaining data warehouse environments, and the impact of new technologies (data webhouse, clickstream, XML). Prerequisite: SEIS630

We can keep improving the quality and safety of health care if the rapid growth of medical knowledge and medical data can be efficiently analyzed and effectively shared. This course will discuss processes in healthcare analytics, including data acquisition, storage, retrieval, management, and analysis of healthcare data in heterogeneous formats (i.e. numeric health records, medical text, and medical images). Major topics include: (1) analyzing patient records and identifying frequent medical sequences for treatment and prevention, (2) evaluating medical text and generating aggregated summary based on hierarchical medical concepts, (3) retrieving information from different types of medical images, (4) building clinic decision support systems to detect possible medical mistakes, and (5) comparing brain connectivity graphs from patients with different neurological conditions. Amazon Cloud will be used to analyze multi-million records of numeric and text data. Prerequisite: SEIS 630

This course covers emerging big data architectures, predominately Hadoop and related technologies that deal with large amounts of unstructured and semi-structured data. Topics include operating system, architecture, security, big data structure and storage. The primary applications discussed in this class focus on information retrieval, specifically text processing techniques and algorithms, such as parsing, stemming, compression, and string searching. Information retrieval is also a great case study for broader issues in building systems that scale and perform, so we discuss associated issues in data structures, algorithms, computational complexity, and measurement. Prerequisite: (SEIS 601 or SEIS 603) and SEIS 630 (can be taken concurrently).

This course covers emerging big data architectures, predominately Hadoop and related technologies that deal with large amounts of unstructured and semi-structured data. Topics include operating system, architecture, security, big data structure and storage. The primary applications discussed in this class focus on information retrieval, specifically text processing techniques and algorithms, such as parsing, stemming, compression, and string searching. Information retrieval is also a great case study for broader issues in building systems that scale and perform, so we discuss associated issues in data structures, algorithms, computational complexity, and measurement. Prerequisite: (SEIS 601 or SEIS 603) and SEIS 630 (can be taken concurrently).

This course covers the technical concepts of managing vast amount of unstructured, semi-structured and structured data, collectively called "Big Data". Due to the sheer volume of Big Data, traditional approaches to managing databases does not work well for Big data and does not perform as expected. A distributed architecture for both the file system and the operating system is needed. Some of the techniques used in managing Big Data have the origins in the research and the developments that have been going on for decades in the area of parallel processing and distributed database management systems. This course focuses on why big data sets must be distributed and the issues that distribution introduces. The basic concepts on which distributed data sets are handled are discussed first. Once a foundation is defined, software tools that we use to work with big data sets are studied to provide an in-depth analysis of the concepts introduced. Specifically, we will study the issues distributed data design, data fragmentation, data replication, distributed fault tolerance/recovery. We will also study the use of Hadoop, Pig, Hive, and HBase in dealing big data sets and use real life examples of how these open source software are used. Prerequisite: SEIS 630 (Database Management Systems and Design) or override is required. Familiarity with Java is strongly recommended. Prerequisite: SEIS 630.

This course covers the technical concepts of managing vast amount of unstructured, semi-structured and structured data, collectively called "Big Data". Due to the sheer volume of Big Data, traditional approaches to managing databases does not work well for Big data and does not perform as expected. A distributed architecture for both the file system and the operating system is needed. Some of the techniques used in managing Big Data have the origins in the research and the developments that have been going on for decades in the area of parallel processing and distributed database management systems. This course focuses on why big data sets must be distributed and the issues that distribution introduces. The basic concepts on which distributed data sets are handled are discussed first. Once a foundation is defined, software tools that we use to work with big data sets are studied to provide an in-depth analysis of the concepts introduced. Specifically, we will study the issues distributed data design, data fragmentation, data replication, distributed fault tolerance/recovery. We will also study the use of Hadoop, Pig, Hive, and HBase in dealing big data sets and use real life examples of how these open source software are used. Prerequisite: SEIS 630 (Database Management Systems and Design) or override is required. Familiarity with Java is strongly recommended. Prerequisite: SEIS 630.

This course will introduce the concepts of embedded processor design. An overview of the most popular embedded processors such as the ARM, Analog Devices (ARM7 Cortex, Blackfin, and Sharc) TI (MSP430, 55x, ect), Microchip (PIC), Freescale (RS08, Power Core, M Core, etc), Atmel (AVR), NXP (ARM9, 8051, etc) will be covered. the strengths and weakness of each family of processors and where they are used will also be covered. The use of assemblers and simulators, accelerometers, A/D, D/A converters, signal synthesizers and serial communication interfaces will be covered in detail. The students will have lab time with ARM circuit Boards. Blackfin circuit boards are also availalbe for the student to use. An introduction to Digital Signal Processing in the time domain will be presented from a firmware engineers perspective, (time domain with no calculus). Prerequisite: SEIS 610

This course is designed for students to be exposed to technologies and best practices that help them understand high level concepts and the supporting technologies that make up the Internet of Things. Additionally, students will use their hands to build a prototype of a real product and put it into practice to collect and analyze data. This will give them the foundation to further explore creating their own product in the future or join an existing IoT focused company. Most importantly, at the end of the course students will be able to understand the broad concepts and speak intelligently on how the Internet of Things will have an impact on our lives today and in the future. Prerequisite: SEIS 601 or SEIS 603.

This course introduces the fundamentals of web application design, and development using open standards. Students will learn how to create interactive database- driven media rich web applications. Students will learn both the technical and design aspects of creating effective web applications using a variety of technologies and development tools (mostly open source tools where appropriate). The course culminates in a term project that brings together elements of design and technology into a functioning web application. This is an introductory course and no prior knowledge or experience of web design or web development is required. Prerequisite: SEIS 610.

Machine Learning builds computational systems that learn from and adapt to the data presented to them. It has become one of the essential pillars in information technology today and provides a basis for several applications we use daily in diverse domains such as engineering, medicine, finance, and commerce. This course covers widely used supervised and unsupervised machine learning algorithms used in industry in technical depth, discussing both the theoretical underpinnings of machine learning techniques and providing hands-on experience in implementing them. Additionally, students will also learn to evaluate effectiveness and avoid common pitfalls in applying machine learning to a given problem. Prerequisite: Minimum grade of C- in SEIS 631

Machine Learning builds computational systems that learn from and adapt to the data presented to them. It has become one of the essential pillars in information technology today and provides a basis for several applications we use daily in diverse domains such as engineering, medicine, finance, and commerce. This course covers widely used supervised and unsupervised machine learning algorithms used in industry in technical depth, discussing both the theoretical underpinnings of machine learning techniques and providing hands-on experience in implementing them. Additionally, students will also learn to evaluate effectiveness and avoid common pitfalls in applying machine learning to a given problem. Prerequisite: Minimum grade of C- in SEIS 631

Artificial Intelligence has made significant strides in recent times and has become ubiquitous in the modern world, impacting our lives in different ways. By harnessing the power of deep neural networks, it is now possible to build real-world intelligent applications that outperform human precision in certain tasks. This course provides a broad coverage of AI techniques with a focus on industry application. Major topics covered in this course include: (1) how deep neural networks learn their intelligence, (2) self-learning from raw data, (3) common training problems and solutions, (4) transferring learning from existing AI systems, (5) training AI systems for machine visions with high accuracy, and (6) training time-series AI systems for recognizing sequential patterns. Students will have hands-on exercises for building efficient AI systems. Prerequisite: SEIS 763

Artificial Intelligence has made significant strides in recent times and has become ubiquitous in the modern world, impacting our lives in different ways. By harnessing the power of deep neural networks, it is now possible to build real-world intelligent applications that outperform human precision in certain tasks. This course provides a broad coverage of AI techniques with a focus on industry application. Major topics covered in this course include: (1) how deep neural networks learn their intelligence, (2) self-learning from raw data, (3) common training problems and solutions, (4) transferring learning from existing AI systems, (5) training AI systems for machine visions with high accuracy, and (6) training time-series AI systems for recognizing sequential patterns. Students will have hands-on exercises for building efficient AI systems. Prerequisite: SEIS 763

This course introduces students to using object-oriented architecture and design patterns in the development of high quality, reliable software systems. Patterns and architectures can have a significant effect on the time to deliver systems and the maintainability and quality of systems. Current object-oriented development methods and tools will be used to describe and implement software designs that are based on patterns. Students will learn the abstraction skills required to discover, document, and patterns and architectures. Java will be used. Prerequisite: SEIS635.

Available to Software Engineering, Software Management and Information Technology students. These students may choose to register for SEIS776-777 and complete a research or software development project under the supervision of a full-time GPS faculty member. Students cannot receive credit for SEIS776 without completing SEIS777. Prerequisite: SEIS 627 and permission of the department.

Available to Software Engineering, Software Management and Information Technology students. These students may choose to register for SEIS776-777 and complete a research or software development project under the supervision of a full-time GPS faculty member. Students cannot receive credit for SEIS776 without completing SEIS777. Prerequisite: SEIS 627 and permission of the department.

A work oriented/internship opportunity to experience U.S. software skills in a real-world setting for students who seek on the job experience in the fields of Information Technology, Data Science, Software Engineering and Software Management. May be taken three times for credit.