Course Schedules

If no courses are currently offered, none will be displayed.

To register for classes online, please use Murphy Online

Summer 2020 Courses

Course - Section Title Days Time Location
SEIS 603 - 01 Found. Software Dev-Python - T - R - - - 1745 - 2100

Days of Week:

- T - R - - -

Time of Day:

1745 - 2100

Location:

Course Registration Number:

30355 (View in ClassFinder)

Credit Hours:

3

Instructor:

Damodar Chetty

[Students are expected to attend virtual class sessions and participate in online activities during the scheduled evening class times.] 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.

Schedule Details

Location Time Day(s)
SEIS 603 - 02 Found. Software Dev-Python M - W - - - - 1745 - 2100

Days of Week:

M - W - - - -

Time of Day:

1745 - 2100

Location:

Course Registration Number:

30356 (View in ClassFinder)

Credit Hours:

3

Instructor:

Eric V. Level

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.

Schedule Details

Location Time Day(s)
SEIS 610 - 01 Software Engineering M - W - - - - 1745 - 2100

Days of Week:

M - W - - - -

Time of Day:

1745 - 2100

Location:

Course Registration Number:

30357 (View in ClassFinder)

Credit Hours:

3

Instructor:

Michael A. Dorin

[Students are expected to attend virtual class sessions and participate in online activities during the scheduled evening class times.] 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.

Schedule Details

Location Time Day(s)
SEIS 627 - 01 Software Planning & Testing M - W - - - - 1745 - 2100

Days of Week:

M - W - - - -

Time of Day:

1745 - 2100

Location:

Course Registration Number:

30359 (View in ClassFinder)

Credit Hours:

3

Instructor:

Syed H. Naqvi

[Students are expected to attend virtual class sessions and participate in online activities during the scheduled evening class times.] This course presents a software planning and quality perspective that guides the selection of tools and application of techniques needed for the successful completion of software development projects. A successful software project must manage many different, yet 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), and test automation. Agile Project and Product Management using Scrum will be introduced as an approach for directing these activities and laying the foundation for continuous process improvement and quality assurance. Prerequisite: SEIS 610 AND SEIS 601/603

Schedule Details

Location Time Day(s)
SEIS 630 - 01 Database Mgmt Systems & Design - T - R - - - 1745 - 2100

Days of Week:

- T - R - - -

Time of Day:

1745 - 2100

Location:

Course Registration Number:

30361 (View in ClassFinder)

Credit Hours:

3

Instructor:

Chi-Lung Chiang

[Students are expected to attend virtual class sessions and participate in online activities during the scheduled evening class times.] 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 aid system performance. Structured Query Language (SQL) is used to work with a database using the Oracle platform. The course also covers query optimization and execution strategies, concurrency control, locking, deadlocks, security, and backup/recovery concepts. Non-relational databases are also briefly introduced. Students will use Oracle and/or SQL Server to design and create a database using SQL as their project. Prerequisite: SEIS 610. SEIS 630 may be taken concurrently with SEIS610.

Schedule Details

Location Time Day(s)
SEIS 631 - 01 Foundations of Data Analysis M - W - - - - 1745 - 2100

Days of Week:

M - W - - - -

Time of Day:

1745 - 2100

Location:

Course Registration Number:

30365 (View in ClassFinder)

Credit Hours:

3

Instructor:

Aran W. Glancy

[Students are expected to attend virtual class sessions and participate in online activities during the scheduled evening class times.] 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).

Schedule Details

Location Time Day(s)
SEIS 632 - 01 Data Analytics & Visualization - T - R - - - 1745 - 2100

Days of Week:

- T - R - - -

Time of Day:

1745 - 2100

Location:

Course Registration Number:

30366 (View in ClassFinder)

Credit Hours:

3

Instructor:

Manjeet Rege

[Students are expected to attend virtual class sessions and participate in online activities during the scheduled evening class times.] 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.

Schedule Details

Location Time Day(s)
SEIS 632 - 02 Data Analytics & Visualization M - W - - - - 1745 - 2100

Days of Week:

M - W - - - -

Time of Day:

1745 - 2100

Location:

Course Registration Number:

30367 (View in ClassFinder)

Credit Hours:

3

Instructor:

Manjeet Rege

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.

Schedule Details

Location Time Day(s)
SEIS 737 - 01 Big Data Management - T - R - - - 1745 - 2100

Days of Week:

- T - R - - -

Time of Day:

1745 - 2100

Location:

Course Registration Number:

30595 (View in ClassFinder)

Credit Hours:

3

Instructor:

Asher Chaudhry

[Students are expected to attend virtual class sessions and participate in online activities during the scheduled evening class times.] 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. Prerequisites:(SEIS 601 or SEIS 603) and SEIS 630. May take concurrently with SEIS 736.

Schedule Details

Location Time Day(s)
SEIS 764 - 01 Artificial Intelligence M - W - - - - 1745 - 2100

Days of Week:

M - W - - - -

Time of Day:

1745 - 2100

Location:

Course Registration Number:

30368 (View in ClassFinder)

Credit Hours:

3

Instructor:

Chih Lai

[Students are expected to attend virtual class sessions and participate in online activities during the scheduled evening class times.] 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

Schedule Details

Location Time Day(s)

Fall 2020 Courses

Course - Section Title Days Time Location
SEIS 601 - 01 Found. of Software Dev-Java - T - - - - - 1745 - 2100 OSS 325

Days of Week:

- T - - - - -

Time of Day:

1745 - 2100

Location:

OSS 325

Course Registration Number:

43904 (View in ClassFinder)

Credit Hours:

3

Instructor:

Chi-Lung Chiang

The primary objective of this course is to introduce the Java programming language and how to use it in software development. Students will learn Java programming fundamentals, including variables, expressions, types, declarations, control structures for iteration and selection, classes and their objects, methods, and interfaces. A secondary objective is to give an introduction to fundamental techniques of software development, including work with debuggers, testing frameworks, and source code version control. Students will write multiple programs in Java, practicing these language elements and techniques and learning how to turn requirements into debugged, tested, and correct programs.No previous programming experience in Java, or any other programming language, is required.

Schedule Details

Location Time Day(s)
SEIS 603 - 01 Found. Software Dev-Python M - - - - - - 1745 - 2100 OSS 333

Days of Week:

M - - - - - -

Time of Day:

1745 - 2100

Location:

OSS 333

Course Registration Number:

45469 (View in ClassFinder)

Credit Hours:

3

Instructor:

Damodar Chetty

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.

Schedule Details

Location Time Day(s)
SEIS 603 - 02 Found. Software Dev-Python - T - - - - - 1745 - 2100 TMH 254

Days of Week:

- T - - - - -

Time of Day:

1745 - 2100

Location:

TMH 254

Course Registration Number:

45470 (View in ClassFinder)

Credit Hours:

3

Instructor:

Eric V. Level

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.

Schedule Details

Location Time Day(s)
SEIS 603 - 03 Found. Software Dev-Python - - W - - - - 1745 - 2100 OSS 428

Days of Week:

- - W - - - -

Time of Day:

1745 - 2100

Location:

OSS 428

Course Registration Number:

45471 (View in ClassFinder)

Credit Hours:

3

Instructor:

Eric V. Level

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.

Schedule Details

Location Time Day(s)
SEIS 603 - 04 Found. Software Dev-Python - - - R - - - 1745 - 2100 OSS 325

Days of Week:

- - - R - - -

Time of Day:

1745 - 2100

Location:

OSS 325

Course Registration Number:

45472 (View in ClassFinder)

Credit Hours:

3

Instructor:

Gary L. Berosik

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.

Schedule Details

Location Time Day(s)
SEIS 605 - 01 Technical Communications - T - - - - - 1745 - 2100 OSS 329

Days of Week:

- T - - - - -

Time of Day:

1745 - 2100

Location:

OSS 329

Course Registration Number:

43902 (View in ClassFinder)

Credit Hours:

3

Instructor:

Timothy K. Williams

Teaches the theory and practice of written and oral communication as used by IT professionals. Emphasizes technical writing style (the logical organization of detailed information written in direct, concise, and unambiguous language), collaboration, best practices when using visuals, and the ethical use of authoritative sources. Assignments include descriptions, instructions, informative and persuasive presentations, and a short, formal research paper. Also covers communication issues related to managerial strategies and tactics, business analysis, and project management. After completing this course, students will be more confident about their ability to communicate effectively in the workplace.

Schedule Details

Location Time Day(s)
SEIS 605 - 02 Technical Communications - - - R - - - 1745 - 2100 OSS 122

Days of Week:

- - - R - - -

Time of Day:

1745 - 2100

Location:

OSS 122

Course Registration Number:

44157 (View in ClassFinder)

Credit Hours:

3

Instructor:

Dorian G. Harvey

Teaches the theory and practice of written and oral communication as used by IT professionals. Emphasizes technical writing style (the logical organization of detailed information written in direct, concise, and unambiguous language), collaboration, best practices when using visuals, and the ethical use of authoritative sources. Assignments include descriptions, instructions, informative and persuasive presentations, and a short, formal research paper. Also covers communication issues related to managerial strategies and tactics, business analysis, and project management. After completing this course, students will be more confident about their ability to communicate effectively in the workplace.

Schedule Details

Location Time Day(s)
SEIS 610 - 01 Software Engineering M - - - - - - 1745 - 2100 SCH 127

Days of Week:

M - - - - - -

Time of Day:

1745 - 2100

Location:

SCH 127

Course Registration Number:

43903 (View in ClassFinder)

Credit Hours:

3

Instructor:

Michael A. Dorin

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.

Schedule Details

Location Time Day(s)
SEIS 610 - 02 Software Engineering - - - R - - - 1745 - 2100 OSS 127

Days of Week:

- - - R - - -

Time of Day:

1745 - 2100

Location:

OSS 127

Course Registration Number:

44213 (View in ClassFinder)

Credit Hours:

3

Instructor:

Bradford C. Armitage

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.

Schedule Details

Location Time Day(s)
SEIS 610 - 04 Software Engineering See Details * *

Days of Week:

See Details

Time of Day:

*

Location:

*

Course Registration Number:

45473 (View in ClassFinder)

Credit Hours:

3

Instructor:

Michael A. Dorin

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.

Schedule Details

Location Time Day(s)
OSS 3290900-160012 Sep '20
OSS 3290900-160026 Sep '20
OSS 3290900-160010 Oct '20
OSS 3290900-160024 Oct '20
OSS 3290900-160007 Nov '20
OSS 3290900-160021 Nov '20
OSS 3290900-160012 Dec '20
SEIS 615 - 01 Dev Ops & Cloud Infrastructure M - - - - - - 1745 - 2100 OSS 326

Days of Week:

M - - - - - -

Time of Day:

1745 - 2100

Location:

OSS 326

Course Registration Number:

46449 (View in ClassFinder)

Credit Hours:

3

Instructor:

Jason D. Baker

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 601 or 603) and SEIS 610

Schedule Details

Location Time Day(s)
SEIS 615 - 02 Dev Ops & Cloud Infrastructure - - W - - - - 1745 - 2100 OSS 326

Days of Week:

- - W - - - -

Time of Day:

1745 - 2100

Location:

OSS 326

Course Registration Number:

46450 (View in ClassFinder)

Credit Hours:

3

Instructor:

Chi-Lung Chiang

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 601 or 603) and SEIS 610

Schedule Details

Location Time Day(s)
SEIS 615 - 03 Dev Ops & Cloud Infrastructure - - - - F - - 1745 - 2100 OSS 326

Days of Week:

- - - - F - -

Time of Day:

1745 - 2100

Location:

OSS 326

Course Registration Number:

46451 (View in ClassFinder)

Credit Hours:

3

Instructor:

Chi-Lung Chiang

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 601 or 603) and SEIS 610

Schedule Details

Location Time Day(s)
SEIS 627 - 01 Software Planning & Testing - - - R - - - 1745 - 2100 OSS 328

Days of Week:

- - - R - - -

Time of Day:

1745 - 2100

Location:

OSS 328

Course Registration Number:

45603 (View in ClassFinder)

Credit Hours:

3

Instructor:

Aric B. Aune

This course presents a software planning and quality perspective that guides the selection of tools and application of techniques needed for the successful completion of software development projects. A successful software project must manage many different, yet 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), and test automation. Agile Project and Product Management using Scrum will be introduced as an approach for directing these activities and laying the foundation for continuous process improvement and quality assurance. Prerequisite: SEIS 610 AND SEIS 601/603

Schedule Details

Location Time Day(s)
SEIS 627 - 02 Software Planning & Testing - - W - - - - 1745 - 2100 OSS 229

Days of Week:

- - W - - - -

Time of Day:

1745 - 2100

Location:

OSS 229

Course Registration Number:

45815 (View in ClassFinder)

Credit Hours:

3

Instructor:

Frank S. Haug

This course presents a software planning and quality perspective that guides the selection of tools and application of techniques needed for the successful completion of software development projects. A successful software project must manage many different, yet 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), and test automation. Agile Project and Product Management using Scrum will be introduced as an approach for directing these activities and laying the foundation for continuous process improvement and quality assurance. Prerequisite: SEIS 610 AND SEIS 601/603

Schedule Details

Location Time Day(s)
SEIS 630 - 02 Database Mgmt Systems & Design - - W - - - - 1745 - 2100 OSS 333

Days of Week:

- - W - - - -

Time of Day:

1745 - 2100

Location:

OSS 333

Course Registration Number:

43905 (View in ClassFinder)

Credit Hours:

3

Instructor:

Jordan B. Barlow

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 aid system performance. Structured Query Language (SQL) is used to work with a database using the Oracle platform. The course also covers query optimization and execution strategies, concurrency control, locking, deadlocks, security, and backup/recovery concepts. Non-relational databases are also briefly introduced. Students will use Oracle and/or SQL Server to design and create a database using SQL as their project. Prerequisite: SEIS 610. SEIS 630 may be taken concurrently with SEIS610.

Schedule Details

Location Time Day(s)
SEIS 630 - 03 Database Mgmt Systems & Design - - - - F - - 1745 - 2100 OSS 333

Days of Week:

- - - - F - -

Time of Day:

1745 - 2100

Location:

OSS 333

Course Registration Number:

45474 (View in ClassFinder)

Credit Hours:

3

Instructor:

Jordan B. Barlow

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 aid system performance. Structured Query Language (SQL) is used to work with a database using the Oracle platform. The course also covers query optimization and execution strategies, concurrency control, locking, deadlocks, security, and backup/recovery concepts. Non-relational databases are also briefly introduced. Students will use Oracle and/or SQL Server to design and create a database using SQL as their project. Prerequisite: SEIS 610. SEIS 630 may be taken concurrently with SEIS610.

Schedule Details

Location Time Day(s)
SEIS 630 - 04 Database Mgmt Systems & Design See Details * *

Days of Week:

See Details

Time of Day:

*

Location:

*

Course Registration Number:

44916 (View in ClassFinder)

Credit Hours:

3

Instructor:

Jordan B. Barlow

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 aid system performance. Structured Query Language (SQL) is used to work with a database using the Oracle platform. The course also covers query optimization and execution strategies, concurrency control, locking, deadlocks, security, and backup/recovery concepts. Non-relational databases are also briefly introduced. Students will use Oracle and/or SQL Server to design and create a database using SQL as their project. Prerequisite: SEIS 610. SEIS 630 may be taken concurrently with SEIS610.

Schedule Details

Location Time Day(s)
OSS 3330900-160012 Sep '20
OSS 3330900-160026 Sep '20
OSS 3330900-160010 Oct '20
OSS 3330900-160024 Oct '20
OSS 3330900-160007 Nov '20
OSS 3330900-160021 Nov '20
OSS 3330900-160012 Dec '20
SEIS 631 - 01 Foundations of Data Analysis - T - - - - - 1745 - 2100 TMH 253

Days of Week:

- T - - - - -

Time of Day:

1745 - 2100

Location:

TMH 253

Course Registration Number:

45055 (View in ClassFinder)

Credit Hours:

3

Instructor:

Ebrahim F. Kazemzadeh

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).

Schedule Details

Location Time Day(s)
SEIS 631 - 02 Foundations of Data Analysis - - - R - - - 1745 - 2100 OSS 329

Days of Week:

- - - R - - -

Time of Day:

1745 - 2100

Location:

OSS 329

Course Registration Number:

45134 (View in ClassFinder)

Credit Hours:

3

Instructor:

Aran W. Glancy

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).

Schedule Details

Location Time Day(s)
SEIS 631 - 03 Foundations of Data Analysis - - W - - - - 1745 - 2100 OSS 325

Days of Week:

- - W - - - -

Time of Day:

1745 - 2100

Location:

OSS 325

Course Registration Number:

45475 (View in ClassFinder)

Credit Hours:

3

Instructor:

Ebrahim F. Kazemzadeh

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).

Schedule Details

Location Time Day(s)
SEIS 632 - 01 Data Analytics & Visualization M - - - - - - 1745 - 2100 OSS 325

Days of Week:

M - - - - - -

Time of Day:

1745 - 2100

Location:

OSS 325

Course Registration Number:

44985 (View in ClassFinder)

Credit Hours:

3

Instructor:

Craig J. Truempi

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.

Schedule Details

Location Time Day(s)
SEIS 632 - 02 Data Analytics & Visualization - - - R - - - 1745 - 2100 OSS 230

Days of Week:

- - - R - - -

Time of Day:

1745 - 2100

Location:

OSS 230

Course Registration Number:

45038 (View in ClassFinder)

Credit Hours:

3

Instructor:

Ebrahim F. Kazemzadeh

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.

Schedule Details

Location Time Day(s)
SEIS 632 - 03 Data Analytics & Visualization See Details * *

Days of Week:

See Details

Time of Day:

*

Location:

*

Course Registration Number:

45138 (View in ClassFinder)

Credit Hours:

3

Instructor:

Manjeet Rege

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.

Schedule Details

Location Time Day(s)
OSS 3260900-160012 Sep '20
OSS 3260900-160026 Sep '20
OSS 3260900-160010 Oct '20
OSS 3260900-160024 Oct '20
OSS 3260900-160007 Nov '20
OSS 3260900-160021 Nov '20
OSS 3260900-160012 Dec '20
SEIS 635 - 01 Software Analysis and Design - - - - F - - 1745 - 2100 OSS 329

Days of Week:

- - - - F - -

Time of Day:

1745 - 2100

Location:

OSS 329

Course Registration Number:

43906 (View in ClassFinder)

Credit Hours:

3

Instructor:

Eric V. Level

This course covers basic object-oriented techniques for analyzing software specifications and designing and implementing correct and useful software systems. Modern Agile iterative and incremental processes for software development such as Scrum and Kanban are emphasized. The Unified Modeling Language (UML) is reviewed, along with approaches to testing, debugging, and source code version control. Other topics include domain modeling, design reviews, responsibility-driven design, software class discovery and design, converting designs to code, basic design and architectural patterns, package designs, and deployment. Students will work on an object-oriented team project, applying concepts and techniques to describe and create a working software system. They will also learn the basics of Continuous Integration (CI) by using standard development environments, techniques, and tools in doing their teamwork. Prerequisite: SEIS 601 and SEIS 610.

Schedule Details

Location Time Day(s)
SEIS 636 - 01 Requirements Analysis See Details * *

Days of Week:

See Details

Time of Day:

*

Location:

*

Course Registration Number:

44830 (View in ClassFinder)

Credit Hours:

3

Instructor:

Jan M. Gardner

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.

Schedule Details

Location Time Day(s)
OSS 3250900-160012 Sep '20
OSS 3250900-160026 Sep '20
OSS 3250900-160010 Oct '20
OSS 3250900-160024 Oct '20
OSS 3250900-160007 Nov '20
OSS 3250900-160021 Nov '20
OSS 3250900-160012 Dec '20
SEIS 640 - 01 Operating Systems Design See Details * *

Days of Week:

See Details

Time of Day:

*

Location:

*

Course Registration Number:

43907 (View in ClassFinder)

Credit Hours:

3

Instructor:

Michael A. Dorin

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

Schedule Details

Location Time Day(s)
OSS 3291745-210009 Sep '20
OSS 3291745-210023 Sep '20
OSS 3291745-210007 Oct '20
OSS 3291745-210021 Oct '20
OSS 3291745-210004 Nov '20
OSS 3291745-210018 Nov '20
OSS 3291745-210009 Dec '20
SEIS 641 - 01 Enterprise Operating System - T - - - - - 1745 - 2100 OSS 229

Days of Week:

- T - - - - -

Time of Day:

1745 - 2100

Location:

OSS 229

Course Registration Number:

46448 (View in ClassFinder)

Credit Hours:

3

Instructor:

Karl R. Morris

This course examines what a mainframe is, why it has survived and the IT personnel that need to interact with it. It then discusses the basic hardware, software and networking components and the methods used to access and process data on the mainframe.

Schedule Details

Location Time Day(s)
SEIS 650 - 01 Legal Issues in Technology - - W - - - - 1745 - 2100 OSS 415

Days of Week:

- - W - - - -

Time of Day:

1745 - 2100

Location:

OSS 415

Course Registration Number:

44983 (View in ClassFinder)

Credit Hours:

3

Instructor:

Thomas R. Sheran

Students who complete this course should be able to recognize the legal issues relevant to software development and web-based communication and commerce --including how to obtain, retain, and challenge legal ownership of intellectual property; how to identify and allocate the risk of tort and contract liability, and how to address the legal and policy issues of privacy and security in cyberspace. Prerequisite: SEIS 610

Schedule Details

Location Time Day(s)
SEIS 662 - 01 Enterprise Resource Planning M - - - - - - 1745 - 2100 OSS 432

Days of Week:

M - - - - - -

Time of Day:

1745 - 2100

Location:

OSS 432

Course Registration Number:

44823 (View in ClassFinder)

Credit Hours:

3

Instructor:

William H. Gamble

This course will provide a practical overview of Enterprise Resource Planning, connecting the academic and technical elements with real-world, case-based issues as encountered by business and other organizations. ERP has becomea critical strategic consideration for many if not most 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 part through hands-on software engagement carrying out processes. In addition, since new ERP platforms integrate Analytics the course will look into trends relating to critical issues such as Enterprise Cloud and Smart Data. Professionals currently working in the IT organizations or future IT professionals will benefit from this course. Prerequisite: SEIS 610. SEIS 610 may be taken concurrently with SEIS 662.

Schedule Details

Location Time Day(s)
SEIS 663 - 01 IT Security and Networking - T - - - - - 1745 - 2100 OSS 313

Days of Week:

- T - - - - -

Time of Day:

1745 - 2100

Location:

OSS 313

Course Registration Number:

44984 (View in ClassFinder)

Credit Hours:

3

Instructor:

Melinda J. Mattox

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

Schedule Details

Location Time Day(s)
SEIS 663 - 02 IT Security and Networking - - - R - - - 1745 - 2100 BEC LL07

Days of Week:

- - - R - - -

Time of Day:

1745 - 2100

Location:

BEC LL07

Course Registration Number:

45135 (View in ClassFinder)

Credit Hours:

3

Instructor:

Julie D. Denning

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

Schedule Details

Location Time Day(s)
SEIS 664 - 01 Information Tech. Delivery - - - - F - - 1745 - 2100 OSS 328

Days of Week:

- - - - F - -

Time of Day:

1745 - 2100

Location:

OSS 328

Course Registration Number:

45199 (View in ClassFinder)

Credit Hours:

3

Instructor:

Charles T. Betz

This broad survey course covers IT and digital delivery, operations, and management in both theory and practice. Topics include IT and digital value; digital infrastructure including cloud; Agile and Lean influences including DevOps; product and service management; work management; operations management, coordination including process management; IT investment and portfolio; organization and cultural factors; IT management frameworks; IT governance, risk, security, compliance; enterprise information management; and enterprise architecture. Class sessions emphasize hands-on, team-based learning. Introductory Linux command-line skills are covered. Prerequisite: SEIS 610

Schedule Details

Location Time Day(s)
SEIS 709 - 01 Enterprise Arch. & IT Strategy - - - R - - - 1745 - 2100 BEC LL17

Days of Week:

- - - R - - -

Time of Day:

1745 - 2100

Location:

BEC LL17

Course Registration Number:

47244 (View in ClassFinder)

Credit Hours:

3

Instructor:

Asim Tahir

This course provides students with a theoretical and practical understanding of the subject areas related to strategy and enterprise architecture plus technical and business opportunities and industry trends. It also introduces implementation frameworks, methodologies, and technologies essential to realization of enterprise architecture. Prerequisite: SEIS 610.

Schedule Details

Location Time Day(s)
SEIS 732 - 01 Data Warehousing & Bus Intel - T - - - - - 1745 - 2100 OSS 333

Days of Week:

- T - - - - -

Time of Day:

1745 - 2100

Location:

OSS 333

Course Registration Number:

44214 (View in ClassFinder)

Credit Hours:

3

Instructor:

Frank S. Haug

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

Schedule Details

Location Time Day(s)
SEIS 732 - 02 Data Warehousing & Bus Intel - - - R - - - 1745 - 2100 OSS 333

Days of Week:

- - - R - - -

Time of Day:

1745 - 2100

Location:

OSS 333

Course Registration Number:

45204 (View in ClassFinder)

Credit Hours:

3

Instructor:

Frank S. Haug

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

Schedule Details

Location Time Day(s)
SEIS 735 - 01 Healthcare Analytics - - - - F - - 1745 - 2100 OSS 313

Days of Week:

- - - - F - -

Time of Day:

1745 - 2100

Location:

OSS 313

Course Registration Number:

47305 (View in ClassFinder)

Credit Hours:

3

Instructor:

Chih Lai

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 and SEIS 632.

Schedule Details

Location Time Day(s)
SEIS 736 - 01 Big Data Engineering M - - - - - - 1745 - 2100 OSS 328

Days of Week:

M - - - - - -

Time of Day:

1745 - 2100

Location:

OSS 328

Course Registration Number:

44731 (View in ClassFinder)

Credit Hours:

3

Instructor:

Abhishek Roy

As data is becoming more and more ubiquitous, the need to consume it to perform computations and power intelligent systems is also becoming more important. Bigger and more powerful neural networks need a large amount of data to be more accurate in performing tasks and making decisions. This means that it is increasingly important to understand the architecture and data plumbing for such sophisticated systems of the future. This course provides a broad coverage of the building blocks of a modern big data architecture which is fast, scalable and reliable. Major topics covered in this course include: (1) persistent storage and data organization (2) data ingestion and integration, (3) batch and stream processing, (4) modern cloud architectures, and (5) a real life example of geospatial analytics using such architecture. Students will complete hands on exercises leveraging big data tools to build data pipelines. Prerequisites: (SEIS 601 or SEIS 603) and SEIS 630. May take concurrently with SEIS 737.

Schedule Details

Location Time Day(s)
SEIS 736 - 02 Big Data Engineering - - - R - - - 1745 - 2100 OSS 326

Days of Week:

- - - R - - -

Time of Day:

1745 - 2100

Location:

OSS 326

Course Registration Number:

45037 (View in ClassFinder)

Credit Hours:

3

Instructor:

Cort M. Lunke

As data is becoming more and more ubiquitous, the need to consume it to perform computations and power intelligent systems is also becoming more important. Bigger and more powerful neural networks need a large amount of data to be more accurate in performing tasks and making decisions. This means that it is increasingly important to understand the architecture and data plumbing for such sophisticated systems of the future. This course provides a broad coverage of the building blocks of a modern big data architecture which is fast, scalable and reliable. Major topics covered in this course include: (1) persistent storage and data organization (2) data ingestion and integration, (3) batch and stream processing, (4) modern cloud architectures, and (5) a real life example of geospatial analytics using such architecture. Students will complete hands on exercises leveraging big data tools to build data pipelines. Prerequisites: (SEIS 601 or SEIS 603) and SEIS 630. May take concurrently with SEIS 737.

Schedule Details

Location Time Day(s)
SEIS 737 - 01 Big Data Management - T - - - - - 1745 - 2100 OSS 328

Days of Week:

- T - - - - -

Time of Day:

1745 - 2100

Location:

OSS 328

Course Registration Number:

44988 (View in ClassFinder)

Credit Hours:

3

Instructor:

Asher Chaudhry

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. Prerequisites:(SEIS 601 or SEIS 603) and SEIS 630. May take concurrently with SEIS 736.

Schedule Details

Location Time Day(s)
SEIS 737 - 02 Big Data Management - - - - F - - 1745 - 2100 OSS 122

Days of Week:

- - - - F - -

Time of Day:

1745 - 2100

Location:

OSS 122

Course Registration Number:

45258 (View in ClassFinder)

Credit Hours:

3

Instructor:

Nara R. Khou

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. Prerequisites:(SEIS 601 or SEIS 603) and SEIS 630. May take concurrently with SEIS 736.

Schedule Details

Location Time Day(s)
SEIS 741 - 01 Embedded Microprocessor Design M - - - - - - 1745 - 2100 OSS 229

Days of Week:

M - - - - - -

Time of Day:

1745 - 2100

Location:

OSS 229

Course Registration Number:

46453 (View in ClassFinder)

Credit Hours:

3

Instructor:

John M. Kruse

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

Schedule Details

Location Time Day(s)
SEIS 744 - 01 Internet of Things M - - - - - - 1745 - 2100 TMH 442

Days of Week:

M - - - - - -

Time of Day:

1745 - 2100

Location:

TMH 442

Course Registration Number:

45200 (View in ClassFinder)

Credit Hours:

3

Instructor:

Daniel R. Yarmoluk

As billions of devices are getting connected, the Internet of Things (IoT) has become one of the most talked about technology trends.But IoT is not really about technology and connected devices.At its core it is about business outcomes and people; it is about new ways of doing business, talent and change management; it is about migration to open technologies and open structures based on co-development and ecosystems and partnerships; it is an evolution and guiding philosophy.This course is intended to teach data science and analytics students the value of IoT and how to think of integrating data science concepts (big data, machine learning, visualization) as the key parts of driving human changein an increasingly data- 3driven world.The course is designed to guide emerging data scientists into understanding business value and how to inject data science at the core from data collection of IoT devices to business models delivering the value of data insights.The emerging gap of operational technology (OT) professionals forces the (IT) professionals to think past technology and tools to outcome-based results. This IoT introduction course is targeted at individuals who want to understand what theInternet of Things is, how it evolves from the Internet, what the core technologies and systems are and how it is implemented.

Schedule Details

Location Time Day(s)
SEIS 751 - 01 Web App. Design & Dev. - T - - - - - 1745 - 2100 OSS 326

Days of Week:

- T - - - - -

Time of Day:

1745 - 2100

Location:

OSS 326

Course Registration Number:

43908 (View in ClassFinder)

Credit Hours:

3

Instructor:

Marius N. Tegomoh

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.

Schedule Details

Location Time Day(s)
SEIS 763 - 01 Machine Learning - T - - - - - 1745 - 2100 BIN LL02

Days of Week:

- T - - - - -

Time of Day:

1745 - 2100

Location:

BIN LL02

Course Registration Number:

44987 (View in ClassFinder)

Credit Hours:

3

Instructor:

Manjeet Rege

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: SEIS 603 and 631

Schedule Details

Location Time Day(s)
SEIS 763 - 02 Machine Learning - - - R - - - 1745 - 2100 OSS 313

Days of Week:

- - - R - - -

Time of Day:

1745 - 2100

Location:

OSS 313

Course Registration Number:

45479 (View in ClassFinder)

Credit Hours:

3

Instructor:

Manjeet Rege

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: SEIS 603 and 631

Schedule Details

Location Time Day(s)
SEIS 763 - 03 Machine Learning M - - - - - - 1745 - 2100 OSS 313

Days of Week:

M - - - - - -

Time of Day:

1745 - 2100

Location:

OSS 313

Course Registration Number:

45816 (View in ClassFinder)

Credit Hours:

3

Instructor:

Chih Lai

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: SEIS 603 and 631

Schedule Details

Location Time Day(s)
SEIS 764 - 01 Artificial Intelligence See Details * *

Days of Week:

See Details

Time of Day:

*

Location:

*

Course Registration Number:

45606 (View in ClassFinder)

Credit Hours:

3

Instructor:

Manjeet Rege

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

Schedule Details

Location Time Day(s)
OSS 3130900-160019 Sep '20
OSS 3130900-160003 Oct '20
OSS 3130900-160017 Oct '20
OSS 3130900-160031 Oct '20
OSS 3130900-160014 Nov '20
OSS 3130900-160005 Dec '20
OSS 3130900-160019 Dec '20
SEIS 764 - 02 Artificial Intelligence - - W - - - - 1745 - 1900 OSS 313

Days of Week:

- - W - - - -

Time of Day:

1745 - 1900

Location:

OSS 313

Course Registration Number:

45607 (View in ClassFinder)

Credit Hours:

3

Instructor:

Chih Lai

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

Schedule Details

Location Time Day(s)
SEIS 785 - 01 Topics: Blockchain M - - - - - - 1745 - 2100 OSS 227

Days of Week:

M - - - - - -

Time of Day:

1745 - 2100

Location:

OSS 227

Course Registration Number:

45830 (View in ClassFinder)

Credit Hours:

3

Instructor:

David V. Duccini

This course will examine the confluence of technologies that underpin blockchain-based distributed ledgers that first appeared in cryptocurrencies like Bitcoin. New terminology is introduced, followed by discussions regarding why this technology is disruptively powerful and a philosophical inquiry into the nature of money itself. The course breaks down the role of “mining” and demonstrates why the economics of the current implementations are not scalable (or even profitable). The process of building blocks one technology at a time from the underlying revision control system, the communication channel known as “gossip,” to achieving consensus in both a trusted and untrusted world will be covered. Students will examine practical case studies beyond cryptocurrencies, which will include critical identification of when these technologies are not practical. Finally, the course will conclude with an in-depth exploration into Smart Documents and Smart Contracts and their possible outcomes.

Schedule Details

Location Time Day(s)

J-Term 2021 Courses

Course - Section Title Days Time Location