Enrollment and waitlist data for current and upcoming courses refresh every 10 minutes; all other information as of 6:00 AM.
| 01/31 - 05/20 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 29063
Online: Sync Distributed | Lecture
Online
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.
3 Credits
| 01/31 - 05/20 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 29066
Online: Sync Distributed | Lecture
Online
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.
3 Credits
| 01/31 - 05/20 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 29065
Online: Sync Distributed | Lecture
Online
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.
3 Credits
| 01/31 - 05/20 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 29067
In Person | Lecture
St Paul: O'Shaughnessy Science Hall 325
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.
3 Credits
| 01/31 - 05/20 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 29068
Online: Sync Distributed | Lecture
Online
Requirements Met:
Software Technical Elective
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.
3 Credits
| 01/31 - 05/20 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 29069
Online: Sync Distributed | Lecture
Online
Requirements Met:
Software Technical Elective
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.
3 Credits
| 01/31 - 05/20 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 29107
Online: Sync Distributed | Lecture
Online
Requirements Met:
Software Technical Elective
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.
3 Credits
| M | T | W | Th | F | Sa | Su |
02/05: 02/19: 03/05: 03/19: 04/02: 04/23: 05/07: |
Subject: Software Eng (Grad) (SEIS)
CRN: 29108
In Person | Lecture
St Paul: O'Shaughnessy Science Hall 313
Requirements Met:
Software Technical Elective
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.
3 Credits
| 01/31 - 05/20 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 29109
Online: Sync Distributed | Lecture
Online
Requirements Met:
Software Technical Elective
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: SEIS610 Software Engineering. Students can register for SEIS610 and SEIS615 concurrently. Prerequisites: SEIS 601 or SEIS 603 and SEIS 610
3 Credits
| 01/31 - 05/20 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 29110
Online: Sync Distributed | Lecture
Online
Requirements Met:
Software Technical Elective
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: SEIS610 Software Engineering. Students can register for SEIS610 and SEIS615 concurrently. Prerequisites: SEIS 601 or SEIS 603 and SEIS 610
3 Credits
| 01/31 - 05/20 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 29111
Online: Sync Distributed | Lecture
Online
Requirements Met:
Software Technical Elective
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: SEIS610 Software Engineering. Students can register for SEIS610 and SEIS615 concurrently. Prerequisites: SEIS 601 or SEIS 603 and SEIS 610
3 Credits
| 01/31 - 05/20 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 29112
Online: Sync Distributed | Lecture
Online
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
3 Credits
| 01/31 - 05/20 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 29113
Online: Sync Distributed | Lecture
Online
Requirements Met:
Software Data Mgmt Conc
Software Technical Elective
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.
3 Credits
| 01/31 - 05/20 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 29114
Online: Sync Distributed | Lecture
Online
Requirements Met:
Software Data Mgmt Conc
Software Technical Elective
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.
3 Credits
| 01/31 - 05/20 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 29115
Online: Sync Distributed | Lecture
Online
Requirements Met:
Software Data Mgmt Conc
Software Technical Elective
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.
3 Credits
| 01/31 - 05/20 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 29116
Online: Sync Distributed | Lecture
Online
Requirements Met:
Software Technical Elective
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).
3 Credits
| 01/31 - 05/20 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 29117
Online: Sync Distributed | Lecture
Online
Requirements Met:
Software Technical Elective
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).
3 Credits
| 01/31 - 05/20 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 29118
Online: Sync Distributed | Lecture
Online
Requirements Met:
LLM/MSL Elective
Software Technical Elective
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.
3 Credits
| 01/31 - 05/20 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 29119
Online: Sync Distributed | Lecture
Online
Requirements Met:
LLM/MSL Elective
Software Technical Elective
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.
3 Credits
| 01/31 - 05/20 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 29120
Online: Sync Distributed | Lecture
Online
Requirements Met:
LLM/MSL Elective
Software Technical Elective
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.
3 Credits
| 01/31 - 05/20 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 29121
Online: Sync Distributed | Lecture
Online
Requirements Met:
Software Object-Oriented Conc
Software Technical Elective
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.
3 Credits
| 01/31 - 05/20 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 29123
Online: Sync Distributed | Lecture
Online
New Artificial Intelligence approaches provide powerful capability in analyzing complex and heterogeneous data that are previously difficult to analyze. The data may range from structural patient records to semi-structural medical text, images, and videos. Specifically, this course will discuss the following topics: (1) the fundamental learning methods used by machines, (2) problems, solutions, and advantages of artificial intelligence and machine learning, (3) learning and interpretation of healthcare and business data, (4) transferring existing artificial intelligence models for new business problems, (5) processing and classifying healthcare images such as X-ray or videos, (6) case study of time-series and text analytics in healthcare area and more general business domain. Data Science students completing SEIS 764 Artificial Intelligence should not take this course. Prerequisite: SEIS 631
3 Credits
| 01/31 - 05/20 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 29124
Online: Sync Distributed | Lecture
Online
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.
3 Credits
| 01/31 - 05/20 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 29125
Online: Sync Distributed | Lecture
Online
Requirements Met:
Software Technical Elective
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
3 Credits
| 01/31 - 05/20 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 29126
Online: Sync Distributed | Lecture
Online
Requirements Met:
Software Technical Elective
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
3 Credits
| 01/31 - 05/20 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 29138
Online: Sync Distributed | Lecture
Online
Digital Transformation is everywhere: business to business, business to consumer and even government to citizens. Digital transformation promises a bridge to a digital future, where organizations can thrive more fluid business models and processes. In this course, we start by showing the step by step of what digital transformation is, harnessing various exponential technologies and the five domains of digital transformation: Customers, Competition, Data, Innovation, and Value. A deep dive into data, the economic value of data, and data monetization in a B2B and B2C context. Understanding the layers of data, value proposition and business models play a holistic and practical guide for a digital-first organization and professional to transform legacy businesses or create new value propositions in the digital age. We also take an in-depth look at many technologies, including data science, that are part of many successful digital transformations.
3 Credits
| 01/31 - 05/20 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 29128
Online: Sync Distributed | Lecture
Online
This course provides students with a theoretical and practical understanding of Strategy and Enterprise Architecture (EA). It studies how EA enables organizations to effectively accomplish business goals. Specifically, the course analyzes the relationships among business strategies, IT strategies, business, applications, information, and technology architectures. It also examines current industry trends such as: design thinking, digital transformation, cloud migration, and introduces students to EA implementation frameworks and tools.This course provides students with a theoretical and practical understanding of Strategy and Enterprise Architecture (EA). It studies how EA enables organizations to effectively accomplish business goals. Specifically, the course analyzes the relationships among business strategies, IT strategies, business, applications, information, and technology architectures. It also examines current industry trends such as: design thinking, digital transformation, cloud migration, and introduces students to EA implementation frameworks and tools.
3 Credits
| 01/31 - 05/20 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 29129
Online: Sync Distributed | Lecture
Online
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.
3 Credits
| 01/31 - 05/20 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 29130
Online: Sync Distributed | Lecture
Online
Requirements Met:
Software Data Mgmt Conc
Software Technical Elective
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
3 Credits
| 01/31 - 05/20 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 29131
Online: Sync Distributed | Lecture
Online
Requirements Met:
Software Data Mgmt Conc
Software Technical Elective
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
3 Credits
| 01/31 - 05/20 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 29134
Online: Sync Distributed | Lecture
Online
Requirements Met:
Software Data Mgmt Conc
Software Technical Elective
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.
3 Credits
| 01/31 - 05/20 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 29135
Online: Sync Distributed | Lecture
Online
Requirements Met:
Software Data Mgmt Conc
Software Technical Elective
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.
3 Credits
| 01/31 - 05/20 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 29133
Online: Sync Distributed | Lecture
Online
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.
3 Credits
| 01/31 - 05/20 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 29137
Online: Sync Distributed | Lecture
Online
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.
3 Credits
| 01/31 - 05/20 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 29139
Online: Sync Distributed | Lecture
Online
Requirements Met:
Software Technical Elective
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.
3 Credits
| 01/31 - 05/20 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 29140
Online: Sync Distributed | Lecture
Online
Requirements Met:
Software Technical Elective
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
3 Credits
| 01/31 - 05/20 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 29141
Online: Sync Distributed | Lecture
Online
Requirements Met:
Software Technical Elective
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
3 Credits
| 01/31 - 05/20 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 29142
Online: Sync Distributed | Lecture
Online
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
3 Credits
| M | T | W | Th | F | Sa | Su |
02/05: 02/19: 03/05: 03/19: 04/02: 04/23: 05/07: |
Subject: Software Eng (Grad) (SEIS)
CRN: 29143
Online: Sync Distributed | Lecture
Online
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
3 Credits