Enrollment and waitlist data for current and upcoming courses refresh every 10 minutes; all other information as of 6:00 AM.
| M | T | W | Th | F | Sa | Su |
05/26 - 07/11: 05/26 - 07/11: |
05/26 - 07/11: 05/26 - 07/11: |
05/29: 05/29: |
Subject: Software Eng (Grad) (SEIS)
CRN: 30210
CoFlex:In Person&Online Sync | Lecture
St Paul: O'Shaughnessy Science Hall 333
Online
This introductory software development course focuses on fundamental programming concepts. We will cover general problem-solving techniques, algorithm creation, data types, constants, variables, expressions, Boolean logic, control flow, and principles of object-oriented programming. Throughout the course, we will implement programs using the Python programming language, exploring its versatility as both an interpreted and a compiled language. Students will work with core data types such as numbers, strings, lists, dictionaries, and sets. They will learn how to use Python for data management, establishing a foundation for future endeavors in fields like data science and web development. Additionally, we will examine how AI-powered tools can enhance the learning and development of Python code. For instance, we will introduce AI-driven code completion and error detection tools to help students understand syntax and debug more effectively. We may also explore AI applications in data analysis and automation, demonstrating potential uses for Python skills. Finally, we will introduce PyTest for unit and integration testing. No prior programming experience in Python or any other programming language is required. NOTE: A class meeting added on Friday 5/29 to maintain required instructional contact hours.
3 Credits
| 07/13 - 08/27 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
5:30 pm 5:30 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 30211
CoFlex:In Person&Online Sync | Lecture
St Paul: Schoenecker Center 408
Online
This course covers the fundamentals of IT infrastructure in the cloud. It provides a detailed overview of cloud concepts, services, security, architecture, and economics. This course will examine the theory behind these modern practices and the real-world implementation challenges faced by IT organizations. Students will learn how to design and implement cloud-based solutions. 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 AWS and Docker.
3 Credits
| 07/13 - 08/27 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm |
5:30 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 30437
Online: Sync Distributed | Lecture
Online
This course will teach students the essentials of becoming a full stack web developer by creating dynamic, interactive websites, and is suitable for anyone with basic computer programming skills. The course initially focuses on HTML, CSS and JavaScript and later transactions into technologies like Angular framework, Node, and Serverless functions in a cloud environment. Students develop skills for designing, publishing, and maintaining websites for professional or personal use. No previous experience or knowledge of web development is needed. Prerequisites: SEIS 602 or SEIS 604
3 Credits
| 05/26 - 07/11 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
5:30 pm 5:30 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 30212
CoFlex:In Person&Online Sync | Lecture
St Paul: Schoenecker Center 314
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.
3 Credits
| 07/13 - 08/27 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
5:30 pm 5:30 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 30213
CoFlex:In Person&Online Sync | Lecture
St Paul: Schoenecker Center 314
Online
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. The course will introduce students to Statistical Science including Probability Distribution, Sampling Distribution, Statistical Inference, and Significance Testing. Students will also develop proficiency in the widely used Python language which will be used throughout the course to reinforce the topics covered. Packages like NumPy and Pandas will be discussed at length for Data Cleaning, Data Wrangling: Joins, Combine, Data Reshape, Data Aggregation, Group Operation, and Time Series analysis. Prerequisite: SEIS 603
3 Credits
| M | T | W | Th | F | Sa | Su |
05/26 - 07/11: 05/26 - 07/11: |
05/26 - 07/11: 05/26 - 07/11: |
05/29: 05/29: |
Subject: Software Eng (Grad) (SEIS)
CRN: 30214
CoFlex:In Person&Online Sync | Lecture
St Paul: O'Shaughnessy Science Hall 313
Online
Requirements Met:
LLM/MSL Elective
Even the most insightful data analysis has limited value if analysts cannot convey clear, actionable insights to non-technical audiences. This course develops the critical skills necessary to transform complex quantitative findings into compelling data stories and visualizations. Students will learn how to leverage visual design principles that speak directly to human cognitive abilities, guiding business stakeholders toward data-driven decisions. The curriculum covers creating meaningful graphs, reports, and dashboards that improve comprehension, catalyze communication, and enable fact-based choices. By mastering techniques for visualizing and explaining data, students will become adept at distilling analytical conclusions into incisive narratives readily grasped by diverse audiences. Upon completion, they will have obtained hands-on experience with state-of-the-art data visualization tools to generate impactful data-driven visual insights. NOTE: A class meeting added on Friday 5/29 to maintain required instructional contact hours.
3 Credits
| 05/26 - 07/11 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
5:30 pm 5:30 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 30215
CoFlex:In Person&Online Sync | Lecture
St Paul: O'Shaughnessy Science Hall 333
Online
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. Prerequisites: SEIS 631 and 632, 632 can be taken concurrently.
3 Credits
| M | T | W | Th | F | Sa | Su |
07/01: |
05/26 - 07/11: |
Subject: Software Eng (Grad) (SEIS)
CRN: 30399
Online: Sync Distributed | Online: Synchronous
Online
Artificial Intelligence develops computational models that emulate intelligent behavior through learning, perception, and decision-making. With the advent of deep learning, AI systems now achieve state-of-the-art performance in domains such as computer vision, natural language processing, and sequential pattern recognition. This course provides a rigorous introduction to the design and training of artificial neural networks, including techniques for improving generalization and adapting models to new tasks. Students will gain hands-on experience in implementing and optimizing deep learning architectures, while also examining the theoretical foundations that govern their behavior. Emphasis is placed on evaluating model performance, diagnosing training challenges, and understanding the limitations and risks associated with deploying AI systems in real-world contexts. NOTE: A replacement class meeting has been scheduled on Wednesday 7/1 due to holiday on Saturday 7/4. Prerequisite: SEIS 763
3 Credits