Enrollment and waitlist data for current and upcoming courses refresh every 10 minutes; all other information as of 6:00 AM.
| 05/28 - 07/17 | ||||||
| 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: 30319
CoFlex:In Person&Online Sync | Lecture
St Paul: O'Shaughnessy Science Hall 325
Online
This is an introductory software development course with a 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 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. PyTest will be discussed for Unit and Integration Testing.
3 Credits
| M | T | W | Th | F | Sa | Su |
07/17 - 08/08: 07/17 - 08/29: 08/10 - 08/29: |
07/17 - 08/08: 07/17 - 08/29: 08/10 - 08/29: |
08/09: |
Subject: Software Eng (Grad) (SEIS)
CRN: 30320
CoFlex:In Person&Online Sync | Lecture
St Paul: Schoenecker Center 314
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. Please note: this course has a Friday meeting date added, 8/9, to meet the required number of course sessions.
3 Credits
| 07/17 - 08/29 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm |
5:30 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 30326
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 601 or 603
3 Credits
| 05/28 - 07/17 | ||||||
| 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: 30321
CoFlex:In Person&Online Sync | Lecture
St Paul: O'Shaughnessy Science Hall 325
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
| M | T | W | Th | F | Sa | Su |
07/17 - 08/29: 07/17 - 08/29: |
07/17 - 08/29: 07/17 - 08/29: |
08/09: |
Subject: Software Eng (Grad) (SEIS)
CRN: 30325
CoFlex:In Person&Online Sync | Lecture
St Paul: Schoenecker Center 408
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 Please note: this course has a Friday meeting date added, 8/9, to meet the required number of course sessions.
3 Credits
| M | T | W | Th | F | Sa | Su |
06/03: 06/10: 06/17: 06/24: 07/01: 07/08: |
05/29: 06/05: 06/12: 06/26: 07/03: 07/10: |
05/31: 06/21: |
Subject: Software Eng (Grad) (SEIS)
CRN: 30322
CoFlex:In Person&Online Sync | Lecture
St Paul: O'Shaughnessy Science Hall 313
Requirements Met:
LLM/MSL 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
| 05/28 - 07/17 | ||||||
| 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: 30323
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
| 05/28 - 07/17 | ||||||
| M | T | W | Th | F | Sa | Su |
9:00 am |
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Subject: Software Eng (Grad) (SEIS)
CRN: 30324
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