Results

Enrollment and waitlist data for current and upcoming courses refresh every 10 minutes; all other information as of 6:00 AM.


Refine Search Results

SEIS: Software Eng (Grad)

601-01
Foundations of Java I
 
See Details
A. Kazemzadeh
 
02/05 - 05/17
25/8/0
Lecture
CRN 21338
3 Cr.
Size: 25
Enrolled: 8
Waitlisted: 0
02/05 - 05/17
M T W Th F Sa Su
     

5:30 pm
8:30 pm
OSS 328

5:30 pm
8:30 pm
Online

     

Subject: Software Eng (Grad) (SEIS)

CRN: 21338

CoFlex:In Person&Online Sync | Lecture

St Paul: O'Shaughnessy Science Hall 328

Online

  Abe Kazemzadeh

This is a foundational software development course focusing on fundamental programming concepts as implemented using the Java programming language. These concepts include general problem solving and algorithm creation techniques, primitive and object data types, constants, variables, expressions, and boolean logic and control flow. In addition, we will discuss fundamental object-oriented concepts, such as objects and classes, object instantiation and initialization, method implementation and invocation, interfaces, inheritance, and garbage collection. Students will apply these concepts by writing programs in the Java programming language. JUnit will be discussed for Unit and Integration Testing.  

3 Credits

602-01
Foundations of Java II
 
See Details
G. Shrestha
 
02/05 - 05/17
25/18/0
Lecture
CRN 21852
3 Cr.
Size: 25
Enrolled: 18
Waitlisted: 0
02/05 - 05/17
M T W Th F Sa Su
 

5:30 pm
8:30 pm
OSS 328

5:30 pm
8:30 pm
Online

         

Subject: Software Eng (Grad) (SEIS)

CRN: 21852

CoFlex:In Person&Online Sync | Lecture

St Paul: O'Shaughnessy Science Hall 328

Online

  Gaurav Shrestha

This is a foundational software development course focusing on intermediate-level fundamental and foundational concepts. Abstract data type concepts will be discussed in detail. Data Structures and some of their associated algorithms for Algorithm Analysis will be discussed. Canonical implementations and framework supplied implementation alternatives (such as the JDK or other framework alternatives) will be explored and used as well. To apply the lecture concepts, we will implement software using the Java programming language and explore some of the tools used by software developers. Eclipse would be used as an integrated development environment for code development. Further, tools for managing software build, configuration, and version control (e.g., Git) and unit and integration testing (e.g., JUnit) will be used. We will also discuss multi-threading, memory management, refactoring, and advanced debugging techniques. Prerequisite: SEIS 601 or equivalent

3 Credits

603-01
Foundations of Python I
 
See Details
S. Naqvi
 
02/05 - 05/17
25/19/0
Lecture
CRN 21340
3 Cr.
Size: 25
Enrolled: 19
Waitlisted: 0
02/05 - 05/17
M T W Th F Sa Su
     

6:00 pm
9:00 pm
OSS 333

6:00 pm
9:00 pm
Online

     

Subject: Software Eng (Grad) (SEIS)

CRN: 21340

CoFlex:In Person&Online Sync | Lecture

St Paul: O'Shaughnessy Science Hall 333

Online

  Syed Naqvi

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

603-02
Foundations of Python I
 
See Details
E. Level
 
02/05 - 05/17
25/12/0
Lecture
CRN 21339
3 Cr.
Size: 25
Enrolled: 12
Waitlisted: 0
02/05 - 05/17
M T W Th F Sa Su
   

5:30 pm
8:30 pm
OSS 325

5:30 pm
8:30 pm
Online

       

Subject: Software Eng (Grad) (SEIS)

CRN: 21339

CoFlex:In Person&Online Sync | Lecture

St Paul: O'Shaughnessy Science Hall 325

Online

  Eric Level

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

603-03
Foundations of Python I
 
See Details
E. Level
 
02/05 - 05/17
25/22/0
Lecture
CRN 21341
3 Cr.
Size: 25
Enrolled: 22
Waitlisted: 0
02/05 - 05/17
M T W Th F Sa Su
 

5:30 pm
8:30 pm
OSS 325

5:30 pm
8:30 pm
Online

         

Subject: Software Eng (Grad) (SEIS)

CRN: 21341

CoFlex:In Person&Online Sync | Lecture

St Paul: O'Shaughnessy Science Hall 325

Online

  Eric Level

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

604-01
Foundations of Python II
 
See Details
E. Level
 
02/05 - 05/17
25/10/0
Lecture
CRN 21835
3 Cr.
Size: 25
Enrolled: 10
Waitlisted: 0
02/05 - 05/17
M T W Th F Sa Su
     

5:30 pm
8:30 pm
OSS 325

5:30 pm
8:30 pm
Online

     

Subject: Software Eng (Grad) (SEIS)

CRN: 21835

CoFlex:In Person&Online Sync | Lecture

St Paul: O'Shaughnessy Science Hall 325

Online

  Eric Level

This is a foundational software development course focusing on intermediate-level fundamental and foundational concepts. Abstract data type concepts will be discussed in detail. Data Structures and some of their associated algorithms for Algorithm Analysis will be discussed. Canonical implementations and framework supplied implementation alternatives will be explored and used as well. To apply the lecture concepts, we will implement software using the Python programming language and explore some of the tools used by software developers. Spyder or PyCharm would be used as integrated development environments (IDE) for code development. Further, tools for managing software build, configuration, and version control (e.g., Git) and unit and integration testing (e.g., PyTest) will be used. We will also discuss multi-threading, memory management, refactoring, and advanced debugging techniques. Prerequisites: SEIS 603

3 Credits

610-01
Software Engineering
 
See Details
M. Dorin
 
02/05 - 05/17
25/23/0
Lecture
CRN 21371
3 Cr.
Size: 25
Enrolled: 23
Waitlisted: 0
02/05 - 05/17
M T W Th F Sa Su

5:30 pm
8:30 pm
SCC 408

5:30 pm
8:30 pm
Online

           

Subject: Software Eng (Grad) (SEIS)

CRN: 21371

CoFlex:In Person&Online Sync | Lecture

St Paul: Schoenecker Center 408

Online

  Michael Dorin

This introductory course covers software engineering concepts, techniques, and methodologies. The course introduces software engineering life-cycle models, such as Scrum and Kanban. Students learn the essential concepts of different lifecycle models and where their application is appropriate. The course continues by teaching concepts of requirements acquisition and various methods of requirements refinement. Also presented in this course are concepts of object-oriented and structured design. The course incorporates vital supporting topics such as software metrics, project planning, cost estimation, software maintenance, and an introduction to data structures and running time analysis. Prerequisite: SEIS 601 or SEIS 603. SEIS 610 can be taken concurrently with SEIS 601 or SEIS 603.

3 Credits

615-01
Cloud Computing
 
See Details
R. Chiang
 
02/05 - 05/17
26/24/0
Lecture
CRN 21373
3 Cr.
Size: 26
Enrolled: 24
Waitlisted: 0
02/05 - 05/17
M T W Th F Sa Su
 

5:30 pm
8:30 pm
OSS 326

5:30 pm
8:30 pm
Online

         

Subject: Software Eng (Grad) (SEIS)

CRN: 21373

CoFlex:In Person&Online Sync | Lecture

St Paul: O'Shaughnessy Science Hall 326

Online

  Ron Chiang

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

615-02
Cloud Computing
 
See Details
R. Chiang
 
02/05 - 05/17
25/25/0
Lecture
CRN 21374
3 Cr.
Size: 25
Enrolled: 25
Waitlisted: 0
02/05 - 05/17
M T W Th F Sa Su
     

5:30 pm
8:30 pm
OSS 326

5:30 pm
8:30 pm
Online

     

Subject: Software Eng (Grad) (SEIS)

CRN: 21374

CoFlex:In Person&Online Sync | Lecture

St Paul: O'Shaughnessy Science Hall 326

Online

  Ron Chiang

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

616-01
Infrastructure as Code
 
See Details
R. Chiang
 
02/05 - 05/17
25/22/0
Lecture
CRN 21837
3 Cr.
Size: 25
Enrolled: 22
Waitlisted: 0
02/05 - 05/17
M T W Th F Sa Su

5:30 pm
8:30 pm
OSS 326

5:30 pm
8:30 pm
Online

           

Subject: Software Eng (Grad) (SEIS)

CRN: 21837

CoFlex:In Person&Online Sync | Lecture

St Paul: O'Shaughnessy Science Hall 326

Online

  Ron Chiang

This course covers the engineering and design of IT infrastructure, focusing on infrastructure as Code 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. The lessons will cover a number of tools, techniques, and patterns to implement infrastructure as code. Students will learn about platforms and tooling involved in creating and configuring infrastructure elements, patterns for using these tools, and practices for making infrastructure as code work in production. Prerequisites: SEIS 615

3 Credits

622-01
Web App Development
 
W 5:30 pm - 8:30 pm
G. Shrestha
 
02/05 - 05/17
24/22/0
Lecture
CRN 21838
3 Cr.
Size: 24
Enrolled: 22
Waitlisted: 0
02/05 - 05/17
M T W Th F Sa Su
   

5:30 pm
8:30 pm
Online

       

Subject: Software Eng (Grad) (SEIS)

CRN: 21838

Online: Sync Distributed | Lecture

Online

  Gaurav Shrestha

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

627-01
Software Agile Processes
 
T 5:30 pm - 8:30 pm
S. Mathur
 
02/05 - 05/17
25/17/0
Lecture
CRN 21376
3 Cr.
Size: 25
Enrolled: 17
Waitlisted: 0
02/05 - 05/17
M T W Th F Sa Su
 

5:30 pm
8:30 pm
Online

         

Subject: Software Eng (Grad) (SEIS)

CRN: 21376

Online: Sync Distributed | Lecture

Online

  Sanjay Mathur

This course will provide students with a comprehensive overview of the principles, processes, and practices of many available agile software product development techniques. Students will learn agile planning, development, and delivery techniques with Scrum, Kanban, Lean, Extreme, Crystal, Dynamic, and Feature Driven Development.  Scaled agile framework (SAFe) for large enterprises in scaling lean and agile practices beyond a single team along with Large-scale Scrum (LeSS) and disciplined agile delivery (DAD) will also be explored.  Students will be provided with the opportunity to apply the skills in creating and delivering new products in a team environment.  Drivers behind agility in software development along with methods for project tracking, project communication, team collaboration, client relationship management, stakeholder management and quality of deliverables will be discussed at length.  

3 Credits

630-01
Database Mgmt Systems & Design
 
See Details
A. Kazemzadeh
SEIS* 
02/05 - 05/17
29/28/0
Lecture
CRN 21377
3 Cr.
Size: 29
Enrolled: 28
Waitlisted: 0
02/05 - 05/17
M T W Th F Sa Su

5:30 pm
8:30 pm
OSS 313

5:30 pm
8:30 pm
Online

           

Subject: Software Eng (Grad) (SEIS)

CRN: 21377

CoFlex:In Person&Online Sync | Lecture

St Paul: O'Shaughnessy Science Hall 313

Online

Requirements Met:
     Software Data Mgmt Conc
     Software Technical Elective

  Abe Kazemzadeh

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

630-02
Database Mgmt Systems & Design
 
See Details
A. Kazemzadeh
SEIS* 
02/05 - 05/17
28/28/0
Lecture
CRN 21378
3 Cr.
Size: 28
Enrolled: 28
Waitlisted: 0
02/05 - 05/17
M T W Th F Sa Su
   

5:30 pm
8:30 pm
OSS 329

5:30 pm
8:30 pm
Online

       

Subject: Software Eng (Grad) (SEIS)

CRN: 21378

CoFlex:In Person&Online Sync | Lecture

St Paul: O'Shaughnessy Science Hall 329

Online

Requirements Met:
     Software Data Mgmt Conc
     Software Technical Elective

  Abe Kazemzadeh

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

631-01
Data Preparation and Analysis
 
See Details
S. Naqvi
 
02/05 - 05/17
26/22/0
Lecture
CRN 21380
3 Cr.
Size: 26
Enrolled: 22
Waitlisted: 0
02/05 - 05/17
M T W Th F Sa Su

6:00 pm
9:00 pm
OSS 333

6:00 pm
9:00 pm
Online

           

Subject: Software Eng (Grad) (SEIS)

CRN: 21380

CoFlex:In Person&Online Sync | Lecture

St Paul: O'Shaughnessy Science Hall 333

Online

  Syed Naqvi

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

631-02
Data Preparation and Analysis
 
See Details
S. Naqvi
 
02/05 - 05/17
25/24/0
Lecture
CRN 21381
3 Cr.
Size: 25
Enrolled: 24
Waitlisted: 0
02/05 - 05/17
M T W Th F Sa Su
 

6:00 pm
9:00 pm
OSS 333

6:00 pm
9:00 pm
Online

         

Subject: Software Eng (Grad) (SEIS)

CRN: 21381

CoFlex:In Person&Online Sync | Lecture

St Paul: O'Shaughnessy Science Hall 333

Online

  Syed Naqvi

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

632-01
Data Analytics & Visualization
 
See Details
M. Rege
LL.M 
02/05 - 05/17
25/25/0
Lecture
CRN 21382
3 Cr.
Size: 25
Enrolled: 25
Waitlisted: 0
02/05 - 05/17
M T W Th F Sa Su
   

5:30 pm
8:30 pm
OSS 313

5:30 pm
8:30 pm
Online

       

Subject: Software Eng (Grad) (SEIS)

CRN: 21382

CoFlex:In Person&Online Sync | Lecture

St Paul: O'Shaughnessy Science Hall 313

Online

Requirements Met:
     LLM/MSL Elective

  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.

3 Credits

632-02
Data Analytics & Visualization
 
W 5:30 pm - 8:30 pm
M. Rege
LL.M 
02/05 - 05/17
25/25/0
Lecture
CRN 21383
3 Cr.
Size: 25
Enrolled: 25
Waitlisted: 0
02/05 - 05/17
M T W Th F Sa Su
   

5:30 pm
8:30 pm
Online

       

Subject: Software Eng (Grad) (SEIS)

CRN: 21383

Online: Sync Distributed | Lecture

Online

Requirements Met:
     LLM/MSL Elective

  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.

3 Credits

640-01
Ethical Hacking and OS
 
See Details
M. Dorin
SEIS* 
02/05 - 05/17
25/7/0
Lecture
CRN 22334
3 Cr.
Size: 25
Enrolled: 7
Waitlisted: 0
02/05 - 05/17
M T W Th F Sa Su
   

5:30 pm
8:30 pm
SCC 408

5:30 pm
8:30 pm
Online

       

Subject: Software Eng (Grad) (SEIS)

CRN: 22334

CoFlex:In Person&Online Sync | Lecture

St Paul: Schoenecker Center 408

Online

Requirements Met:
     Software Embedded Systems Conc
     Software Comp Security Cert
     Software Technical Elective

  Michael Dorin

This course introduces the basic concepts involved in ethical hacking. An ethical hacker assesses software security by looking for weaknesses and vulnerabilities in target systems. An effective ethical hacker must understand network communications, software development, and operating systems internals. The course begins with a review of the fundamental topics of operating systems design. Topics such as process scheduling, input/output, memory management, file system design, security, and protection mechanisms are covered. The course continues with activities performed by ethical hackers, such as testing via injection attacks, searching for broken authentication, identifying security misconfigurations, and pinpointing data exposure. Prerequisites: None.

3 Credits

651-01
AI Ethics
 
See Details
B. Keaveny
 
02/05 - 05/17
25/16/0
Lecture
CRN 22748
3 Cr.
Size: 25
Enrolled: 16
Waitlisted: 0
02/05 - 05/17
M T W Th F Sa Su
     

5:30 pm
8:30 pm
Online

     

Subject: Software Eng (Grad) (SEIS)

CRN: 22748

Online: Sync Distributed | Lecture

Online

  Brandan Keaveny, Jessi Benzel

The purpose of this course is to guide students through the knowledge, skills, and opportunities needed to develop an ethical foundation on which they can build their careers as AI practitioners or as professionals in other fields that have been or will be impacted by AI. We will explore a variety of ethical issues related to the development and use of AI across multiple fields of study, with an emphasis on the human impact of AI. Course topics will cover a range of foundational AI concepts including data preparation, bias, neural networks, natural language processing, large language models, generative AI, model validation, and more, in the context of issues like discrimination, misinformation, intellectual property, regulation, jobs, and humanity at large. Class sessions are comprised of a weekly “hot topic” where we will explore the ethical implications of current events in AI, a lecture period, and lab where students have the opportunity to discuss and apply the course material to practical and theoretical exercises. This course is intended for both technical and non-technical audiences.

3 Credits

666-01
Digital Transformation 2.0
 
R 5:30 pm - 8:30 pm
D. Yarmoluk
 
02/05 - 05/17
25/8/0
Lecture
CRN 21393
3 Cr.
Size: 25
Enrolled: 8
Waitlisted: 0
02/05 - 05/17
M T W Th F Sa Su
     

5:30 pm
8:30 pm
Online

     

Subject: Software Eng (Grad) (SEIS)

CRN: 21393

Online: Sync Distributed | Lecture

Online

  Dan Yarmoluk

Digital transformation promises a bridge to a digital future, where organizations can thrive more fluid business models and processes.  Less than 20% of organizations are getting digital transformations right, but these digitally transformed organizations can deliver twice as fast as other organizations. Large language models (LLMs) and ChatGPT, automation and AI will supercharge further change into a second chapter of radical change. Digital Transformation 2.0 is an innovative course that delves into the world of digital transformation, focusing on the new change, the Future of Work and the impact of ChatGPT and Generative AI technologies on modern businesses and industries. This course provides students with hands-on experience using ChatGPT and other AI tools while exploring digital maturity models and the establishment of a Generative AI Center of Excellence (GAICoE). Students will learn how to integrate AI-driven solutions into business processes and strategies, transforming the way organizations operate in the digital age. 

3 Credits

709-01
Enterprise Archt & Strategy
 
W 5:30 pm - 8:30 pm
S. Mathur
 
02/05 - 05/17
25/7/0
Lecture
CRN 21387
3 Cr.
Size: 25
Enrolled: 7
Waitlisted: 0
02/05 - 05/17
M T W Th F Sa Su
   

5:30 pm
8:30 pm
Online

       

Subject: Software Eng (Grad) (SEIS)

CRN: 21387

Online: Sync Distributed | Lecture

Online

  Sanjay Mathur

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 their 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

732-01
Data Stores and Feature Design
 
See Details
N. Crawford
SEIS* 
02/05 - 05/17
25/15/0
Lecture
CRN 21389
3 Cr.
Size: 25
Enrolled: 15
Waitlisted: 0
02/05 - 05/17
M T W Th F Sa Su
 

5:30 pm
8:30 pm
SCC 331

5:30 pm
8:30 pm
Online

         

Subject: Software Eng (Grad) (SEIS)

CRN: 21389

CoFlex:In Person&Online Sync | Lecture

St Paul: Schoenecker Center 331

Online

Requirements Met:
     Software Data Mgmt Conc
     Software Technical Elective

  Nate Crawford

The real world is messy and a data scientist’s job will be to make sense of it. This course will dive into specialized data formats, such as time series, geospatial data, semi-structured and the data management systems and considerations required to load and extract information from them. Leveraging both creativity and context data scientists can design highly impactful features for machine learning applications by using SQL and Python to transform data. This course aims to provide hands-on experience working with these data formats and the power of developing novel metrics and features for analytics and machine learning. To do this effectively, this course will compare and contrast the conceptual designs of relational, data warehouse, NoSQL, and other data systems so that practitioners can utilize these systems to their fullest. Lastly, enterprises are investing heavily in data governance, data lineage, and metadata management to better preserve contextual information about their data. These systems will be covered as they will increasingly be required to enable disparate sources of information to be leveraged together and crucial for data scientists to build accurate and ethical models for deployment. Prerequisites: SEIS 630 and SEIS 631

3 Credits

732-02
Data Stores and Feature Design
 
See Details
N. Crawford
SEIS* 
02/05 - 05/17
25/9/0
Lecture
CRN 21390
3 Cr.
Size: 25
Enrolled: 9
Waitlisted: 0
02/05 - 05/17
M T W Th F Sa Su
     

5:30 pm
8:30 pm
SCC 331

5:30 pm
8:30 pm
Online

     

Subject: Software Eng (Grad) (SEIS)

CRN: 21390

CoFlex:In Person&Online Sync | Lecture

St Paul: Schoenecker Center 331

Online

Requirements Met:
     Software Data Mgmt Conc
     Software Technical Elective

  Nate Crawford

The real world is messy and a data scientist’s job will be to make sense of it. This course will dive into specialized data formats, such as time series, geospatial data, semi-structured and the data management systems and considerations required to load and extract information from them. Leveraging both creativity and context data scientists can design highly impactful features for machine learning applications by using SQL and Python to transform data. This course aims to provide hands-on experience working with these data formats and the power of developing novel metrics and features for analytics and machine learning. To do this effectively, this course will compare and contrast the conceptual designs of relational, data warehouse, NoSQL, and other data systems so that practitioners can utilize these systems to their fullest. Lastly, enterprises are investing heavily in data governance, data lineage, and metadata management to better preserve contextual information about their data. These systems will be covered as they will increasingly be required to enable disparate sources of information to be leveraged together and crucial for data scientists to build accurate and ethical models for deployment. Prerequisites: SEIS 630 and SEIS 631

3 Credits

739-01
SW Analysis, Design, and Impl
 
See Details
M. Dorin
 
02/05 - 05/17
25/16/0
Lecture
CRN 21843
3 Cr.
Size: 25
Enrolled: 16
Waitlisted: 0
02/05 - 05/17
M T W Th F Sa Su
     

5:30 pm
8:30 pm
SCC 408

5:30 pm
8:30 pm
Online

     

Subject: Software Eng (Grad) (SEIS)

CRN: 21843

CoFlex:In Person&Online Sync | Lecture

St Paul: Schoenecker Center 408

Online

  Michael Dorin

The course is a unique culmination of software development practices taught in the Master of Software Engineering program and provides students an opportunity to create and showcase a capstone project by implementing a full-stack application. This capstone class provides Software Engineering students with the unique opportunity to conceptualize, design, and implement a project related to their chosen domain. During the project, students build competence in a modern interactive and incremental development methodology; students will refine their acquisition skills and analysis of program requirements. Students will also learn software design patterns and create sophisticated architectural and operational diagrams. Automated software tests will be run, and continuous integration deployment principles will be performed. Prerequisite: SEIS 602, and SEIS 610, and SEIS 622

3 Credits

745-01
Data Lake Engineering
 
See Details
A. Roy
 
02/05 - 05/17
25/14/0
Lecture
CRN 22327
3 Cr.
Size: 25
Enrolled: 14
Waitlisted: 0
02/05 - 05/17
M T W Th F Sa Su

5:30 pm
8:30 pm
SCC 331

5:30 pm
8:30 pm
Online

           

Subject: Software Eng (Grad) (SEIS)

CRN: 22327

CoFlex:In Person&Online Sync | Lecture

St Paul: Schoenecker Center 331

Online

  Abhishek Roy

Individuals generate more data than ever before as they interact with websites, social platforms, streaming services, and increasingly data-driven industries like healthcare, retail, and energy. A growing number of connected devices continuously stream data using familiar web protocols and patterns. In our increasingly digital world, this data is depended upon to drive artificial intelligence and automation in near real-time. Before data can be relied upon to drive AI, however, it must be integrated, carefully curated, and governed at scale. It falls on data engineers to bring together data from various sources and contextualize those datasets to produce intelligence. Massively distributed Data Lake platforms empower engineers to work with datasets at a volume and variety not suitable for traditional, relational databases. This hands-on course focuses on data collection, storage, and analysis on a cloud Data Lake architecture, covering both batch and streaming pipelines. Additionally, it explores NoSQL database paradigms that facilitate low-latency queries over distributed and often unstructured or semi-structured datasets. Expect to learn fundamental concepts and gain practical experience working with different types of data, all within a reliable cloud lab environment. Prerequisites: (SEIS 601 or SEIS 603) and SEIS 630

3 Credits

745-02
Data Lake Engineering
 
See Details
C. Lunke
 
02/05 - 05/17
25/22/0
Lecture
CRN 22337
3 Cr.
Size: 25
Enrolled: 22
Waitlisted: 0
02/05 - 05/17
M T W Th F Sa Su
   

5:30 pm
8:30 pm
OSS 333

5:30 pm
8:30 pm
Online

       

Subject: Software Eng (Grad) (SEIS)

CRN: 22337

CoFlex:In Person&Online Sync | Lecture

St Paul: O'Shaughnessy Science Hall 333

Online

  Cort Lunke

Individuals generate more data than ever before as they interact with websites, social platforms, streaming services, and increasingly data-driven industries like healthcare, retail, and energy. A growing number of connected devices continuously stream data using familiar web protocols and patterns. In our increasingly digital world, this data is depended upon to drive artificial intelligence and automation in near real-time. Before data can be relied upon to drive AI, however, it must be integrated, carefully curated, and governed at scale. It falls on data engineers to bring together data from various sources and contextualize those datasets to produce intelligence. Massively distributed Data Lake platforms empower engineers to work with datasets at a volume and variety not suitable for traditional, relational databases. This hands-on course focuses on data collection, storage, and analysis on a cloud Data Lake architecture, covering both batch and streaming pipelines. Additionally, it explores NoSQL database paradigms that facilitate low-latency queries over distributed and often unstructured or semi-structured datasets. Expect to learn fundamental concepts and gain practical experience working with different types of data, all within a reliable cloud lab environment. Prerequisites: (SEIS 601 or SEIS 603) and SEIS 630

3 Credits

755-01
UI/UX Design
 
See Details
C. Schwab
 
02/05 - 05/17
25/5/0
Lecture
CRN 21851
3 Cr.
Size: 25
Enrolled: 5
Waitlisted: 0
02/05 - 05/17
M T W Th F Sa Su
   

5:30 pm
8:30 pm
OSS 327

5:30 pm
8:30 pm
Online

       

Subject: Software Eng (Grad) (SEIS)

CRN: 21851

CoFlex:In Person&Online Sync | Lecture

St Paul: O'Shaughnessy Science Hall 327

Online

  Carl Schwab

The course will introduce students to the methods and tools used in User Experience (UX) and User Interface (UI) design. UxDesign will provide an introduction to the foundation of each of the design stage of a product’s lifecycle/journey, and will provide a key understanding on the components required to ensure the end product will meet end user needs. Some of the topics discussed in the course include User Experience Design, Design Thinking, Human Centered Design, UxDesign techniques, such as: personas, user stories / user story mapping, storyboards, wireframing, UxDesign methods, such as: design methods, design prioritization, and rapid/interactive UI development; and coverage of key prototyping tools and software.

3 Credits

763-01
Machine Learning
 
See Details
C. Lai
 
02/05 - 05/17
29/29/0
Lecture
CRN 21394
3 Cr.
Size: 29
Enrolled: 29
Waitlisted: 0
02/05 - 05/17
M T W Th F Sa Su
     

5:30 pm
8:30 pm
SCC 314

5:30 pm
8:30 pm
Online

     

Subject: Software Eng (Grad) (SEIS)

CRN: 21394

CoFlex:In Person&Online Sync | Lecture

St Paul: Schoenecker Center 314

Online

  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. Prerequisites: SEIS 631 and 632, 632 can be taken concurrently.

3 Credits

763-02
Machine Learning
 
T 5:30 pm - 8:30 pm
M. Rege
 
02/05 - 05/17
20/18/0
Lecture
CRN 21395
3 Cr.
Size: 20
Enrolled: 18
Waitlisted: 0
02/05 - 05/17
M T W Th F Sa Su
 

5:30 pm
8:30 pm
Online

         

Subject: Software Eng (Grad) (SEIS)

CRN: 21395

Online: Sync Distributed | Lecture

Online

  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. Prerequisites: SEIS 631 and 632, 632 can be taken concurrently.

3 Credits

764-01
Artificial Intelligence
 
See Details
C. Lai
 
02/05 - 05/17
30/30/0
Lecture
CRN 21396
3 Cr.
Size: 30
Enrolled: 30
Waitlisted: 0
02/05 - 05/17
M T W Th F Sa Su

5:30 pm
8:30 pm
SCC 314

5:30 pm
8:30 pm
Online

           

Subject: Software Eng (Grad) (SEIS)

CRN: 21396

CoFlex:In Person&Online Sync | Lecture

St Paul: Schoenecker Center 314

Online

  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

3 Credits

765-01
ML Ops
 
See Details
J. Howard
 
02/05 - 05/17
25/5/0
Lecture
CRN 22749
3 Cr.
Size: 25
Enrolled: 5
Waitlisted: 0
02/05 - 05/17
M T W Th F Sa Su
   

5:30 pm
8:30 pm
OSS 326

5:30 pm
8:30 pm
Online

       

Subject: Software Eng (Grad) (SEIS)

CRN: 22749

CoFlex:In Person&Online Sync | Lecture

St Paul: O'Shaughnessy Science Hall 326

Online

  Jim Howard

In the rapidly evolving landscape of machine learning and artificial intelligence, the efficient deployment, management, and monitoring of machine learning models are crucial for successful and sustainable outcomes. The Machine Learning Operations (MLOps) course is designed to equip participants with the knowledge and skills needed to bridge the gap between machine learning development and operational deployment. Through a comprehensive curriculum, hands-on labs, and real-world case studies, participants will learn the essential principles and practices that enable seamless collaboration between data scientists, machine learning engineers, and operations teams. This course covers key concepts, tools, and strategies used in MLOps, helping organizations streamline their machine learning pipelines and enhance the reliability, scalability, and maintainability of their models. Prerequisite: SEIS 763

3 Credits

766-01
Vision AI
 
See Details
C. Lai
 
02/05 - 05/17
25/9/0
Lecture
CRN 22750
3 Cr.
Size: 25
Enrolled: 9
Waitlisted: 0
02/05 - 05/17
M T W Th F Sa Su
 

5:30 pm
8:30 pm
SCC 314

5:30 pm
8:30 pm
Online

         

Subject: Software Eng (Grad) (SEIS)

CRN: 22750

CoFlex:In Person&Online Sync | Lecture

St Paul: Schoenecker Center 314

Online

  Chih Lai

This course offers an interactive learning experience that delves into how machines perceive, analyze, and react to images and visual cues. You'll gain a greater understanding of images, videos, and their processing algorithms through hands-on activities. By working on practical tasks like manipulating images and experimenting with Generative AI models like GANs, you'll discover the vast applications of Vision AI. Industries such as entertainment and healthcare are already benefiting from these technologies, which enable machines to recognize patterns, predict outcomes, and even create art. With this course, you'll learn both the theoretical and practical aspects of Vision AI, empowering you to combine your creativity with cutting-edge technology. At the end of this course, students will develop skill sets in visual intelligence and be poised to shape the future of this exciting field. Prerequisite: SEIS 764 Artificial Intelligence

3 Credits


Advanced Search

Day(s) of the Week
Open/Closed Courses