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SEIS: Software Eng (Grad)

603-01
Found. Software Dev-Python
 
MW 5:45 pm - 9:00 pm
S. Naqvi
 
05/31 - 07/21
28/27/0
Lecture
CRN 31223
3 Cr.
Size: 28
Enrolled: 27
Waitlisted: 0
05/31 - 07/21
M T W Th F Sa Su

5:45 pm
9:00 pm
OSS 326

 

5:45 pm
9:00 pm
OSS 326

       

Subject: Software Eng (Grad) (SEIS)

CRN: 31223

In Person | Lecture

St Paul: O'Shaughnessy Science Hall 326
     (Common Good capacity: 32 participants)

  Syed Naqvi

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

610-01
Software Engineering
 
MW 5:45 pm - 9:00 pm
R. Chiang
SEIS* 
05/31 - 07/21
25/12/0
Lecture
CRN 31224
3 Cr.
Size: 25
Enrolled: 12
Waitlisted: 0
05/31 - 07/21
M T W Th F Sa Su

5:45 pm
9:00 pm
OSS 313

 

5:45 pm
9:00 pm
OSS 313

       

Subject: Software Eng (Grad) (SEIS)

CRN: 31224

In Person | Lecture

St Paul: O'Shaughnessy Science Hall 313
     (Common Good capacity: 38 participants)

Requirements Met:
     Software Technical Elective

  Ron Chiang

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

630-01
Database Mgmt Systems & Design
 
TR 5:45 pm - 9:00 pm
R. Chiang
SEIS* 
05/31 - 07/21
28/16/0
Lecture
CRN 31226
3 Cr.
Size: 28
Enrolled: 16
Waitlisted: 0
05/31 - 07/21
M T W Th F Sa Su
 

5:45 pm
9:00 pm
Online

 

5:45 pm
9:00 pm
Online

     

Subject: Software Eng (Grad) (SEIS)

CRN: 31226

Online: Sync Distributed | Lecture

Online

Requirements Met:
     Software Data Mgmt Conc
     Software Technical Elective

  Ron Chiang

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

631-01
Foundations of Data Analysis
 
MW 5:45 pm - 9:00 pm
M. Rege
SEIS* 
05/31 - 07/21
28/19/0
Lecture
CRN 31227
3 Cr.
Size: 28
Enrolled: 19
Waitlisted: 0
05/31 - 07/21
M T W Th F Sa Su

5:45 pm
9:00 pm
Online

 

5:45 pm
9:00 pm
Online

       

Subject: Software Eng (Grad) (SEIS)

CRN: 31227

Online: Sync Distributed | Lecture

Online

Requirements Met:
     Software Technical Elective

  Manjeet Rege

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

632-01
Data Analytics & Visualization
 
TR 5:45 pm - 9:00 pm
M. Rege
LL.MSEIS* 
05/31 - 07/21
28/24/0
Lecture
CRN 31228
3 Cr.
Size: 28
Enrolled: 24
Waitlisted: 0
05/31 - 07/21
M T W Th F Sa Su
 

5:45 pm
9:00 pm
Online

 

5:45 pm
9:00 pm
Online

     

Subject: Software Eng (Grad) (SEIS)

CRN: 31228

Online: Sync Distributed | Lecture

Online

Requirements Met:
     LLM/MSL Elective
     Software Technical 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

763-01
Machine Learning
 
MW 5:45 pm - 9:00 pm
C. Lai
SEIS* 
05/31 - 07/21
28/24/0
Lecture
CRN 31231
3 Cr.
Size: 28
Enrolled: 24
Waitlisted: 0
05/31 - 07/21
M T W Th F Sa Su

5:45 pm
9:00 pm
Online

 

5:45 pm
9:00 pm
Online

       

Subject: Software Eng (Grad) (SEIS)

CRN: 31231

Online: Sync Distributed | Lecture

Online

Requirements Met:
     Software Technical Elective

  Chih Lai

Machine Learning builds computational systems that learn from and adapt to the data presented to them. It has become one of the essential pillars in information technology today and provides a basis for several applications we use daily in diverse domains such as engineering, medicine, finance, and commerce. This course covers widely used supervised and unsupervised machine learning algorithms used in industry in technical depth, discussing both the theoretical underpinnings of machine learning techniques and providing hands-on experience in implementing them. Additionally, students will also learn to evaluate effectiveness and avoid common pitfalls in applying machine learning to a given problem. Prerequisite: SEIS 603 and 631

3 Credits


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