Engineering Science MS: Data Science Focus

The Engineering Science MS with a course focus in Data Science offers students a comprehensive education in big data and analysis. Students gain knowledge, expertise, and practical training in various areas, including data collection and management, data analytics, scalable data-driven discovery, and fundamental concepts. The curriculum combines solid theoretical foundations with hands-on training, ensuring graduates are well-prepared to address the analytics needs of employers. Additionally, students can explore diverse application areas, further expanding their understanding of real-world data challenges.

Required Core (24 credits)

CSE 560LEC - Data Models Query Lang Credits: 3
CSE 574LEC - Intro Machine Learning Credits: 3
EAS 501LEC - Introduction to Numerical Mathematics for Computing and Data Science Credits: 3
EAS 502LEC - Introduction to Probability Theory for Data Science Credits: 3
EAS 503LEC - Programming and Database Fundamentals for Data Scientists Credits: 3
EAS 504LEC - Applications of Data Science: Industry Overview Credits: 3
EAS 508LEC - Statistical Learning and Data Mining I Credits: 3
EAS 509LEC - Statistical Learning and Data Mining II Credits: 3

Electives & Culminating Experience (6 credits)

Electives (3-6 credits)

Number of elective credits may vary depending on choice made to complete the culminating experience. Be sure to complete enough elective credit to reach 30 minimum total credit hours.

CDA 500LEC - Special Topics Credits: 1 - 3
CSE 531LEC - Algorithms Anal & Dsgn 1 Credits: 3
CSE 535LEC - Information Retrieval Credits: 3
CSE 546LEC - Reinforcement Learning Credits: 3
CSE 562LEC - Database Systems Credits: 3
CSE 573LEC - Comp Vision & Image Proc Credits: 3
CSE 586LEC - Distributed Systems Credits: 3
CSE 587LEC - Data Intensive Computing Credits: 3
CSE 589LEC - Modern Network Concepts Credits: 3
CSE 601LEC - Data Mining and Bioinformatics Credits: 3
CSE 610LEC - Special Topics Credits: 3
CSE 674LEC - Advanced Machine Learning Credits: 3
CSE 676LEC - Deep Learning Credits: 3
CSE 740SEM - Seminars Credits: 1 - 3
EAS 595LEC - Special Topics Credits: 3
EAS 595 Course Topic: Fundamentals of AI
EE 539LEC - Principal Information Theory and Coding Credits: 3
IE 535LEC - Human Cent Design for Interact Credits: 3
IE 575LEC - Stochastic Methods Credits: 3
MAE 609LEC - High Perform Computing 1 Credits: 3
MGS 655LEC - Distributed Computing and Big Data Technologies Credits: 3
MGS 657LEC - Cloud Data Wrhsing & Data Engn Credits: 3
MTH 558LEC - Mathematical Finance Credits: 3
STA 511LEC - Advanced Statistical Computing Credits: 3
STA 517LEC - Categorical Data Analysis Credits: 3

Culminating Experience: Exam or Project (0-3 credits)

EAS 560TUT - Data Science Project Credits: 1 - 3

Total Credit Hours: 30

3.0 Degree GPA required

Program Learning Outcomes (PLOs)

1) Ability to demonstrate domain expertise with engineering methods and tools

2) Ability to develop relevant programming abilities

3) Ability to use basic engineering concepts flexibly in a variety of contexts.

4) Ability to apply domain-specific concepts and methods to solve problems in real-world contexts and will communicate these solutions effectively

5) Demonstrate an ability to conduct professional practice

SED Statement

This program is officially registered with the New York State Education Department (SED).