Curriculum
Master's Program in Data Science and Engineering
Curriculum
General Program Requirements
30 credits required for degree completion
Minimum GPA of 3.0 is required for graduation
Education Objectives
This program is an applied program with projects and training working with software and algorithms used throughout the field. We teach in both R and Python and use cloud computing. Students will gains hands on experience, and work with faculty accross fields in their capstone projects. The program length of 1 1/2 and 2 years.
In their careers, graduates of the Master's Program in Data Science and Engineering (DSE) will:
- apply data driven methodology to a chosen domain specialization to meet the needs of society
- communicate clearly and assume leadership roles
- contribute to the field of applied data science, participate in professional societies, maintain current knowledge in the field, and pursue advanced studies
Degree Requirements (scroll down for a default schedule, starting Fall 2019)
Required Courses |
18 credits |
|
Six courses (3 cr. each)
|
|
Supervised 3-credit Project or 6-credit Thesis Course |
3-6 Credits |
|
3 credits
|
|
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6 credits
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Electives Courses (3 cr. each) |
6-9 credits |
| Currently approved electives include: | |
|
BME I5100: Biomedical Signal Processing and Signal Modeling
BME I5000: Biomedical Imaging
BME I4200: Organ Transport and Pharmacokinetics
ChE I5500: Interfacial Phenomena
ChE I5700: Advanced Materials Engineering
ChE I8900: Nanotechnology
CE H1200/CE 56600, Engineering Hydrology
CSc I0600. Advanced Algorithms
CSc I0500: Computer Graphics
CSc I1000: Database Systems I
CSc I1100 Database Systems II
CSc1900. Machine Learning and Data Mining
CSc I4633: Multimedia
CSc I6730 Data Reduction in Physical Sciences
CSc I6716: Computer Vision
CSc I0802 Web-based Geographical Information System (Web-GIS)
CSc IA804: Massively Data Parallel Programming on GPUs
EE I2200: Image Processing
EE I5501: Introduction to Robotics
EE G3300: Advanced Mobile Robotics
EE I6400: Computer-Aided Digital VLSI Design
EE G6530 Biologically Inspired Computation for Engineering Problems
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Default Schedule:
First semester (Fall 2019):
DSE I1020 Introduction to Data Science, DSE I1030 Applied Statistics, DSE I2700 Visual Analytics
Second semester (Spring 2020):
DSE I2100 Applied Machine Learning and Data Mining, DSE I2400 Data Engineering: Infrastructure and Applications, DSE I2450 Big Data and Scalable Computation
Third semester (Fall 2020): (Thesis Option)
Elective 1
DSE I9800 Master's Thesis
Fourth Semester (Spring 2021): (Thesis Option)
Elective 2
OR
Third Semester (Fall 2020): (Project Option)
Elective 1
Elective 2
DSE I9800 Master's Project
Fourth Semester (Spring 2021): (Project Option)
Elective 3
North Academic Center 8/206
160 Convent Avenue
New York, NY 10031