## ##   * Overview
  * Program Requirements
Khoury College of Computer Sciences and the Department of Electrical and
Computer Engineering jointly offer an interdisciplinary Master of Science in
Data Science. This program is designed to give students a comprehensive
framework for reasoning about data. Students engage in extensive coursework
intended to develop depth in data collection, storage, retrieval,
manipulation, visualization, modeling, and interpretation. Students are also
able to choose elective courses from a variety of offerings in Khoury, the
College of Engineering, and throughout the campus to explore areas that
generate data or specialized data science applications. Successful program
graduates are well positioned to attain data scientist and data engineer
positions in a fast-growing field or to progress into doctoral degrees in
related disciplines.
During the admissions process, applicants take a pretest to determine if the
[Master of Science in Data Science](/graduate/computer-information-
science/computer-science/data-science-ms/index.html) or Master of Science in
Data Science—Align fits better with their current skill level. In addition,
prospective applicants work with recruitment and enrollment coaching teams to
select the appropriate program before applying.
The Master of Science in Data Science—Align curriculum is specifically
designed to prepare incoming students without any prior programming
experience. During the first semester of year one, students are expected to
take foundational courses in computer science fundamentals, as well as a
course in data structures/discrete mathematics. During their second semester,
students will take coursework in programming for data science, as well as
linear algebra and probability.
* * *
  * Concentrations and course offerings may vary by campus and/or by program modality. Please consult with your advisor or admissions coach for the course availability each term at your campus or within your program modality.
  * Certain options within the program may be  _required_ at certain campuses or for certain program modalities. Please consult with your advisor or admissions coach for requirements at your campus or for your program modality. 
* * *
Complete all courses and requirements listed below unless otherwise indicated.
Students should refer to the course numbering table for [graduate course
leveling](/graduate/academic-policies-procedures/records-transcripts/).
## Align Bridge Coursework
Students are required to complete all bridge courses unless otherwise
determined by the program.
A grade of B or higher is required in each course.
Course List Code | Title | Hours  
---|---|---  
_Fundamentals_|  
[CS 5001](/search/?P=CS%205001 "CS 5001")  
and [CS 5003](/search/?P=CS%205003 "CS 5003")| Intensive Foundations of
Computer Science  
and Recitation for CS 5001| 4  
_Discrete Structures_|  
[CS 5002](/search/?P=CS%205002 "CS 5002")| Discrete Structures| 4  
_Programming for Data Science_|  
[DS 5010](/search/?P=DS%205010 "DS 5010")| Introduction to Programming for
Data Science| 4  
Additional Align Coursework|  
[DS 5020](/search/?P=DS%205020 "DS 5020")| Introduction to Linear Algebra and
Probability for Data Science| 4  
## Core Requirements
A cumulative GPA of 3.000 or higher is required in the following core courses:
Course List Code | Title | Hours  
---|---|---  
Complete 20 semester hours from the following:|  
Algorithms|  
Complete 4 semester hours from the following:| 4  
[CS 5800](/search/?P=CS%205800 "CS 5800")| Algorithms|  
[EECE 7205](/search/?P=EECE%207205 "EECE 7205")| Fundamentals of Computer
Engineering|  
Data Management and Processing|  
[DS 5110](/search/?P=DS%205110 "DS 5110")| Introduction to Data Management and
Processing| 4  
Machine Learning and Data Mining|  
[DS 5220](/search/?P=DS%205220 "DS 5220")| Supervised Machine Learning and
Learning Theory| 4  
[DS 5230](/search/?P=DS%205230 "DS 5230")| Unsupervised Machine Learning and
Data Mining| 4  
Presentation and Visualization|  
[DS 5500](/search/?P=DS%205500 "DS 5500")| Data Science Capstone| 4  
## Electives1
Course List Code | Title | Hours  
---|---|---  
Complete 12 semester hours from the following:| 12  
Khoury College of Computer Sciences|  
[CS 5100](/search/?P=CS%205100 "CS 5100")| Foundations of Artificial
Intelligence|  
[CS 5180](/search/?P=CS%205180 "CS 5180")| Reinforcement Learning and
Sequential Decision Making|  
[CS 5200](/search/?P=CS%205200 "CS 5200")| Database Management Systems|  
[CS 5330](/search/?P=CS%205330 "CS 5330")| Pattern Recognition and Computer
Vision|  
[CS 5340](/search/?P=CS%205340 "CS 5340")| Computer/Human Interaction|  
[CS 5610](/search/?P=CS%205610 "CS 5610")| Web Development|  
[CS 6120](/search/?P=CS%206120 "CS 6120")| Natural Language Processing|  
[CS 6200](/search/?P=CS%206200 "CS 6200")| Information Retrieval|  
[CS 6240](/search/?P=CS%206240 "CS 6240")| Large-Scale Parallel Data
Processing|  
[CS 6350](/search/?P=CS%206350 "CS 6350")| Empirical Research Methods|  
[CS 6620](/search/?P=CS%206620 "CS 6620")| Fundamentals of Cloud Computing|  
[CS 6650](/search/?P=CS%206650 "CS 6650")| Building Scalable Distributed
Systems|  
[CS 7140](/search/?P=CS%207140 "CS 7140")| Advanced Machine Learning|  
[CS 7150](/search/?P=CS%207150 "CS 7150")| Deep Learning|  
[CS 7180](/search/?P=CS%207180 "CS 7180")| Special Topics in Artificial
Intelligence|  
[CS 7200](/search/?P=CS%207200 "CS 7200")| Statistical Methods for Computer
Science|  
[CS 7250](/search/?P=CS%207250 "CS 7250")| Information Visualization: Theory
and Applications|  
[CS 7280](/search/?P=CS%207280 "CS 7280")| Special Topics in Database
Management|  
[CS 7290](/search/?P=CS%207290 "CS 7290")| Special Topics in Data Science|  
[DS 5983](/search/?P=DS%205983 "DS 5983")| Topics in Data Science|  
[DS 7990](/search/?P=DS%207990 "DS 7990")| Thesis|  
[DS 7995](/search/?P=DS%207995 "DS 7995")| Project|  
College of Engineering|  
[CIVE 7100](/search/?P=CIVE%207100 "CIVE 7100")| Time Series and Geospatial
Data Sciences|  
[EECE 5612](/search/?P=EECE%205612 "EECE 5612")| Statistical Inference: An
Introduction for Engineers and Data Analysts|  
[EECE 5639](/search/?P=EECE%205639 "EECE 5639")| Computer Vision|  
[EECE 5640](/search/?P=EECE%205640 "EECE 5640")| High-Performance Computing|  
[EECE 5645](/search/?P=EECE%205645 "EECE 5645")| Parallel Processing for Data
Analytics|  
[EECE 7337](/search/?P=EECE%207337 "EECE 7337")| Information Theory|  
[EECE 7370](/search/?P=EECE%207370 "EECE 7370")| Advanced Computer Vision|  
[EECE 7397](/search/?P=EECE%207397 "EECE 7397")| Advanced Machine Learning|  
[IE 6700](/search/?P=IE%206700 "IE 6700")| Data Management for Analytics|  
[IE 7280](/search/?P=IE%207280 "IE 7280")| Statistical Methods in Engineering|  
College of Social Sciences and Humanities|  
[ECON 5140](/search/?P=ECON%205140 "ECON 5140")| Applied Econometrics|  
[PPUA 5261](/search/?P=PPUA%205261 "PPUA 5261")| Dynamic Modeling for
Environmental Decision Making|  
[PPUA 5262](/search/?P=PPUA%205262 "PPUA 5262")| Big Data for Cities|  
[PPUA 5263](/search/?P=PPUA%205263 "PPUA 5263")| Geographic Information
Systems for Urban and Regional Policy|  
[PPUA 5266](/search/?P=PPUA%205266 "PPUA 5266")| Urban Theory and Science|  
[PPUA 7237](/search/?P=PPUA%207237 "PPUA 7237")| Advanced Spatial Analysis of
Urban Systems|  
College of Science|  
[ENVR 5563](/search/?P=ENVR%205563 "ENVR 5563")| Advanced Spatial Analysis|  
[PHYS 5116](/search/?P=PHYS%205116 "PHYS 5116")| Network Science 1|  
[PHYS 7305](/search/?P=PHYS%207305 "PHYS 7305")| Statistical Physics|  
[PHYS 7321](/search/?P=PHYS%207321 "PHYS 7321")| Computational Physics|  
Bouvé College of Health Sciences|  
[PHTH 5202](/search/?P=PHTH%205202 "PHTH 5202")| Introduction to Epidemiology|  
[PHTH 5210](/search/?P=PHTH%205210 "PHTH 5210")| Biostatistics in Public
Health|  
[PHTH 6224](/search/?P=PHTH%206224 "PHTH 6224")| Social Epidemiology|  
College of Arts, Media and Design|  
[GSND 5110](/search/?P=GSND%205110 "GSND 5110")| Game Design and Analysis|  
[GSND 6350](/search/?P=GSND%206350 "GSND 6350")| Data-Driven Player Modeling|  
## Program Credit/GPA Requirements
40–48 total semester hours required  
Minimum 3.000 GPA required
1
Students taking electives worth less than 4 SH (i.e., Bouvé courses) should
enroll for an accompanying data science project course in the same semester to
bring the cumulative SH to 4. In order to earn this additional 1 SH, students
are expected to work with faculty to design an additional project in line with
the curricular aims of their chosen elective and the data science core
learning outcomes.