Data Science Master's Degree Program
Tackle today’s high tech data-driven problems
Data plays an ever-increasing role in today’s society and the increase in volume, velocity and variety of data has driven the demand in employees skilled in data science and analytics. The “2020 Emerging Jobs Report” from LinkedIn include Artificial Intelligence Specialist and Data Scientist in the top three emerging jobs in the U.S.
Program Key Highlights
Choose Your Specialization
Big Data
Develop a deep understanding of complex data sets and how to solve business decisions.
Machine Learning
Gain the experience that is sought after in nearly every industry by learning to develop repeatable procedures through algorithms used in applications, AI and predictive learning.
Statistics and Modeling
Represent, manipulate, analyze, and interpret big data using exploratory, inferential methods and use packages/tools in effective ways.
Part-Time, Online Options
*full-time completed in 2 years, part-time completed in 3 years.
In-Demand Data Science Industries and Careers
Data Scientists are in-demand and industries everywhere are seeking advance learners in data science. It is one of the few degrees that has few boundaries to where it can lead you.
- Communication/Media
- Construction
- Finance
- Government
- Gaming
- Healthcare
- Insurance
- Manufacturing
- Retail
Students with an advanced Master's in Data Science degree can seek careers in professions including:
- Applications Architect
- Big Data
- Data Analyst
- Data Scientist
- Data Engineer
- Systems Software
- Machine Learning Scientist
- Business Intelligence Analyst
- Search Marketing Strategist
Our M.S. in data science program is a STEM-designated program, which means international students are eligible to apply for 36 months of Optional Practical Training, allowing employers to potentially hire international students for up to three years instead of only one.
Take the next step towards your future
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Request Information
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Admission Requirements
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Application Details
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Application Deadline
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Learning Outcomes
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Faculty
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Course Work
To be eligible for admission to the Graduate School at Marquette University, applicants must meet the following requirements:
- A bachelor's degree in data science, computer science, statistics, information science, mathematics, or related field from a regionally accredited institution or international equivalent must be completed prior to starting graduate school.
- A minimum grade point average (GPA) of 3.00 on a 4.00 scale.
- Basic computational thinking competency as demonstrated by completion of an introductory course (e.g., COSC 1010 Introduction to Software Development or equivalent) or proof of successful completion of a recommended introductory online Python programming course as recommended by the program director.
- Completion of a foundational statistics course (e.g., MATH 1700 Modern Elementary Statistics PSYC 2001 Psychological Measurements and Statistics: Lecture Only, SOCI 2060 Social Statistics or equivalent) with familiarity in programs such as R, MATLAB, SAS, Stata, etc or proof of successful completion of a recommended introductory online foundational statistics course as recommended by the program director.
- Demonstrated English proficiency for non-U.S. citizens.
Application Requirements
Read all application instructions prior to beginning an application.
- A completed online application form and fee.
- Transcripts:
- Submit copies of all current and previous college/universities except Marquette1
- Statement of purpose essay outlining relevant work experience or education, career goals, possible areas of interest and reasons for seeking admission to this program.
- For international applicants who have not attended an English-speaking university only: TOEFL score or other acceptable proof of English proficiency.
- GRE (recommended for domestic applicants, required for international applicants)
- Resume (recommended)
- Three letters of recommendation (recommended)
1Upon admission, final official transcripts from all previously attended colleges/universities, with certified English translations if original language is not English, must be submitted to the Graduate School within the first five weeks of the term of admission or a hold preventing registration for future terms will be placed on the student’s record.
January 15: For priority consideration. After the priority consideration, applications are reviewed on a rolling basis and should be submitted any time before the following dates:
- Fall term admissions- August 1 (June 1 for international applicants)
- Spring term admissions- December 15 (October 1 for international applicants)
Applicants who wish to be considered for merit-based financial aid (graduate assistantships/scholarships) should be aware of the merit-based financial aid deadlines by which all applicant materials must be received by the Graduate School:
- Fall term: February 15
- Spring term: November 15
Students completing this certificate program will be able to:
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Represent, manipulate, analyze and interpret big data using exploratory, inferential methods and use packages/tools in effective ways.
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Recognize and analyze ethical and social issues in data science.
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Apply and evaluate complex models to devise solutions for data science tasks.
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Interpret data science analysis outcomes and draw conclusions using effective written, graphical and verbal tools and techniques.
Data science courses led by respected faculty
Expect excellence from the program, led by respected faculty members including:
The MS in Data Science offers specializations in Machine Learning and Big Data, with the choice to complete the degree with the following plan options.
Plan A: Thesis Option Completion Requirements
- Common core in data science- 18 credits
- Thesis- 6 credits
- Approved Electives- 6 credits
Total Credits: 30
Plan B: Non-Thesis Option Completion Requirements
- Common core in data science-18 credits
- Approved Electives- 15 credits
Total Credits: 33
COMMON CORE
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COSC 6510
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Data Intelligence
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3
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COSC 6520
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Data Analytics
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3
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COSC 5500
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Visual Analytics
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3
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COSC 6820
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Data Ethics
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3
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COSC 6570
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Data at Scale
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3
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COSC 5610
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Data Mining
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3
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No Specialization (Generalist)
Common Core
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18
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Electives
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Graduate courses approved by advisor
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15
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Total Credit Hours
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33
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Specialization in Machine Learning
Common Core
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18
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COSC 5800
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Principles of Database Systems
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3
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COSC 5600
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Fundamentals of AI
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3
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COSC 6330
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Advanced Machine Learning
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3
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Electives
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Graduate courses approved by advisor
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6
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Total Credit Hours
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33
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Specialization in Big Data
Common Core
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18
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COSC 5800
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Principles of Database Systems
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3
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COSC 6060
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Parallel and Distributed Systems
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3
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COSC 6380
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Advanced Database Systems
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3
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Electives
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Graduate courses approved by advisor
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6
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Total Credit Hours
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33
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Specialization in Statistics and Modeling
Common Core
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18
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Choose three of the following:
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9
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MSSC 5540
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Numerical Analysis
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3
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MSSC 5630
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Mathematical Modeling and Analysis
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3
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MSSC 5650
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Theory of Optimization
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3
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MSSC 5700
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Theory of Probability
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3
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MSSC 5710
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Mathematical Statistics
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3
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MSSC 5750
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Computational Statistics
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3
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MSSC 5760
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Time Series Analysis
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3
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MSSC 5790
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Bayesian Statistics
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3
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MSSC 6000
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Scientific Computing
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3
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MSSC 6010
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Computational Probability
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3
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MSSC 6020
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Statistical Simulation
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3
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MSSC 6040
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Applied Linear Algebra
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3
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MSSC 6250
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Statistical Machine Learning
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3
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EECE 6020
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Probability and Random Processes in Engineering
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3
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EECE 6510
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Optimal and Adaptive Digital Signal Processing
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3
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EECE 6932
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Advanced Topics in Electrical and Computer Engineering
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3
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Electives
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Graduate courses approved by advisor
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6
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Total Credit Hours
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33
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