MASTER OF SCIENCE DATA SCIENCEThe M.S. in Data Science program provides a strong foundation in the science of Big Data and its analysis by gathering in a single program the knowledge, expertise, and educational assets in data collection and management, data analytics, scalable data-driven pattern discovery, and the fundamental concepts behind these methods.Students who graduate from this two year master’s program will learn state-of-the-art methods for treating Big Data, be exposed to the cutting edge methods and theory forming the basis for the next generation of Big Data technology, and complete a project demonstrating that they can use fundamental concepts to design innovative methods for new application areas arising from business, government, security, medicine, biology, physical sciences, and the environment.

FEATURES• Program based on existing courses taught by regular faculty

members who are leaders in their fields.• Joint program between Computer Science and Engineering,

Electrical and Computer Engineering, Statistics, and Biostatistics

• Key feature is a capstone project that makes the theoretical knowledge gained in the program operational in realistic setting.

• Distance learning option via interactive video.

a new rigorous degree for the modern digital age

For more information

Web: datascience.umn.eduPhone: (612) 626-8020Email: datascience@umn.edu

ADMISSONSADMISSION REQUIREMENTS • Baccalaureate degree from an accredited college

or university in computer science, math, statistics, engineering, natural sciences, or a related field.

• GPA OF 3.00• Calculus (2 semesters)• Multivariable Calculus (1 semester)• Linear Algebra (1 semester)• Statistics (1 semester)• Programing experience in a general purpose

programming language (e.g., C, C++, Java, Python), including basic algorithms and data structures equivalent to the first two semesters of beginning computer science courses as part of an undergraduate Computer Science degree.

• Experience with mathematical software environments such as Matlab, R or the equivalent is a big plus.

APPLICATION PROCESSThe following documents will need to be submit-ted electronically as part of the application:• Statement of Purpose• Curriculum Vitae (CV)• Letters of Recommendation (3)• Transcripts• GRE• TOEFL (international students)

Master’s of Science in Data Science

PROGRAM CURRICULUMThe Data Science M.S. is a 31 credit program which can be completed in two years if taken full-time. Of the 31 credits 18 credits are required courses from three different tracks, 6 credits are elective, 1 credit is a research colloquium, and 6 credits are for the capstone project.

STATISTICS (6 CREDITS)• Regression• Model Fitting• Inference

ALGORITHMICS (6 CREDITS)• Data Mining• Machine Learning• Mathematical Algorithms• Visualization

INFRASTRUCTURE & LARGE SCALE COMPUTING (6 CREDITS)• Parallel/Distributed Computing• Data Base Management• Cloud Computing

CAPSTONE PROJECTStudents will complete a capstone project supervised by faculty. During the project, students will go through the entire process of designing and implementing a solution to a real-world problem. The problems and datasets students en-gage with will come from real-world projects from academic research or industry settings.

SAMPLE PROJECTS ALGORITHMICS• Interactive and perceptually accurate visualization of

multidimensional data.• Large-scale machine learning for data-driven discovery.• Enhancing spatial perception and presence in immersive

virtual environments.• Big tensor mining: theory scalable algorithms, and

applications.• Learning data analytics: providing actionable insights to

increase college student success.• Analyzing the Earth system using graph-based approaches.

STATISTICS• Analyzing Alzheimer’s disease neuroimaging initiative

(ADNI) data• Compare climate simulation model performances• Statistical inference in high dimensions

INFRASTRUCTURE• Storage systems for efficient processing and storing big

data.• Intelligent and high performance systems• Big data processing in mobile cloud platforms.• Automated out-of-core execution of parallel message-

passing applications.

FACULTY• Zach Almquist (Stat)• Arindam Banerjee (CS&E)• Daniel Boley (CS&E)• Bradley Carlin (Biostat)• Abhishek Chandra (CS&E)• Snigdhansu Chatterjee (Stat)• Vladimir Cherkassky (ECE)• Charles Doss (Stat)• David Du (CS&E)• Lynn Eberly (Biostat)• Tryphon Georgiou (ECE)• Charles Geyer (Stat)

• Weihua Guan (Biostat)• Jarvis Haupt (ECE)• James Hodges (Biostat)• John Hughes (Biostat)• Victoria Interrante (CS&E)• Galin Jones (Stat)• George Karypis (CS&E)• Mostafa Kaveh (ECE)• Daniel Keefe (CS&E)• Rui Kuang (CS&E)• Vipin Kumar (CS&E)• David Lilja (ECE)• Zhi-Quan Luo (ECE)• Arya Mazumdar (ECE)

• Mohamed Mokbel (CS&E)• Wei Pan (Biostat)• Keshab Parhi (ECE)• Cavan Reilly (Biostat)• Adam Joseph Rothman (Stat)• Yousef Saad (CS&E)• John Sartori (ECE)• Shashi Shekhar (CS&E)• Xiaotong Shen (Stat)• Jaideep Srivastava (CS&E)• Jon Weissman (CS&E)• Gongjun Xu (Stat)• Shuzhong Zhang (ISyE)• Hui Zou (Stat)