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The increasing importance of big data in engineering and applied sciences motivates ICME and the Department of Statistics to collaborate on a MSc track that trains students in data science with a computational focus.
 The focused MSc track is developed within the structure of the current MSc program in ICME. Students in the program will develop strong mathematical, statistical, computational, and programming skills through the ICME M.S. requirements and gain a fundamental data science education through 18 units of focused elective courses in the data science and related areas.
The program is geared towards two types of students: those with an engineering or applied sciences background who are interested in gaining a better understanding of mathematical and statistical underpinnings of data science; and those who are mathematically- and/or statistically- oriented and are looking to gain expertise in data science and applications.
Graduates from this program will be prepared to continue on to a Ph.D. in ICME, Statistics, MS&E, CS or application areas, or work as a data science professional in industry.
Degree requirements and details can be found by visiting: Data Science Track (Located at the bottom of the page after Requirement 5 of the Computational Science Track). 
Students may apply to the M.S. program directly through ICME and have the option to declare their preference for the Data Science program during the application process or anytime during the first year of their study. Selection of the students is made by the ICME admission committee which will have representation from the Data Science track steering committee.

Degree Requirements

The coursework follows the requirements of the traditional ICME M.S. degree with additional restrictions placed on the general and focused electives. As defined in the general Graduate Student Requirements, students have to maintain a grade point average (GPA) of 3.0 or better and classes must be taken at the 200 level or higher. In order to continue on to the Ph.D. in ICME, M.S. students have to maintain a GPA of at least 3.5. The total number of units in the degree is 45.

Requirement 1: Foundational (12 Units)
Students must demonstrate foundational knowledge in the field by completing the following core courses. Courses in this area must be taken for letter grades. Deviations from the core curriculum must be justified in writing and approved by the student’s ICME adviser and the chair of the ICME curriculum committee. Courses that are waived may not be counted towards the master’s degree.


  • CME 302 Numerical Linear Algebra 3
  • CME 304 Numerical Optimization 3
  • CME 305 Discrete Mathematics and Algorithms 3
  • CME 308 Stochastic Methods in Engineering (or an equivalent course approved by the committee) 3

Requirement 2: Data Science Electives (12 Units)
Data Science electives should demonstrate breadth of knowledge in the technical area. The elective course list is defined. Courses outside this list can be accepted as electives subject to approval. Petitions for approval should be submitted to student services.


  • STATS 200 Introduction to Statistical Inference 3
  • STATS 203 Introduction to Regression Models and Analysis of Variance 3
  • STATS 305 Introduction to Statistical Modeling 3
  • STATS 315A Modern Applied Statistics: Learning 2-3
  • STATS 315B Modern Applied Statistics: Data Mining 2-3
  • Requirement 3: Specialized Electives (9 Units)

Requirement 3: Specialized Electives (9 Units)

Choose three courses in specialized areas from the following list. Courses outside this list can be accepted as electives subject to approval. Petitions for approval should be submitted to student services.


  • BIOE 214 Representations and Algorithms for Computational Molecular Biology 3-4
  • BIOMEDIN 215 Data Driven Medicine 3
  • BIOS 221 Modern Statistics for Modern Biology 3
  • CS 224W Social and Information Network Analysis 3-4
  • CS 347 Parallel and Distributed Data Mining 3
  • CS 448 Topics in Computer Graphics 3-4
  • ENERGY 240 Geostatistics 2-3
  • OIT 367 Analytics from Big Data 4
  • PSYCH 240A Human Neuroimaging Methods 3
  • PSYCH 303 Human and Machine Hearing 3
  • STATS 290 Paradigms for Computing with Data 3
  • STATS 366 Modern Statistics for Modern Biology 3

Requirement 4: Advanced Scientific Programming and High Performance Computing Core (6 Units)
To ensure that students have a strong foundation in programming students are required to take 6 units of advanced programming, with at least 3 units in parallel computing. Approved courses for advanced scientific programming include:


  • CME 212 Advanced Programming for Scientists and Engineers 3
  • CME 214 Software Design in Modern Fortran for Scientists and Engineers 3
  • CS 107 Computer Organization and Systems 3-5
  • CS 249B Large-scale Software Development 3
  • CME 213 Introduction to parallel Computing using MPI, openMP, and CUDA 3
  • CME 342 Parallel Methods in Numerical Analysis 3
  • CS 149 Parallel Computing 3-4
  • CS 315A Parallel Computer Architecture and Programming 3
  • CS 315B Parallel Computing Research Project 3
  • CS 316 Advanced Multi-Core Systems 3
  • CS 344C, offered in previous years, may also be counted
  • 3

For DS students, the 1-unit course in MapReduce offered by ICME annually is also highly recommended. Courses outside this list can be accepted as electives subject to approval. Petitions for approval should be submitted to student Services.

Requirement 5: Practical Component (6 Units)
Students are required to take 6 units of practical components that may include and combination of:

  • A capstone project supervised by a faculty member and approved by the steering committee. The capstone project should be computational in nature. Students should submit a one-page proposal, supported by the faculty member, to the steering committee ([email protected]) for approval.
  • Clinics, such as the new Data Science Clinic offered by ICME starting Winter 2013.
  • Other courses that have a strong hands-on and practical component, such as STATS 390 Consulting Workshop.

Read more.

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