One of the first programs of its kind, in the nation, WPI’s interdisciplinary PhD program in Data Science recognizes that traditional data processing applications can no longer handle today’s large and complex datasets. New models are needed to handle big data; and knowledgeable graduates with expertise in turning those observations into meaningful recommendations are in high demand.

You’ll be working alongside faculty and industry partners to analyze, capture, search, share, store, transfer, query, and visualize huge amounts of data to solve real-world challenges. Some broad-stroke examples:

  • using predictive analytics to identify cyber threats
  • employing big data analytics to improve healthcare outcomes
  • empowering “smart” cities to make data-driven policy changes critical for societal well-being

Applying to the Data Science PhD Program

Students will find WPI’s data science degree options listed with engineering, science, and mathematics on the application form.

datascience

Curriculum

WPI’s Data Science PhD program is interdisciplinary, drawing from Computer Science, Mathematical Sciences, and the Foisie Business School. Together, courses and dissertation research revolve around five key areas:

  • Integrative Data Science
  • Business Intelligence and Case Studies
  • Data Access and Management
  • Data Analytics and Mining
  • Mathematical Analytics
  • Plan of Study

    PhD requirements include coursework as well as a research component. Together they total a minimum of 60 credit hours beyond the Data Science master’s degree requirement. Students entering the Ph.D. Program with a bachelor’s degree first complete the M.S. in data science at WPI using the M.S. Thesis option as first step towards their Ph.D. degree.

    Each Ph.D. student is assigned an Academic Advisor and together they formulate a Plan of Study that then is approved by the Data Science Steering Committee.

    1. Core coursework requirements in the 5 categories as detailed below – 21 credits / 7 courses
    2. Electives in coursework – 9 credits / 3 courses
    3. Research credits – 30 credits

      TOTAL 60 credits (beyond MS program)
  • Core Data Science Coursework Requirement (21 Credits / 7 Courses)

    A Ph.D. student must obtain core competency by taking 7 courses from the below list of Data Science core areas, with an A grade in 4 out of the 7 courses and at least a grade B for the remaining 3 courses,  within 2 years after starting the Ph.D. 60 program.

    Integrative Data Science  (Required)
    DS 501. Introduction to Data Science (3 credits)

    Mathematical Analytics 3 credits (Select at least one)
    DS 502. Statistical Methods for Data Science (3 credits)
    MA 542. Regression Analysis
    MA 554. Applied Multivariate Analysis

    Data Access and Management 3 credits (Select at least one)
    CS 542. Database Management Systems (3 credits)
    MIS571. Database Applications Development
    DS 503. Big Data Management (3 credits)
    CS 561. Advanced Topics in Database Systems

    Data Analytics and Mining 3 credits (Select at least one)
    CS 548. Knowledge Discovery and Data Mining (3 credits)
    DS 504. Big Data Analytics (3 credits)
    CS 539. Machine Learning

    Business Intelligence and Case Studies 3 credits (Select at least one)
    MIS 584. Business Intelligence
    MKT 568. Data Mining Business Applications

  • Electives

    Nine more course credits must be taken, with the listing of courses pre-approved as electives for the Data Science program found in the WPI Graduate Catalog under Data Science. Other graduate courses, graduate research credits, or ISGs may also be used, with prior approval of the Data Science Steering Committee. 

    Two elective courses designed as ramp up courses for students who may be lacking in sufficient background in either statistics or programming, respectively, are listed below. They can count towards the 33 credits of the DS MS degree, However, they cannot be used to meet the above requirements in five core categories.

    MA 511. Applied Statistics for Engineers and Scientists

    CS 5007. Intro to Applications of CS with Data Structures and Algorithms (Programming for non-CS majors)

  • Research Credits (30 credits)

    At least 30 credits must be research credits, consisting of DS 597 Directed Research and DS 699 Dissertation Research. Prior to Admission to Candidacy, a student may receive up to 18 credits of Pre-Dissertation Research under DS 597. Only after Admission to Candidacy by passing the Research Qualifier may a student receive credit toward Dissertation Research under DS 699.

    As part of the research component, PhD students pass a Qualifying Examination and propose and defend Dissertation Research.  Learn more about the Ph.D. milestones, including the Ph.D. Qualifying Examination, the Ph.D. dissertation proposal and Ph.D. final dissertation defense.

Project Based Learning

Turning Big Data into Actionable Information

Research

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DataScience
Close faculty interaction, cutting-edge equipment, and personal attention let you structure your program so it suits your individual career goals. You’ll leave with a degree that will help you succeed in your distinctive path.

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DataScience
Data science research gives you opportunities to work on grand challenge problems with societal importance, including topics such as cybersecurity, healthcare, and sustainability.

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Our data science graduate program offers expertise in computer science, statistics, and business topics while giving you essential opportunities to work with industry partners.

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DataScience
WPI’s innovative and multidisciplinary graduate program prepares students to become talented and effective leaders in this rapidly evolving field.

Cassandra
DB2
Hadoop
IBM Cognos
IBM ILOG CPLEX
IBM SPSS Modeler
InfoSphere Big Insights
InfoSphere Streams
Mahout
Maple
MATLAB

MySQL
Oracle Server
Palisade DecisionTools Suite
R
RapidMiner
SAS
Spotfire
SQL Server
Tableaux
Weka

Faculty Profiles

Faculty Profiles

Elke A. Rundensteiner

Elke Rundensteiner

Professor
Computer Science

As founding Director of the interdisciplinary Data Science program here at WPI, I take great pleasure in doing all in my power to support the Data Science community in all its facets from research collaborations, new educational initiatives to our innovative Graduate Qualifying projects at the graduate level.

[...]
Fatemeh Emdad

Fatemeh Emdad

Associate Teaching Professor
Computer Science

Professor Fatemeh Emdad completed her Ph.D. in Applied Mathematics with a concentration in Applied Mathematics at Colorado State University. She is the recipient of the top-ranked student academic award from the President of Shiraz University. She is the author of the book High Dimensional Data Analysis and more than 20 journal and conference articles. She completed her postdoctoral degree with the University of Texas Medical Branch and Shriners Hospital for Children Burn Unit when she was the NIH postdoctoral scientist fellow (under Ruth L.

[...]
Xiangnan Kong

Xiangnan Kong

Assistant Professor
Computer Science

Professor Kong’s research interests focus on data mining and machine learning, with emphasis on addressing the data science problems in biomedical and social applications. Data today involves an increasing number of data types that need to be handled differently from conventional data records, and an increasing number of data sources that need to be fused together. Dr. Kong is particularly interested in designing algorithms to tame data variety issues in various research fields, such as biomedical research, social computing, neuroscience, and business intelligence.

[...]
Yanhua Li

Yanhua Li

Assistant Professor
Computer Science

My broad research interests are in analyzing, understanding, and making sense of big data generated from various complex networks in many contexts, including urban network analysis, large-scale network data sampling, measurement, online social behavior modeling. My recent research focuses on exploring the challenges in managing and analyzing big data from urban networks, with an ultimate goal of improving human life quality and designing smarter cities.

[...]
Randy Clinton Paffenroth

Randy Paffenroth

Associate Professor
Mathematical Sciences

My research focuses on compressed sensing, machine learning, signal processing, and the interaction between mathematics, computer science and software engineering. My interests range from theoretical results to algorithms for tackling practical applied problems, and I enjoy problems most when mathematical results lead to efficient software implementations for big data. I am looking forward to working with students at all levels and backgrounds who share an interest in mathematics, software, or data.

[...]
Andrew C. Trapp

Andrew Trapp

Associate Professor
Foisie Business School

Decision-making is becoming increasingly complex as data expands and resources decrease. My research centers on using prescriptive (integer optimization) and predictive (machine learning) analytics, together with algorithms, to effectively allocate scarce resources. My work employs mathematical modeling and the development of methods and tools to benefit vulnerable and marginalized individuals, groups, and populations.

[...]

Getting Involved

Getting Involved

We’re data scientists – we use tools of the trade and big data analytics to innovate. Follow department happenings and industry trends via our social media channels on Facebook and LinkedIn.

Students walking around the fountain in the springtime

After Graduation