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:
Students will find WPI’s data science degree options listed with engineering, science, and mathematics on the application form.
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:
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.
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
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)
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.
WPI Data Science faculty and PhD candidates are exploring every aspect of this burgeoning field. Together, they’re fueling breakthroughs that have direct, real-world impact in health, genetic analysis, sustainability, educational software, financial trading, and more.
A faculty advisor will help you design a Plan of Study for your dissertation as well as coursework in the core areas of data analytics and big data computing, statistical foundations and mathematical analytics, and business intelligence and innovation.
The Data Science Innovation Lab is dedicated workspace for project work by students in the Data Science program. Robust servers and computer clusters are available for experimenting with large-scale datasets throughout labs at WPI, including many interdisciplinary facilities.
State-of-the-art software programs:
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
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.
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.
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.
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.
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.
I am Associate Professor of Operations and Industrial Engineering at Worcester Polytechnic Institute (WPI), with courtesy professorships in Mathematical Sciences and Data Science. I hold a Ph.D. in Industrial Engineering from the University of Pittsburgh. My objective is to use science and technology to assist real human need by improving systems that serve vulnerable peoples, such as refugees and asylum seekers, survivors of human trafficking, and foster care.
