Data Science Master’s Program



The Data Science Master’s degree at the Johns Hopkins University will provide the training in applied mathematics, statistics and computer science to serve as the basis for an understanding, and appreciation, of existing data science tools.  Our program aims to produce the next generation of leaders in data science by emphasizing mastery of the skills needed to translate real-world data-driven problems in mathematical ones, and then solving these problems by using a diverse collection of scientific tools.



In addition to Introduction to Data Science (EN.553.636), students will take one course in each of the four core areas:  Statistics, Machine Learning, Optimization, and Computing.  Students will decide on an area of focus and take three courses in either Computational Medicine, Computational Machine Learning, Computer Vision, Computational Finance, Mathematics of Data Science, Language and Speech, or Statistical Theory.  The final capstone project is course EN.553.806 Capstone Experience in Data Science, and include a research topic approved by the faculty advisor and the Internal Oversight Committee, and a written paper. The goal of the final course and written paper is to allow the student to apply data analysis techniques learned in the program, and possibly to extend those ideas to more general settings or to new application areas.  Lastly, the paper will be summarized in a poster session organized at the end of each semester.

    • A two-day orientation program will precede the first Fall semester of enrollment.
    • EN.553.636 Introduction to Data Science, and

    One course in each of the four Core Areas below.  Courses chosen in this section must be distinct from the courses used to satisfy requirements 2 and 3.

    • Statistics – Introduction to Statistics (EN.553.630), Bayesian Statistics (EN.553.632), Statistical Theory I (EN.553.730), Statistical Theory II (EN.553.731)
    • Machine Learning – Statistical Machine Learning: Methods, Theory, and Applications (PH.140.644), Machine Learning (EN.601.675), Statistical Machine Learning (EN.601.775), Machine Learning (EN.553.740)
    • Optimization – Nonlinear Optimization I (EN.553.761), Nonlinear Optimization II (EN.553.762), Convex Optimization (EN.553.765)
    • Computing – Computing for Applied Mathematics (EN.553.688), Parallel Programming (EN.601.620)
  • Three courses from one of the following focus areas.

    Computational Medicine:  CHOOSE 3
    AS.410.633     Introduction to Bioinformatics
    AS.410.635     Bioinformatics:  Tools for Genome Analysis
    AS.410.661     Methods in Proteomics
    AS.410.671     Gene Expression Data Analysis and Visualization
    EN.553.650    Computational Molecular Medicine
    EN.605.620    Algorithms for Bioinformatics -or- EN.605.621
    EN.605.621     Foundations of Algorithms -or- EN.605.620
    EN.605.653     Computational Genomics
    EN.605.754     Analysis of Gene Expression and High-Content Biological Data

    Computational Machine Learning: CHOOSE 3
    PH.140.644     Statistical Machine Learning: Methods, Theory, and Applications
    EN.520.612     Machine Learning for Signal Processing
    EN.520.647     Introduction to Information Theory and Coding
    EN.520.648     Compressed Sensing and Sparse Recovery
    EN.520.651     Random Signal Analysis
    EN.520.666     Information Extraction
    EN.553.602     Research and Design in Applied Mathematics: Data Mining
    EN.553.740     Machine Learning I
    EN.553.743      Graphical Models
    EN.601.675      Machine Learning
    EN.601.676      Machine Learning: Data to Models
    EN.601.677      Causal Inference
    EN.601.679      Representation Learning
    EN.601.681      Machine Learning: Optimization
    EN.601.682      Machine Learning: Deep Learning
    EN.601.775       Statistical Machine Learning
    EN.601.800      Unsupervised Learning: Big Data to Low-Dimensional Representations

    Computer Vision: CHOOSE 3
    EN.520.648          Compressed Sensing and Sparse Recovery
    EN.601.661          Computer Vision
    EN.601.682          Machine Learning: Deep Learning
    EN.601.783          Vision as Bayesian Inference

    Computational Finance:  CHOOSE 3
    EN.553.627          Stochastic Processes and Applications to Finance I
    EN.553.628          Stochastic Processes and Applications to Finance II
    EN.553.641          Equity Markets and Quantitative Trading
    EN.553.642          Investment Science
    EN.553.644          Introduction to Financial Derivatives
    EN.553.645          Interest Rate and Credit Derivatives
    EN.553.646          Risk Measurement and Management in Financial Markets
    EN.553.647          Quantitative Portfolio Theory & Performance Analysis
    EN.553.648          Financial Engineering and Structured Products
    EN.553.649          Advanced Equity Derivatives
    EN.553.753          Commodity Markets and Trade Finance

    Mathematics of Data Science:  CHOOSE 3
    EN.553.633          Monte Carlo Methods
    EN.553.665          Introduction to Convexity
    EN.553.738          High-Dimensional Approximation, Probability, and Statistical Learning
    EN.553.740          Machine Learning I
    EN.553.741          Machine Learning II
    EN.553.761          Nonlinear Optimization I
    EN.553.762          Nonlinear Optimization II
    EN.553.763          Stochastic Search and Optimization
    EN.553.765          Convex Optimization
    EN.533.766          Combinatorial Optimization
    EN.553.792          Matrix Analysis and Linear Algebra
    EN.601.634          Randomized and Big Data Algorithms
    EN.601.635          Appromization Algorithms

    Language and Speech:  CHOOSE 3
    AS.050.617          Semantics I
    AS.050.622          Semantics II
    AS.050.620          Syntax
    AS.050.625          Phonology
    EN.520.666          Information Extraction
    EN.520.680          Speech and Auditory Processing by Humans and Machines
    EN.601.665          Natural Language Processing
    EN.601.765          Machine Learning: Linguistic and Sequence Modeling

    Statistical Theory:  CHOOSE 3
    PH.140.644          Statistical Machine Learning: Methods, Theory, and Applications
    EN.533.630          Introduction to Statistics
    EN.553.730          Statistical Theory
    EN.553.731          Statistical Theory II
    EN.553.632          Bayesian Statistics
    EN.553.735          Topics in Statistical Pattern Recognition
    EN.553.737          Distribution-free Statistics and Resampling Methods
    EN.553.738          High-Dimensional Approximation, Probability, and Statistical Learning
    EN.553.739          Statistical Pattern Recognition Theory & Methods
    EN.601.677          Causal Inference
    EN.601.775          Statistical Machine Learning
    • The program requires the student to take one elective course. To maximize a student’s flexibility in choosing this course, the student may choose from any course listed in the Areas of Focus as well as EN.520.620.
  • Capstone Experience in Data Science (EN.553.806), or another project-oriented course approved by the research supervisor, academic advisor and the Internal Oversight Committee.  Students must complete a Proposal Request for the Capstone Experience in Data Science form and follow instructions to submit for approval before being permitted to enroll in EN.553.806.


    1. The student must find and contact a research supervisor who will agree to supervise the capstone experience.  The research supervisor must be a JHU faculty member.
    2. The student must complete a proposal form, describing the project goals, and submit to their academic advisor, who will in turn send it to the Internal Oversight Committee for approval.
    3. The proposal will include the following and must be submitted using the approved proposal request form (above):
      • Title of proposed project
      • Project description, with sufficient details (e.g., 200 words)
      • Completion timeline
      • Name(s) and signature(s) of faculty supervisor(s)
    4. Upon approval, the student will be permitted to register for EN.553.806: Capstone Experience in Data Science.
    5. Upon completion, the research supervisor will provide a Pass/Fail (P/F) grade.
    6. As part of the experience, the student must write a paper or research report that must be approved the the research supervisor.  The final paper should be 6-12 pages in latex full-page format (1 inch margins, 12 point Times font) or ms-word equivalent.
    7. The written paper will be summarized in a poster presented in a poster session organized at the end of each semester.
    • Minimum Grade Requirements:  A course grade of B- or higher is required to meet all course requirements.
    • Academic probation:  Any student receiving either one grade of D+, D, or F or two grades of C(+/–) during their program of study will be placed on academic probation. If an additional grade below B– is received before the course is repeated and successfully completed, the student will be dismissed. If the student retakes the course with a grade of B- or above, the probationary status will be lifted.
    • Retake policy: Students are allowed to retake a course only once (per course), upon agreement of both the student’s advisor and the course instructor.
    • Students must take an approved online data ethics course, such as the one available at
    • Students are required to complete the online responsible conduct of research training.
    • Students are required to complete the Academic Ethics online course.  Students will be auto-enrolled in this course upon matriculation.
    • Beginning Fall 2021, all foreign students for whom English is not the first language, will be required to undergo assessment, and possible placement in one or more professional communication courses to help with grammar, pronunciation and idiomatic expression.  More information about ESL courses, can be found on the Center for Leadership Education website.


  • Prior to First Semester: Orientation Program (2 days)
    Year 1:  FALL Introduction to Data Science
    (4 courses) Computing (Core)
    Optimization (Core)
    OPTION A Core Area 3
    OPTION B Area of Focus 1
    Year 1: Intersession Online Data Science Ethics Course
    Year 1:  SPRING OPTION A Core Area 4
    (4 courses) Statistics
    Area of Focus 2
    Area of Focus 3
    OPTION B Core Area 4
    Area of Focus 1
    Area of Focus 2
    Area of Focus 3 (or Elective)
    Year 2:  FALL OR SPRING Capstone Experience
    (2 courses) OPTION A Elective
    OPTION B Elective (or Area of Focus)



  • We are now accepting applications for the Spring 2022 semester.  For more information on admission, please visit our Admissions Process and Admissions Criteria web page.



  • Faculty Name Department Email
    Arora, Raman Computer Science/MINDS [email protected]
    Basu, Amitabh Applied Mathematics/MINDS [email protected]
    Braverman, Vladimir Computer Science/MINDS [email protected]
    Budavari, Tamas Applied Mathematics/MINDS [email protected]
    Caffo, Brian Biostatistics/MINDS [email protected]
    Chellappa, Rama Electrical & Computer Eng/MINDS [email protected]
    Eisner, Jason Computer Science/MINDS [email protected]
    Fertig, Elana Oncology/MINDS [email protected]
    Naiman, Daniel Applied Mathematics [email protected]
    Patel, Vishal Electrical & Computer Eng/MINDS [email protected]
    Priebe, Carey Applied Mathematics/MINDS [email protected]
    Shpitser, Ilya Computer Science/MINDS [email protected]
    Venkataraman, Archana Electrical & Computer Eng/MINDS [email protected]
    Vidal, Rene Biomedical Engineering/MINDS [email protected]
    Xu, Yanxun Applied Mathematics/MINDS [email protected]
    Younes, Laurent, Program Director Applied Mathematics/MINDS [email protected]


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