Program Type:
Mode of Study:
On Campus
Program Actions


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.


The general requirements are as follows. 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 then take four additional courses from an approved course list.

The final Capstone Experience in Data Science (EN.553.806) is a research-oriented project which must be approved by the research supervisor, academic advisor and the Internal Oversight Committee.  The Capstone Experience can be taken in multiple semesters, but the total number of credits required for successful completion is six (6). 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.

Requests to add courses to the approved list should be directed to the student’s advisor who will petition the Data Science Master’s Oversight Committee to add the course to the approved list.  The student should not register for the course until approval has been given by the Oversight Committee.

Core Requirements – 5 courses

  • 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.  Courses in italics are recommended.


Offered fall and spring semesters
EN.553.630 Introduction to Statistics.  NOTE:  EN.553.630 may not be taken after EN.553.730.
Offered fall semester
EN.553.632 Bayesian Statistics
EN.553.730 Statistical Theory I
EN.601.677 Causal Inference
Offered spring semester
EN.553.731 Statistical Theory II
EN.553.738 High-Dimensional Approximation, Probability, and Statistical Learning
EN.553.733 Advanced Topic in Bayesian Analysis
EN.553.739 Statistical Pattern Recognition Theory & Methods
EN.570.654 Geostatistics:  Understanding Spatial Data
EN.553.639 Time Series Analysis


Offered fall and spring semesters
EN.601.675 Machine Learning
Offered fall semester
EN.520.612 Machine Learning for Signal Processing
EN.520.637 Foundations of Reinforcement Learning
EN.520.647 Information Theory
EN.520.651 Random Signal Analysis
EN.525.724 Introduction to Pattern Recognition (online)
EN.553.740 Machine Learning I
EN.580.709 Sparse Representations in Computer Vision and Machine Learning
EN.601.634 Randomized and Big Data Algorithms
EN.601.677 Causal Inference
EN.601.682 Machine Learning: Deep Learning
EN.601.780 Unsupervised Learning:  Big Data to Low-Dimensional Representations (alternate with EN.580.745)
EN.601.674 Machine Learning: Learning Theory
Offered spring semester
EN.520.638 Deep Learning
EN.520.648 Compressed Sensing and Sparse Recovery
EN.520.666 Information Extraction
EN.535.741 Optimal Control and Reinforcement Learning (online)
EN.553.602 Research and Design in Applied Mathematics: Data Mining
EN.553.738 High-Dimensional Approximation, Probability, and Statistical Learning
EN.553.741 Machine Learning II
EN.601.676 Machine Learning: Data to Models
EN.625.692 Probabilistic Graphical Models (online)


Offered fall semester
EN.553.761 Nonlinear Optimization I
EN.553.665 Introduction to Convexity
EN.520.618 Modern Convex Optimization
Offered spring semester
EN.553.762 Nonlinear Optimization II
EN.553.763 Stochastic Search and Optimization
EN.601.681 Machine Learning: Optimization
EN.553.766 Combinatorial Optimization


Offered fall and spring semesters
EN.601.633 Introduction to Algorithms
Offered fall semester
EN.553.688 Computing for Applied Mathematics
EN.601.620 Parallel Programming
EN.601.647 Computational Genomics:  Sequences
Offered spring semester
EN.601.646 Sketching and Indexing for Sequences
EN.520.617 Computation for Engineers

Additional Course Requirements – 4 courses

Four additional courses.  Courses listed in the core areas may be taken to complete this requirement, provided that they are not double counted.  The following provide additional options, grouped into categories (but the chosen courses may be taken from different categories).

Semester offered information is based on past offering of the courses and may be subject to changes in department programs in future years.

Offered fall and spring semesters
AS.410.633 Introduction to Bioinformatics (online)
AS.410.635 Bioinformatics:  Tools for Genome Analysis (online)
EN.605.620 Algorithms for Bioinformatics (cannot be taken with EN.605.621)
EN.605.621 Foundations of Algorithms (cannot be taken with EN.605.620)
Offered fall semester
AS.410.671 Gene Expression Data Analysis and Visualization (online)
EN.605.653 Computational Genomics
Offered spring semester
EN.553.650 Computational Molecular Medicine (offered spring)
EN.520.659 Machine Learning for Medical Applications

Offered fall and spring semesters
EN.601.661 Computer Vision
EN.520.614 Image Processing and Analysis
Offered fall semester
EN.520.646 Wavelets & Filter Banks
EN.520.665 Machine Perception
Offered spring semester
EN.601.783 Vision as Bayesian Inference
EN.520.623 Medical Image Analysis
EN.553.693 Mathematical Image Analysis
EN.520.615 Image Processing and Analysis II
EN.525.733 Deep Learning for Computer Vision (online)

Offered fall semester
EN.553.627 Stochastic Processes and Applications to Finance I
EN.553.641 Equity Markets and Quantitative Trading
EN.553.642 Investment Science
EN.553.644 Introduction to Financial Derivatives
EN.553.646 Risk Measurement and Management in Financial Markets
EN.553.649 Advanced Equity Derivatives
Offered spring semester
EN.553.628 Stochastic Processes and Applications to Finance II
EN.553.645 Interest Rate and Credit Derivatives
EN.553.753 Commodity Markets and Trade Finance

Offered fall semester
EN.553.633 Monte Carlo Methods
EN.553.792 Matrix Analysis and Linear Algebra
EN.601.634 Randomized and Big Data Algorithms

Offered fall semester
EN.601.665 Natural Language Processing
Offered spring semester
EN.520.666 Information Extraction
EN.520.680 Speech and Auditory Processing by Humans and Machines
EN.601.769 Events Semantics in Theory and Practice

EN.520.650 Machine Intelligence
EN.580.691 Learning, Estimation and Control
EN.601.615 Databases
EN.601.663 Algorithms for Sensor-Based Robotics

Capstone Experience – 1 course/final project

The Capstone Experience in Data Science (EN.553.806) is a research-oriented project which must be approved by the research supervisor, academic advisor and the Internal Oversight Committee.  The Capstone Experience can be taken in multiple semesters, but the total number of credits required for successful completion is six (6).  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.

Group projects are acceptable to fulfill the capstone experience, with the following requirements:

  • The group submits a proposal that, in addition to the aforementioned requirements, describes the composition of the group, and the role expected by each group member in the completion of the project.
  • Groups should not include more than four
  • Each student in the group submits an individual report, summarizing the full project, but focusing primarily on the part that was under their responsibility.
  • The final poster may be represented individually or as a group.
  • The option to complete a group project is solely at the discretion of the research supervisor.

Other Requirements & Information

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(s) is (are) repeated, or replacement courses(*) chosen with the agreement of the faculty advisor are taken, with successful completion in both cases, the student will be subject to dismissal. If the student retakes or replaces the course(s) with a grade of B- or above, the probationary status will be lifted.

(*) Students should be aware that, if a course different from the initial one is chosen as a replacement, the first grade will remain on the academic transcript and will be included in the GPA calculation.

  • 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 should download and complete a Data Science Program Plan Form to be reviewed by their advisor each semester.  This form will be requested by the academic staff prior to processing graduation forms or changes of status.
  • Students must take an approved online data ethics course, such as the one available at
  • Students are required to take EN.553.801.02 for at least 1 semester.  Students are encouraged to register in multiple semesters.
  • 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)

Students should download and complete a Data Science Program Plan Form to be reviewed by their advisor each semester.  This form will be requested by the academic staff prior to processing graduation forms or changes of status.

Prospective students for the Data Science Master’s program must have completed a Bachelor’s level degree, ideally in Engineering, Mathematics, Computer Science or in the Sciences. In addition, candidates should have completed undergraduate-level courses in Calculus (through multivariable calculus), Linear algebra, Differential equations, Probability, Computer programming (e.g., in C++ or Python) at least,  preferably complemented with a course in Statistics and at least one proof-writing course.

For more information on admission and deadlines, please visit our Admissions Process and Admissions Criteria web page.


Faculty Name Department
Arora, Raman Computer Science/MINDS
Basu, Amitabh Applied Mathematics/MINDS
Braverman, Vladimir Computer Science/MINDS
Budavari, Tamas Applied Mathematics/MINDS
Randal Burns Computer Science
Caffo, Brian Biostatistics/MINDS
Charles Adam Biomedical Engineering/MINDS
Chellappa, Rama Electrical & Computer Eng/MINDS
Eisner, Jason Computer Science/MINDS
Fertig, Elana Oncology/MINDS
Naiman, Daniel Applied Mathematics
Patel, Vishal Electrical & Computer Eng/MINDS
Priebe, Carey Applied Mathematics/MINDS
Shpitser, Ilya Computer Science/MINDS
Sulam, Jeremias Biomedical Engineering/MINDS
Venkataraman, Archana Electrical & Computer Eng/MINDS
Vidal, Rene Biomedical Engineering/MINDS
Vogelstein, Joshua Biomedical Engineering/MINDS
Xu, Yanxun Applied Mathematics/MINDS
Younes, Laurent, Program Director Applied Mathematics/MINDS

List of potential supervisors for the data science capstone experience

The following list includes JHU faculty members who are willing to be contacted by DS students to supervise their capstone project. This list is not exhaustive and students should feel free to contact other JHU faculty with whom they would be interested to work.

  • Should I wait for my advisor to contact me or should I contact him or her?
    It is up to the student to reach out to their advisors and ask for advice for course selection and other matters. Advisors are, in general, responsive to emails, but it is okay to nudge them if no answer was received after a few days. If the advisor is inaccessible, you should contact Ms. Lisa Wetzelberger for assistance.
  • Are all courses listed in the program available every semester?
    Not necessarily. Students should carefully review the recent history of the classes they are interested in by running a class search on SIS. Students must also be aware that, while classes having been offered in past semesters is often a good indication of their future availability, this is not a guarantee, since changes may occur in departmental programs. It is therefore important to allow some flexibility in course planning.
  • Am I guaranteed to be able to register to any course I am interested in?
    The AMS department is saving some seats in data science class for Data Science Master’s students, and several courses in the Computer Science Department are restricted to graduate students in Computer Science and Data Science. However, for all courses, the number of enrolled students is limited by logistic considerations, such as classroom size and number of available TAs. Data science courses are in high demand, and students may be placed on a wait list (without guarantee to be ultimately able to enroll), especially if they register several days after the opening date.
  • What are my recourses if I am placed on a wait list?
    They are unfortunately limited, but it is always possible to petition the course instructor for admission, especially if this course is the only path to graduation. Adding a student from the wait list to a course is the sole decision of the instructor. In particular, the student’s academic advisor, or the master’s academic staff will not be able to provide much help in this matter. Again, it is important to make sure that the course plan over the typical three semesters of the program includes alternate options in case such a situation occurs.
  • When should I start contacting faculty for the capstone experience?
    The best time should be during the second semester in the program, with a goal to start working on the capstone at or before the beginning of the third semester. It is up to students to reach out to faculty for this purpose. Note that students should not register to 553.806 until their capstone proposal has been accepted.

Take the Next Steps

Learn more about graduate studies at Hopkins Engineering. Request information about: