Data Science Master’s Program

Data

DATA SCIENCE

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.

 

PROGRAM REQUIREMENTS

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.

 

Last Modified 04/05/2021

    • 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 and blue font are recommended.

     

    • One course in Statistics

     

    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
    • One course in Machine Learning:

     

    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)

     

    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)
    • One course in Optimization:

     

    Offered fall semester
    EN.553.761 Nonlinear Optimization I
    EN.553.665 Introduction to Convexity

     

    Offered spring semester
    EN.553.762 Nonlinear Optimization II
    EN.553.763 Stochastic Search and Optimization
    EN.553.766 Combinatorial Optimization

     

    • One course in Computing:

     

    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
  • 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 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

    Offered spring semester
    EN.520.650 Machine Intelligence
    EN.580.691 Learning, Estimation and Control
  • One Elective course may be chosen from the above approved courses listed in the focus categories.

     

    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.

  • 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.

     

    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.
    • 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 must take an approved online data ethics course, such as the one available at https://www.coursera.org/learn/data-science-ethics.
    • 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)

     

     

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

     

    CLICK HERE TO APPLY.

  • Faculty Name Department
    Arora, Raman Computer Science/MINDS
    Basu, Amitabh Applied Mathematics/MINDS
    Braverman, Vladimir Computer Science/MINDS
    Budavari, Tamas Applied Mathematics/MINDS
    Caffo, Brian Biostatistics/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
    Venkataraman, Archana Electrical & Computer Eng/MINDS
    Vidal, Rene Biomedical Engineering/MINDS
    Xu, Yanxun Applied Mathematics/MINDS
    Younes, Laurent, Program Director Applied Mathematics/MINDS
    • 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 really inaccessible, you should contact Mrs. 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 DS master’s students, and several courses in the CS department are restricted to graduate students in CS and DS. 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.

 

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