Department website: https://engineering.jhu.edu/ams/academics/graduate-studies/
The Data Science Master’s program at the Johns Hopkins University is a fully residential program which 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 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.
Students matriculating Fall 2025 or later will be able to choose from a Capstone Option and Course-Only Option. For more details, see Requirements, or view our website at https://engineering.jhu.edu/ams/academics/graduate-studies/ms-in-data-science/program-requirements-overview/.
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STEM Approved Classification Code: 27.05.01
Fall Deadline
The deadline for a fall application is December 15 at 11:59PM Eastern Standard Time (UTC-5) for receipt of ALL application materials.
Spring Deadline
The deadline for a spring application is September 15 at 11:59PM Eastern Daylight Time (UTC-4). for receipt of ALL application materials.
Admissions Process, Materials, and Criteria
Admissions Process and Materials
To ensure timely consideration of your application, please complete the items in the following checklist at your earliest convenience.
- Complete the online application, including
- statement of purpose
- supplementary information section
- non-refundable $75 application fee
- unofficial transcripts
- TOEFL/IELTS (for international students, see more information below)
- 3 letters of recommendation
- If English is not your native language, arrange for TOEFL or IELTS Examination scores to be sent to the department by the testing organization. The TOEFL code for applying to Full-Time Engineering graduate programs at The Johns Hopkins University is C559. The IELTS course ID code for applying to programs at the Johns Hopkins University is 4610 or 110079. Applicants are encouraged to take the TOEFL/IELTS as early as possible during the academic year preceding entrance. If you think you may be eligible for a TOEFL/IELTS waiver, please review the information here.
- Under University guidelines, exceptions to the TOEFL-score criteria appearing in the admissions criteria will be granted only in extraordinary cases.
- Arrange for three letters of recommendation from persons familiar with your abilities and achievements, especially relevant to graduate study in applied mathematics, to be submitted electronically through the online application.
- Arrange for unofficial transcripts of all undergraduate and previous graduate study to be uploaded into your online application. Applicants who have attended non-US institutions are also strongly encouraged to submit a professional credential evaluation with the unofficial transcripts. If admitted, you will be required to submit your official final documents directly to Graduate Admissions.
Limited partial financial support is available to some Master’s students, and all applicants are automatically considered for any available departmental support.
Detailed information about application requirements can be found on the WSE Graduate Admissions web site.
If you have further questions, please contact the Graduate Admissions Office at WSEGrad-Admissions@jhu.edu.
Admissions Criteria
Prospective students for our graduate programs must have completed a Bachelor’s level degree, ideally in Engineering, Mathematics, Computer Science or in the Sciences. In addition, candidates should ideally have completed undergraduate-level courses in
- Calculus, through multivariable calculus
- Linear algebra
- Differential equations
- Probability, preferably complemented with a course in Statistics
- Computer programming (e.g., in C++)
- At least one proof-writing courses
Admissions decisions are based on four major factors: Mathematical course background, grades (GPA), Graduate Record Examination (GRE) scores, and recommendation letters.
If the applicant is not a native English speaker, a minimum TOEFL score of 100 (IBT)/ 250 (CBT)/ 600 (PBT), or a minimum IELTS band score of 7 is required.
Program Requirements
The Data Science Master's program is designed to be completed in three semesters of full-time residential (on campus) graduate study. Please see our program website for the most current program requirements, approved courses, and additional information.
Program requirements for students who matriculated PRIOR to Fall 2025.
- Orientation sessions starting 2 weeks before the first day of classes.
- EN.553.636 Introduction to Data Science.
- One course in each of the four Core Areas. Courses chosen in this section must be distinct from the courses used to satisfy the 5 additional course requirements.
- Four elective courses. Courses may be chosen from the Electives section or the Core Areas section, provided courses are not double-counted.
- Data Science Capstone Experience (6 credit course), poster presentation and final paper.
- Complete training on the responsible and ethical conduct of research. Please see WSE Policy on the Responsible Conduct of Research.
- Receive a Passing grade in the mandatory Graduate Academic Ethics course, EN.500.603 (01).
- Data Science Ethics course.
- The communication skills requirement (Communication Skills Practicum)
Beginning Fall 2025, students will have the choice to complete a Capstone Option or a Course Only Option.
Program requirements for students who matriculated FALL 2025 or later: Capstone Option
- Orientation sessions starting 2 weeks before the first day of classes.
- EN.553.636 Introduction to Data Science -OR- EN.601.675 Machine Learning. Must be taken the first semester.
- One course in each of the four (4) Core Areas. Courses chosen in this section must be distinct from the courses used to satisfy the electives requirements.
- Three (3) elective courses. Courses may be chosen from the Electives section or the Core Areas section, provided courses are not double-counted.
- Data Science Capstone Experience*. Six (6) credit course which may be taken in two semesters of 3 credits each.
- Complete training on the responsible and ethical conduct of research. Please see WSE Policy on the Responsible Conduct of Research.
- Receive a Passing grade in the mandatory Graduate Academic Ethics course, EN.500.603 (01).
- Data Science Ethics course.
- The communication skills requirement (Communication Skills Practicum)
Program Requirements for students who matriculated FALL 2025 or later: Course Only Option
- Orientation sessions starting 2 weeks before the first day of classes.
- EN.553.636* Introduction to Data Science OR EN.601.675 Machine Learning. Must be taken the first semester.
- One course in each of the four (4) Core Areas. Courses chosen in this section must be distinct from the courses used to satisfy the 5 electives requirements.
- Five (5) elective courses. Courses may be chosen from the Electives section or the Core Areas section, provided courses are not double-counted.
- Complete training on the responsible and ethical conduct of research. Please see WSE Policy on the Responsible Conduct of Research.
- Receive a Passing grade in the mandatory Graduate Academic Ethics course, EN.500.603 (01).
- Data Science Ethics course.
- The communication skills requirement (Communication Skills Practicum)
| Code | Title | Credits |
|---|---|---|
| Core Requirements | ||
| EN.553.636 | Introduction to Data Science | 4.0 |
| or EN.601.675 | Machine Learning | |
| Core Areas | ||
| Select one course in each of the four Core Areas: | 12 - 16 | |
| ==Statistics== | ||
| EN.553.613 | Applied Statistics & Data Analysis I | 4 |
| EN.553.614 | Applied Statistics and Data Analysis II | 3 |
| EN.553.630 | Mathematical Statistics (NOTE: EN.553.630 may not be taken after EN.553.730.) | 4 |
| EN.553.632 | Bayesian Statistics | 3 |
| EN.553.639 | Time Series Analysis | 3 |
| EN.553.730 | Statistical Theory I | 4 |
| EN.553.731 | Statistical Theory II | 3 |
| EN.553.733 | Nonparametric Bayesian Statistics | 3 |
| EN.553.738 | High-Dimensional Approximation, Probability, and Statistical Learning | 3 |
| EN.553.739 | Statistical Pattern Recognition Theory & Methods | 3 |
| EN.601.677 | Causal Inference | 3 |
| EN.625.603 | Statistical Methods and Data Analysis | 3 |
| EN.625.664 | Computational Statistics | 3 |
| ==Machine Learning== | ||
| EN.520.612 | Machine Learning for Signal Processing | 3 |
| EN.520.637 | Foundations of Reinforcement Learning | 3 |
| EN.520.638 | Deep Learning | 3 |
| EN.520.648 | Compressed Sensing and Sparse Recovery | 3 |
| EN.520.651 | Random Signal Analysis | 4 |
| EN.520.665 | Machine Perception | 3 |
| EN.520.666 | Information Extraction | 3 |
| EN.525.724 | Introduction to Pattern Recognition (Online Course) | 3 |
| EN.530.641 | Statistical Learning For Engineers | 3 |
| EN.535.741 | Optimal Control and Reinforcement Learning | 3 |
| EN.553.602 | Research and Design in Applied Mathematics: Data Mining | 4 |
| EN.553.724 | Probabilistic Machine Learning | 3 |
| EN.553.738 | High-Dimensional Approximation, Probability, and Statistical Learning | 3 |
| EN.553.740 | Machine Learning I | 3 |
| EN.553.741 | Machine Learning II | 3 |
| EN.553.743 | Equivariant Machine Learning | 3 |
| EN.601.634 | Randomized and Big Data Algorithms | 3 |
| EN.601.674 | ML: Learning Theory | 3 |
| EN.601.675 | Machine Learning | 3 |
| EN.601.677 | Causal Inference | 3 |
| EN.601.682 | Machine Learning: Deep Learning | 4 |
| EN.601.779 | Machine Learning: Advanced Topics | 3 |
| EN.625.692 | Probabilistic Graphical Models | 3 |
| ==Optimization== | ||
| EN.520.618 | Modern Convex Optimization | 3 |
| EN.553.653 | Mathematical Game Theory | 4 |
| EN.553.661 | Optimization in Finance | 4 |
| EN.553.662 | Optimization for Data Science | 3 |
| EN.553.665 | Introduction to Convexity | 4 |
| EN.553.669 | Large-Scale Optimization For Data Science | 3 |
| EN.553.761 | Nonlinear Optimization I | 3 |
| EN.553.762 | Nonlinear Optimization II | 3 |
| EN.553.763 | Stochastic Search and Optimization | 3 |
| EN.553.766 | Combinatorial Optimization | 3 |
| EN.553.797 | Introduction to Control Theory and Optimal Control | 3 |
| EN.625.615 | Introduction to Optimization | 3 |
| ==Computing== | ||
| EN.520.617 | Computation for Engineers | 3 |
| EN.553.688 | Computing for Applied Mathematics | 3 |
| EN.601.619 | Cloud Computing | 3 |
| EN.601.620 | Parallel Computing for Data Science | 3 |
| EN.601.633 | Intro Algorithms | 3 |
| EN.601.646 | Sketching and Indexing for Sequences | 3 |
| EN.601.647 | Computational Genomics: Sequences | 3 |
| EN.625.664 | Computational Statistics | 3 |
| EN.685.621 | Algorithms for Data Science | 3 |
| 4 or 5 Additional Courses (depending on whether student chooses Course Only Option or Capstone Option | ||
| The following additional courses may be taken to fulfill the elective requirement. Courses listed in the core areas may be taken the elective requirement, however, they may not be double-counted. See our website for concentrations as well as any updates to the course listing. | ||
| AS.410.633 | Introduction to Bioinformatics | 4 |
| AS.410.635 | Bioinformatics: Tools for Genome Analysis | 4 |
| AS.410.671 | Gene Expression Data Analysis and Visualization | 4 |
| EN.520.614 | Image Processing & Analysis | 3 |
| EN.520.615 | Image Processing & Analysis II | 3 |
| EN.520.623 | Medical Image Analysis | 3 |
| EN.520.635 | Digital Signal Processing | 3 |
| EN.520.640 | Machine Intelligence on Embedded Systems | 3 |
| EN.520.646 | Wavelets & Filter Banks | 3 |
| EN.520.650 | Machine Intelligence | 3 |
| EN.520.659 | Machine learning for medical applications | 3 |
| EN.520.661 | AI and Biometric Systems: Techniques, Applications, and Ethics | 3 |
| EN.520.665 | Machine Perception | 3 |
| EN.520.680 | Speech and Auditory Processing by Humans and Machines | 3 |
| EN.525.733 | Deep Learning for Computer Vision (Online Course) | 3 |
| EN.553.627 | Stochastic Processes and Applications to Finance I | 4 |
| EN.553.628 | Stochastic Processes and Applications to Finance II | 4 |
| EN.553.633 | Monte Carlo Methods | 4 |
| EN.553.635 | Bayesian Statistics for the Physical Sciences | 3 |
| EN.553.641 | Equity Markets and Quantitative Trading | 3 |
| EN.553.642 | Investment Science | 4 |
| EN.553.644 | Introduction to Financial Derivatives | 4 |
| EN.553.645 | Interest Rate and Credit Derivatives | 4 |
| EN.553.646 | Risk Measurement/Management in Financial Markets | 4 |
| EN.553.647 | Quantitative Portfolio Theory and Performance Analysis | 4 |
| EN.553.649 | Advanced Equity Derivatives | 4 |
| EN.553.650 | Computational Molecular Medicine | 4 |
| EN.553.689 | Software Engineering for Data Science | 3 |
| EN.553.693 | Mathematical Image Analysis | 4 |
| EN.553.738 | High-Dimensional Approximation, Probability, and Statistical Learning | 3 |
| EN.553.744 | Data Science Methods for Large Scale Graphs | 3 |
| EN.553.753 | Commodity Markets: Electricity and Natural Gas, Oil, Metals, and Agriculturals | 3 |
| EN.553.792 | Matrix Analysis and Linear Algebra | 4 |
| EN.580.627 | Deep Learning for Medical Imaging | 3 |
| EN.580.688 | Foundations of Computational Biology and Bioinformatics | 3 |
| EN.580.691 | Learning, Estimation and Control | 3 |
| EN.580.709 | 3 | |
| EN.601.615 | Databases | 3 |
| EN.601.635 | Approximation Algorithms | 3 |
| EN.601.637 | Federated Learning and Analytics | 3 |
| EN.601.641 | Blockchains and Cryptocurrencies | 3 |
| EN.601.642 | Modern Cryptography | 3 |
| EN.601.651 | Introduction to Computational Immunogenomics | 3 |
| EN.601.661 | Computer Vision | 3 |
| EN.601.663 | Algorithms for Sensor-Based Robotics (Recommended pre-requisite EN.601.226) | 3 |
| EN.601.664 | Artificial Intelligence | 3 |
| EN.601.665 | Natural Language Processing | 4 |
| EN.601.666 | Information Retrieval and Web Agents | 3 |
| EN.601.668 | Machine Translation | 3 |
| EN.601.670 | Artificial Agents | 3 |
| EN.601.671 | Natural Language Processing: Self-Supervised Models | 3 |
| EN.601.690 | Introduction to Human-Computer Interaction | 3 |
| EN.601.773 | Machine Social Intelligence | 3 |
| EN.601.783 | Vision as Bayesian Inference | 3 |
| EN.601.788 | Machine Learning for Healthcare | 3 |
| EN.605.620 | Algorithms for Bioinformatics | 3 |
| or EN.605.621 | Foundations of Algorithms | |
| EN.605.621 | Foundations of Algorithms | 3 |
| EN.605.626 | Image Processing | 3 |
| EN.605.653 | Computational Genomics | 3 |
| EN.650.683 | Cybersecurity Risk Management | 3 |
| Capstone Experience | ||
| EN.553.806 | Capstone Experience in Data Science (BS/MSE students in undergraduate status must register for EN.553.506) | 6 |
Capstone Experience
The Capstone Experience in Data Science (EN.553.806 or EN.553.506 for undergraduates) 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 Data Science Capstone Experience Proposal form and follow instructions below to submit for approval before enrollment in EN.553.806 will be approved by academic staff.
All students enrolled in EN.553.806 (or EN.553.506) are REQUIRED to present their research findings in poster format at the event held in their final semester. See website for a list of upcoming dates. Students must also submit a final report to their capstone supervisor. The grade for this course is based, in large part, upon the poster event and your final report. For more information on the poster and the report, please see our website.
Additional Required Courses
In addition to the above course requirements, all data science master's students will complete:
- An online Data Ethics course: Students must take an approved online data ethics course such as the one offered by Coursera
- The communication skills requirement (Communication Skills Practicum)
- Online course on Responsible Conduct of Research (AS.360.624)
- University Orientation and Academic Ethics (EN.500.603) - students are automatically enrolled in their first semester
Additional Notes:
- A course grade of B- or better is required to meet all course requirements. One grade of C/C+, is permitted to count towards program requirements. Consult the Department/Program website for additional information regarding Minimum Grade Requirements and the Academic Probation Policy.
- Courses cannot be double-counted for different requirements (even if they appear in several core and/or elective areas).
- All students are required to submit a program plan for review. If any deviations are made from this plan, students are required to submit an updated plan for review.