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.
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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
- GRE scores (MSE applicants)
- TOEFL/IELTS (for international students, see more information below)
- 3 letters of recommendation
- GRE General Test scores are required from Master’s degree applicants. Arrange for scores on the GRE General Test to be sent to the department by the Educational Testing Service. The GRE code for applying to Full-Time Engineering graduate programs at The Johns Hopkins University is 4655. Applicants are encouraged to take the GRE as early as possible during the academic year preceding entrance. Please note that the GRE Advanced Test in Mathematics is recommended, but is not required. We do not accept the GMAT in lieu of GRE scores.
- 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.
Below are the program requirements:
- Orientation sessions starting 2 weeks before the first day of classes.
- EN.553.636 Introduction to Data Science (1 course). Must be taken the first semester.
- 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. Course in italics are recommended (4 courses).
- Four elective courses. Courses may be chosen from the Electives section or the Core Areas section, provided courses are not double-counted (4 courses).
- Data Science Capstone Experience (1 course), final paper and poster presentation.
- WSE Required mini-courses (Academic Ethics, Responsible Conduct of Research).
- Data Science Ethics course.
*While students matriculating in Fall 2023 or later are not required to register for Department Seminar (EN.553.801), we do strongly encourage our Data Science students to take at least 1 semester.
| Code | Title | Credits |
|---|---|---|
| Core Requirements | ||
| EN.553.636 | Introduction to Data Science | 4.0 |
| 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.553.735 | Topics in Statistical Pattern Recognition | 3 |
| EN.553.738 | High-Dimensional Approximation, Probability, and Statistical Learning | 3 |
| EN.553.739 | Statistical Pattern Recognition Theory & Methods | 3 |
| EN.570.654 | Geostatistics: Understanding Spatial Data | 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.520.647 | Information Theory | 3 |
| EN.520.648 | Compressed Sensing and Sparse Recovery | 3 |
| EN.520.651 | Random Signal Analysis | 4 |
| EN.520.666 | Information Extraction | 3 |
| EN.525.724 | Introduction to Pattern Recognition | 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.553.738 | 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.570.654 | Geostatistics: Understanding Spatial Data | 3 |
| EN.601.634 | Randomized and Big Data Algorithms | 3 |
| EN.601.674 | ML: Learning Theory | 3 |
| EN.601.675 | Machine Learning | 3 |
| EN.601.676 | Machine Learning: Data to Models | 3 |
| EN.601.677 | Causal Inference | 3 |
| EN.601.682 | Machine Learning: Deep Learning | 4 |
| EN.601.779 | Machine Learning: Advanced Topics | 3 |
| EN.601.780 | Unsupervised Learning: From Big Data to Low-Dimensional Representations | 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 in 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 Additional Courses | ||
| Courses listed in the core areas may be taken to complete this requirement, provided they are not double-counted. The following provide additional options, grouped into categories (but the chosen courses may be taken from different categories). | 12-16 | |
| ==Computational Medicine== | ||
| 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.520.659 | Machine learning for medical applications | 3 |
| EN.553.650 | Computational Molecular Medicine | 4 |
| EN.580.688 | Foundations of Computational Biology and Bioinformatics | 3 |
| EN.601.651 | Introduction to Computational Immunogenomics | 3 |
| EN.605.620 | Algorithms for Bioinformatics | 3 |
| or EN.605.621 | Foundations of Algorithms | |
| EN.605.653 | Computational Genomics | 3 |
| ==Computer Vision== | ||
| 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.520.646 | Wavelets & Filter Banks | 3 |
| EN.520.648 | Compressed Sensing and Sparse Recovery | 3 |
| EN.525.733 | Deep Learning for Computer Vision | 3 |
| EN.553.693 | Mathematical Image Analysis | 4 |
| EN.601.661 | Computer Vision | 3 |
| EN.601.783 | Vision as Bayesian Inference | 3 |
| EN.605.626 | Image Processing | 3 |
| ==Mathematical Finance== | ||
| EN.553.627 | Stochastic Processes and Applications to Finance I | 4 |
| EN.553.628 | Stochastic Processes and Applications to Finance II | 4 |
| 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.553.648 | 4 | |
| EN.553.649 | Advanced Equity Derivatives | 4 |
| EN.553.743 | Equivariant Machine Learning | 3 |
| EN.553.753 | Three Key Aspects of Climate Change: Energy Transition, Critical Metals and Sustainable Agriculture | 3 |
| ==Mathematics of Data Science== | ||
| EN.553.633 | Monte Carlo Methods | 4 |
| 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.553.792 | Matrix Analysis and Linear Algebra | 4 |
| EN.601.634 | Randomized and Big Data Algorithms | 3 |
| ==Language and Speech== | ||
| EN.520.666 | Information Extraction | 3 |
| EN.520.680 | Speech and Auditory Processing by Humans and Machines | 3 |
| EN.601.665 | Natural Language Processing | 4 |
| EN.601.668 | Machine Translation | 3 |
| EN.601.671 | Natural Language Processing: Self-Supervised Models | 3 |
| EN.601.769 | Events Semantics in Theory and Practice | 3 |
| ==Additional Courses== | ||
| EN.520.640 | Machine Intelligence on Embedded Systems | 3 |
| EN.520.650 | Machine Intelligence | 3 |
| EN.520.665 | Machine Perception | 3 |
| EN.553.653 | Mathematical Game Theory | 4 |
| EN.580.691 | Learning, Estimation and Control | 3 |
| EN.601.615 | Databases | 3 |
| EN.601.642 | Modern Cryptography | 3 |
| EN.601.663 | Algorithms for Sensor-Based Robotics (Recommended pre-requisite EN.601.226) | 3 |
| EN.601.664 | Artificial Intelligence | 3 |
| EN.601.666 | Information Retrieval and Web Agents | 3 |
| EN.650.683 | Cybersecurity Risk Management | 3 |
| Capstone Experience | ||
| EN.553.806 | Capstone Experience in Data Science | 6 |
Capstone Experience
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 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 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)
- Course on Responsible Conduct of Research
- University Orientation and Academic Ethics
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 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.