The Data Science program balances theory and applications so that you can advance your career long-term.
Program Pages Content
The rigorous curriculum focuses on the fundamentals of computer science, statistics, and applied mathematics, while incorporating real-world examples. With options to study online and on-site in state-of-the-art facilities at the Johns Hopkins Applied Physics Laboratory, students learn from practicing engineers and data scientists. Graduates are prepared to succeed in specialized jobs involving everything from the data pipeline and storage, to statistical analysis and eliciting the story the data tells.
Upon completing the degree program, students will:
- Effectively and competitively respond to the growing demand for data scientists.
- Balance both the theory and practice of applied mathematics and computer science to analyze and handle large-scale data sets.
- Describe and transform information to discover relationships and insights into complex data sets.
- Create models using formal techniques and methodologies of abstraction that can be automated to solve real-world problems.
- You must meet the general admission requirements that pertain to all master's degree candidates.
- your prior education must include the following prerequisites: (1) multivariate calculus; (2) discrete mathematics; (3) courses in Java or C++ (note that actual competency in Java is expected and that Python can be accepted on a case-by-case basis); and (4) a course in data structures. Linear Algebra or Differential Equations will be accepted in lieu of Discrete Mathematics. A grade of B− or better must have been earned in each of the prerequisite courses.
- If your prior education does not include the prerequisites listed above, you may still be admitted under provisional status, followed by full admission once you have completed the missing prerequisites. Missing prerequisites may be completed with Johns Hopkins Engineering (all prerequisites beyond calculus are available) or at another regionally accredited institution.
- You may submit a detailed résumé if you would like your academic and professional background to be considered.
- If you are an international student, you may have additional admission requirements.
- A total of ten courses must be completed within five years.
- The curriculum consists of seven required courses, one Applied and Computational Mathematics (625.xxx) elective, and two 700-level electives.
- Courses applied toward undergraduate or graduate degrees at other institutions (non-JHU) are not eligible for transfer or double counting to a Data Science master's degree or post-master's certificate. Up to two graduate courses taken outside of JHU after an undergraduate degree was conferred and not applied toward a graduate or other degree may be considered toward the Data Science master's degree subject to advisor approval.
- Graduate students who are not pursuing a master's degree in Data Science should consult with their advisor to determine which courses must be successfully completed before 600- or 700-level Data Science courses may be taken.
- Only one C-range grade (C+, C, or C−) can count toward the master’s degree.
- All course selections are subject to advisor approval.
Post Master's Certificate
- You must meet the general admission requirements that pertain to all post master's certificate candidates. Coursework should have included coursework comparable to at least three of the four required courses in both the Computer Science area and the Applied and Computational Mathematics area, respectively.
- Applicants with a master's degree in data science or a very closely related field, such as applied statistics, are eligible to apply.
- A total of six courses must be completed within three years.
- You must select at least three courses from the Applied and Computational Mathematics program (625.xxx) and at least three courses from the Computer Science program (605.xxx).
- At least four of the courses must be 700-level with at least two from Computer Science and at least two from Applied and Computational Mathematics.
- 625.603 Statistical Methods and Data Analysis may not be applied to the post-master's certificate.
- One graduate course taken outside of JHU and not applied toward a graduate or other degree may be considered toward the Data Science Post-Master’s Certificate, subject to advisor approval.
- Only grades of B− or above can be counted toward the post-master’s certificate.
- All course selections are subject to advisor approval.
Please refer to the Schedule Planning Information page for a general idea when these courses are offered. For exact dates, times, locations, fees, and instructors, please refer to the course schedule published each term.
Applicants whose prior education does not include the prerequisites listed under Admission Requirements may still be admitted under provisional status, followed by full admission once they have completed the missing prerequisites. All prerequisite courses beyond calculus are available at Johns Hopkins Engineering. These courses do not count toward the degree or certificate requirements.
These required foundation courses must be taken or waived before all other courses in their respective programs.
AND ONE OF THE FOLLOWING:
And ONE 625.xxx course selected from Electives or Additional Selections.
Students who have been waived from foundation or required courses may replace the courses with the same number of other graduate courses. 605.xxx courses must be replaced with 605.xxx courses and 625.xxx courses must be replaced with 625.xxx courses. Students who waive 605.641 must replace it with 605.741 Large-Scale Database Systems. Students who waive 685.621 must replace it with 605.641 Principles of Database Systems OR 605.649 Introduction to Machine Learning. Students who take outside electives from other programs must meet the specific course prerequisites listed.
Students waiving required courses may choose from the list of 700-level electives or from the courses below. The replacement course should be from the same field (605.xxx or 625.xxx) as the waived course.
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