Program Pages Content

Degrees & Pathways


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.

For those that wish to start this program in the fall of 2016 or spring of 2017, and complete it online, please review the Data Science schedule planning document, which maps out some sample course paths for you.

Upon completing the degree program, students will:

  1. Effectively and competitively respond to the growing demand for data scientists.
  2. Balance both the theory and practice of applied mathematics and computer science to analyze and handle large-scale data sets.
  3. Describe and transform information to discover relationships and insights into complex data sets.
  4. Create models using formal techniques and methodologies of abstraction that can be automated to solve real-world problems.


Master's Degree

Admission Requirements

  • You must meet the general admission requirements that pertain to all master's degree candidates.
  • Undergraduate prerequisite coursework must include multivariate calculus; discrete mathematics; courses in Java or C++; and a course in data structures. Python with a programming methodology course will also be accepted for the programming language requirement. 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 these prerequisite courses.
  • A detailed work résumé must be submitted.
  • If you have not taken the prerequisite undergraduate courses, you may satisfy the admission requirements by completing the specified courses (either with Johns Hopkins Engineering or another institution) with a grade of B− or better.
  • When reviewing an application, the candidate's academic and professional background may be considered.
  • If you are an international student, you may have additional admission requirements.

Degree Requirements

  • A total of ten courses must be completed within five years.
  • The curriculum consists of eight required courses and two 700-level electives - one from the Applied and Computational Mathematics (625.7xx) program and one from the Computer Science program (605.7xx).
  • 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 and not applied towards a graduate or other degree may be considered towards the Data Science master's degree subject to advisor approval; one such course may be considered for transfer to the post-master's certificate.
  • No more than one course with a grade of C, and no course with a grade lower than C, may be counted towards the degree.
  • All course selections are subject to advisor approval.

Post Master's Certificate

Admission Requirements

  • You must meet the general admission requirements that pertain to all post master's certificate candidates.
  • Applicants with a master's degree in computer science or applied and computational mathematics, or a closely related discipline, are eligible to apply.

Certificate Requirements

  • A total of six courses must be completed within three years.
  • You must select at least two courses from the Applied and Computational Mathematics area and at least two courses from the Computer Science area.
  • At least one of the courses must be 700-level.
  • No courses with a grade of C or below may be counted towards the certificate.
  • All course selections are subject to advisor approval.


Please refer to the course schedule published each term for exact dates, times, locations, fees, and instructors.


These required foundation courses must be taken or waived before all other courses in their respective programs.


Students who have been waived from foundation or required courses may replace the courses with the same number of other graduate courses. courses must be replaced with courses and courses must be replaced with courses. Students who take outside electives from other programs must meet the specific course and program requirements listed. In the event that the student has transfer courses accepted, they will be considered outside electives.

* 625.416 Optimization in Finance may be substituted for 625.415.


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 ( or as the waived course.

Program News

Top Instructors Receive 2017 Faculty Awards
April 18, 2017

During the 2017 spring faculty meeting held at the Johns Hopkins University Applied Physics Laboratory, Johns Hopkins Engineering honored ten outstanding online and part-time instructors for their dedication in the classroom this past year.

U.S. News & World Report Ranks JHU's Online Engineering Programs Among Nation's Best
January 12, 2017

In the latest rankings from U.S. News & World Report, released January 10, 2017, the Johns Hopkins University Whiting School of Engineering maintained its spot in the top twenty-five schools in the country in the categories of Best Online Graduate Engineering Programs and Best Online Graduate Computer...

Online Master's Degree in Data Science Now Available
September 9, 2016

Johns Hopkins Engineering recently launched a new master's degree program in data science that students can complete online.

The curriculum blends computer science and applied mathematics, and prepares students to analyze relationships in complicated data sets. To earn the master's degree, students will complete ten courses in subjects like data visualization, cloud computing, and statistical models.