Why Johns Hopkins University?
The Johns Hopkins Master of Science in Data Science is offered through the Engineering Department of this research-intensive, internationally renowned large university in Baltimore Maryland. The program is academically rigorous and more theoretically-based than many others, and applicants are expected to have a fairly high level of preparation before they enroll so that classes can focus in-depth on more advanced topics. Applicants with a bachelor’s degree in a technical field such as computer science, statistics, mathematics, engineering, or data-intensive science will be considered. You may also be prepared for the program if you have completed three semesters of calculus, 1 semester of discrete math, and one semester of Java or Python. Standardized tests are not required of applicants, who should have a minimum 3.0 GPA in their undergraduate degree.
Program Snapshot
| University Name | Johns Hopkins University |
| Location | Baltimore |
| State | Maryland |
| Niche.com Grade | A+ |
| Name of Degree | Master of Science in Data Science |
| School or Department Administering Program | Applied Mathematics and Statistics |
| Credits | 30 |
| Cost per credit | $1531 |
| Tuition for Entire Program | $45,930 |
| Test Requirements | No |
| Minimum GPA | 3.00 |
| Prerequisite Courses and Skills | Bachelor’s degree in a technical field; B- or better in 3 semesters of calculus, 1 semester of discrete math (preferred – other courses accepted in lieu), and 1 semester of Java or Python (preferred – C++ plus knowledge of Java or Python also accepted) |
| Campus Visit Required? | No |
| Typical Time to Complete | Two years |
| When Can Students Start the Program? | Fall |
| Program Concentrations |
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| Synchronous classes | No |
| Required Courses |
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| Does the Program Include a Capstone? | Yes |
| Does the program include a practicum or internship? | No |
| Other Features that Make the Program Unique |
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| Program Objectives (quoted directly from the program) |
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| Program Description | This 30-credit program prepares students to meet the growing demand of data scientists through a curriculum that balances theory and practice as students apply mathematical and computational tools to large scale data sets. Students learn to describe and transform information to discover relationships and insights and create models that can be automated to solve organizational problems. Concentrations are offered in Computational Medicine, Computational Machine Learning, Computer Vision, Computational Finance, Mathematics of Data Science, Language and Speech and Statistical Theory. A capstone project is offered. |
What will I Learn?
In comparison with other data science Master’s programs, the JHU MSDS emphasizes a more theoretical and mathematical approach to understanding and applying data science to organizational problems. The program’s objectives are to prepare students who are competitive in the job market by teaching theory and practice of applied mathematics and computer science. Graduates will be able to describe and transform data and create models that can be automated to solve real-world problems.
All JHU Data Science Master’s students must complete a suite of core courses that covers algorithms, data visualization, statistical models and regression, and an introduction to data science. Students can choose between database systems or machine learning, and between an additional computational statistics course or an introduction to optimization. An independent study option is available.
The JHU program offers seven different domain concentrations. Computational Medicine draws on Johns Hopkins’ stellar resources and reputation in health sciences to address data-intensive problems in health care. Theoretically-oriented students can complete concentrations in Statistical Theory or Mathematics of Data Science. The Language and Speech concentration applies technologies of natural language processing and text analysis to linguistic problems. Computer Vision students focus on the problem of using computation and analytics to enable machines to “see” and categorize digital images, in collaboration with on-campus researchers. Finally, the Computational Machine Learning concentration allows students to delve further into the use of statistical models to make sense of massive data sets, learn patterns, and answer questions.
How much will the program cost?
Students usually finish the degree within 2 years and pay $1531 per credit for a total of 30 credits, or just under $46,000 for the entire degree.
How does the program fit into my life?
All courses are asynchronous, meaning that instructors create learning materials such as lectures, activities, quizzes, assignments, and group discussions in weekly modules, and students are responsible for completing the module on their own schedule within the given week. The program can be completed 100% online and no campus visits are required. Due to the theoretical and in-depth nature of some of the coursework, self-motivation is a necessity for students, especially if they pursue an independent study. Students may start the degree in Fall only.
Summary
Johns Hopkins University routinely tops the charts of institutions in terms of federal research funding and research output, and its MSDS program mirrors the university’s focus on rigorous academic inquiry. The program stands out for its range of interesting concentrations and for its collaboration with the research community at JHU. The option of diving deeper into theoretical questions about how data science applications work at a mathematical or statistical level may also be very appealing to some applicants. The program is at the top of the scale in terms of overall cost, so students should be aware that if they seek a more hands-on and workplace-oriented training, they should approach this program with caution.