Master of Science in Data Science

Johns Hopkins University

Issuing School Whiting School of Engineering
Description from the School

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.

In-State Cost $57,010
Out-of-State Cost $57,010
Delivery Model Fully Online
Completion Time 18 - 60 months
Diploma Different? No
Application Deadline 1 Sep 20, 2021
Application Deadline 2 Sep 20, 2021
Alumni Companies
Exam Required No Info
Transfer Available No
Credits Needed to Complete 30
College Funded Aid No
Admission Requirements


Applicants must be in the last semester of undergraduate study or hold a bachelor’s degree from a regionally accredited college or university, or have earned graduate degrees in technical disciplines. Applicants typically have earned a grade point average of at least 3.0 on a 4.0 scale (B or above) in the latter half of their undergraduate studies. Significant relevant work experience or a graduate degree in a relevant technical discipline may be considered in lieu of meeting the GPA guideline. Transcripts from all college studies† Completed and submitted online application form 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 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.

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.


605.101 - Introduction to Python 605.201 - Intro to Programming Using Java 605.202 - Data Structures 605.203 - Discrete Mathematics 625.201 - General Applied Mathematics 625.250 - Multivariable and Complex Analysis 625.251 - Introduction to Ordinary and Partial Differential Equations 625.252 - Linear Algebra

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 are available at Johns Hopkins Engineering. These courses do not count toward the degree or certificate requirements.



625.603 - Statistical Methods and Data Analysis 685.621 - Algorithms for Data Science

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


605.641 - Principles of Database Systems or 605.649 - Introduction to Machine Learning 605.662 - Data Visualization 625.615 - Introduction to Optimization * or 625.664 - Computational Statistics 625.661 - Statistical Models and Regression 685.648 - Data Science

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 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.

*EN.625.616 Optimization in Finance may be substituted.



605.741 - Large-Scale Database Systems 605.746 - Advanced Machine Learning 605.748 - Semantic Natural Language Processing 605.788 - Big Data Processing Using Hadoop


625.714 - Introductory Stochastic Differential Equations with Applications 625.721 - Probability and Stochastic Process I 625.722 - Probability and Stochastic Process II 625.725 - Theory Of Statistics I 625.726 - Theory of Statistics II 625.734 - Queuing Theory with Applications to Computer Science 625.740 - Data Mining 625.741 - Game Theory 625.743 - Stochastic Optimization & Control 625.744 - Modeling, Simulation, and Monte Carlo


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.

605.625 - Probabilistic Graphical Models 605.628 - Applied Topology 605.632 - Graph Analytics 605.633 - Social Media Analytics 605.635 - Cloud Computing 605.645 - Artificial Intelligence 605.647 - Neural Networks 605.649 - Introduction to Machine Learning 605.724 - Applied Game Theory 605.725 - Queuing Theory with Applications to Computer Science 605.726 - Game Theory 625.601 - Real Analysis 625.609 - Matrix Theory 625.611 - Computational Methods 625.618 - Discrete Hybrid Optimization 625.620 - Mathematical Methods for Signal Processing 625.623 - Introduction to Operations Research: Probabilistic Models 625.633 - Monte Carlo Methods 625.636 - Graph Theory 625.638 - Neural Networks 625.641 - Mathematics of Finance 625.642 - Mathematics of Risk, Options, and Financial Derivatives 625.662 - Design and Analysis of Experiments 625.663 - Multivariate Statistics and Stochastic Analysis 625.665 - Bayesian Statistics 625.680 - Cryptography 625.687 - Applied Topology 625.690 - Theory of Computing 625.692 - Probabilistic Graphical Models 625.695 - Time Series Analysis 625.717 - Advanced Differential Equations: Partial Differential Equations 625.718 - Advanced Differential Equations: Nonlinear Differential Equations and Dynamical Systems 625.728 - Theory of Probability


685.795 - Capstone Project in Data Science 685.801 - Independent Study in Data Science I 685.802 - Independent Study in Data Science II

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