Data Science, Master of Science

Admission Requirements

Applicants (degree seeking and special student) must meet the general requirements for admission to graduate study. The applicant’s prior education must include the following courses.

1. Three semesters or five quarters of calculus, which includes multivariate calculus (EN.625.250 Multivariable Calculus and Complex Analysis or equivalent will be accepted)
2. One semester/term of advanced math (Linear Algebra is strongly preferred but Discrete Mathematics or Differential Equations will be accepted)
3. Two semesters/terms of Python (which can include non-credit coursework such as Coursera or edX, etc.) and EN.605.256 Modern Software Concepts in Python or equivalent

Applicants whose prior education does not include the courses listed above may still enroll under provisional status, followed by full admission status once they have completed the missing courses. Missing courses may be completed with Johns Hopkins Engineering (all prerequisites are available) or at another regionally accredited institution. Admitted students 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. All official transcripts from all college studies must be submitted.  When reviewing an application, the candidate’s academic and professional background will be considered.

Program Requirements

Ten courses (30 credits) must be completed within five years. Students are required to choose a focus area. The curriculum consists of two core courses (6 credits), five required courses (15 credits), and three courses (9 credits) from the selected focus area of which at least two must be 700-level. Focus areas do not appear as official designations on a student’s transcript or diploma.

Students with core or required course waivers may take up to two electives selected from other EP programs. Course selections outside of the lists of courses below are subject to advisor approval. Transfer courses will be considered electives. 

Non-degree students 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.

Provisional Courses

Course List

Code	Title	Credits
Undergraduate-level courses offered to complete provisional requirements. 1		Credits
EN.625.108 & EN.625.109	Calculus I and Calculus II	8
or EN.605.156	Calculus for Engineers
EN.605.206	Introduction to Programming Using Python	3
or EN.605.201	Introduction to Programming Using Java
or EN.605.207	Introduction to Programming Using C++
EN.625.250	Multivariable Calculus and Complex Analysis	3
EN.605.256	Modern Software Concepts in Python	3
EN.605.203	Discrete Mathematics	3
or EN.625.251	Introduction to Ordinary and Partial Differential Equations
or EN.625.252	Linear Algebra and Its Applications

¹

Applicants whose prior education does not include the courses listed under Admission Requirements may still enroll under provisional status, followed by full admission once they have completed the missing courses. All courses beyond calculus are available at Johns Hopkins Engineering and can be found above under the Undergraduate Courses heading. These courses do not count toward the degree or certificate requirements.

Core and Required Courses

Course List

Code	Title	Credits
Core Courses		Credits
EN.685.621	Algorithms for Data Science 2	3
EN.625.603	Statistical Methods and Data Analysis 2	3
Required Courses		Credits
EN.685.648	Data Science	3
EN.685.652	Data Engineering Principles and Practice	3
EN.685.662	Data Patterns and Representations	3
EN.625.661	Statistical Models and Regression	3
EN.625.615	Introduction to Optimization 3	3
or EN.625.664	Computational Statistics

²

Students must successfully complete or waive all core courses before enrolling in any other course.

³

EN.625.616 Optimization in Finance may be substituted.

Students who have been waived from core or required courses may replace the courses with the same number of other graduate courses. Students who take outside electives from other programs must meet the specific course prerequisites listed.

Focus Areas

Select one (1) of the following Focus Areas:

Data Management and Cloud Computing

Information Technology and Computation

Machine Learning and Artificial Intelligence

Operations Research

Courses by Focus Area

The focus areas offered represent related groups of courses that are relevant for students with interests in the selected areas. Students are required to choose a focus area and complete at least three courses from the selected focus area of which at least two must be 700-level. The focus areas are presented as an aid to students in planning their course selections and are only applicable to students seeking a master’s degree. They do not appear as official designations on a student’s transcript or diploma.

Data Management and Cloud computing

Course List

Code	Title	Credits
EN.685.701	Data Science: Modeling and Analytics	3
EN.605.632	Graph Analytics	3
EN.605.633	Social Media Analytics	3
EN.605.634	Crowdsourcing and Human Computation	3
EN.605.635	Cloud Computing	3
EN.605.641	Principles of Database Systems	3
EN.605.724	Applied Game Theory	3
EN.605.741	Large-Scale Database Systems	3
EN.605.744	Information Retrieval	3
EN.605.745	Reasoning Under Uncertainty	3
EN.605.788	Big Data Processing Using Hadoop	3
EN.635.632	Engineering Data Intensive Systems	3

information technology and computation

Course List

Code	Title	Credits
EN.625.620	Mathematical Methods for Signal Processing	3
EN.625.636	Graph Theory	3
EN.625.638	Foundations of Neural Networks	3
EN.625.680	Cryptography	3
EN.625.687	Applied Topology	3
EN.625.690	Computational Complexity and Approximation	3
EN.625.725	Theory Of Statistics I	3
EN.625.726	Theory of Statistics II	3
EN.625.734	Queuing Theory	3
EN.625.740	Data Mining	3
EN.625.742	Theory of Machine Learning	3
EN.625.744	Modeling, Simulation, and Monte Carlo	3

machine Learning and Artificial intelligence

Course List

Code	Title	Credits
EN.685.701	Data Science: Modeling and Analytics	3
EN.605.633	Social Media Analytics	3
EN.605.634	Crowdsourcing and Human Computation	3
EN.605.645	Artificial Intelligence	3
EN.605.647	Neural Networks	3
EN.605.740	Machine Learning: Deep Learning	3
EN.605.742	Deep Neural Networks	3
EN.605.743	Advanced Artificial Intelligence	3
EN.635.603	AI/ML Ops	3
EN.635.661	Principles of Human Computer Interaction	3
EN.705.605	Introduction to Generative AI	3
EN.705.608	Applied Generative AI	3
EN.705.742	Advanced Applied Machine Learning	3

operations research

Course List

Code	Title	Credits
EN.625.601	Real Analysis	3
EN.625.609	Matrix Theory	3
EN.625.611	Computational Methods	3
EN.625.615	Introduction to Optimization	3
EN.625.618	Discrete Hybrid Optimization	3
EN.625.623	Introduction to Operations Research: Probabilistic Models	3
EN.625.633	Monte Carlo Methods	3
EN.625.641	Mathematics of Finance	3
EN.625.642	Mathematics of Risk, Options, and Financial Derivatives	3
EN.625.663	Multivariate Statistics and Stochastic Analysis	3
EN.625.664	Computational Statistics	3
EN.625.665	Bayesian Statistics	3
EN.625.692	Probabilistic Graphical Models	3
EN.625.695	Time Series Analysis	3
EN.625.714	Introductory Stochastic Differential Equations with Applications	3
EN.625.717	Advanced Differential Equations: Partial Differential Equations	3
EN.625.718	Advanced Differential Equations: Nonlinear Differential Equations and Dynamical Systems	3
EN.625.721	Probability and Stochastic Processes I	3
EN.625.722	Probability and Stochastic Processes II	3
EN.625.728	Theory of Probability	3
EN.625.741	Game Theory	3
EN.625.743	Stochastic Optimization & Control	3

independent study

Course List

Code	Title	Credits
EN.685.795	Capstone Project in Data Science	3
EN.685.801	Independent Study in Data Science I	3
EN.685.802	Independent Study in Data Science II	3

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