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. One year of calculus (2 semesters or 3 quarters);
2. An introductory course in probability and statistics and
3. Familiarity with the programing language Python (demonstrated through credit-bearing coursework, MOOC course completion with verification, or work experience).
4. Students pursuing the Simulation and Modeling Focus Area are strongly recommended to have completed Calculus III (Multivariable Calculus) or equivalent.
Applicants whose prior education does not include the prerequisites listed above may still enroll under provisional status, followed by full admission status once they have completed the missing prerequisites. Missing prerequisites may be completed with Johns Hopkins Engineering (all prerequisites are available from the table below) or at another regionally accredited institution. 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. These prerequisite courses do not count toward the degree requirements. Transcripts from all college studies must be submitted. When reviewing an application, the candidate’s academic and professional background will be considered.
Provisional Courses
| Code | Title | Credits |
|---|---|---|
| EN.625.108 & EN.625.109 | Calculus I and Calculus II | 4 |
| or EN.605.156 | Calculus for Engineers | |
| EN.625.240 | Introduction to Probability and Statistics | 3 |
| EN.625.250 | Multivariable Calculus and Complex Analysis 1 | 3 |
| EN.605.206 | Introduction to Programming Using Python | 3 |
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Students pursuing the Simulation and Modeling Focus Area are strongly recommended to have completed Calculus III (Multivariable Calculus) or equivalent.
Program Requirements
To earn a Master of Science in Data Analytics Engineering, 10 courses (30 credits) approved by an advisor, must be completed within five years. The curriculum consists of four (12 credits) required core courses, four (12 credits) elective courses from the same focus area with at least two at the 700-level, and two (6 credits) remaining courses from the focus area lists or any relevant external course in the Computer Science, Cybersecurity, Applied and Computational Mathematics, Information Systems Engineering, Data Science, or Artificial Intelligence program. Only one C-range grade (C+, C, or C–) can count toward the master’s degree.
Core Courses
| Code | Title | Credits |
|---|---|---|
| Core | Credits | |
| EN.635.631 | Foundations of Data Analytics | 3 |
| EN.635.782 | Ethics in Intelligent Systems | 3 |
| EN.685.652 | Data Engineering Principles and Practice | 3 |
| EN.685.662 | Data Patterns and Representations | 3 |
Focus Areas
Choose at least four courses from a focus area with at least two courses at the 700-level (i.e. xxx.7xx)
Artificial Intelligence
Cybersecurity
Data Engineering
Machine Learning and Cloud Computing
Simulation and Modeling
Artificial Intelligence
| Code | Title | Credits |
|---|---|---|
| Artificial Intelligence Focus Area Core | Credits | |
| EN.685.621 | Algorithms for Data Science 1 | 3 |
| Artificial Intelligence Focus Area Electives | Credits | |
| EN.605.645 | Artificial Intelligence | 3 |
| EN.605.724 | Applied Game Theory | 3 |
| EN.605.745 | Reasoning Under Uncertainty | 3 |
| EN.635.603 | AI/ML Ops | 3 |
| EN.635.627 | Intelligent Decision Support Systems | 3 |
| EN.695.715 | Assured Autonomy | 3 |
| AS.470.743 | Data Mining and Predictive Analytics | 3 |
| AS.473.602 | Intelligence Analysis | 3 |
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Must be taken prior to any courses in the Artificial Intelligence Focus Area and is counted as one (1) of the required four courses.
Cybersecurity
| Code | Title | Credits |
|---|---|---|
| Cybersecurity Focus Area Core Course | Credits | |
| EN.695.601 | Foundations of Information Assurance 2 | 3 |
| Cybersecurity Focus Area Electives | Credits | |
| EN.625.680 | Cryptography | 3 |
| EN.635.676 | Cybersecurity in Information Systems | 3 |
| EN.635.775 | Cyber Operations, Risk, and Compliance | 3 |
| EN.695.622 | Web Security | 3 |
| EN.695.721 | Network Security | 3 |
| AS.470.671 | Risk Management Analytics | 3 |
| AS.470.731 | Privacy in a Data-driven Society | 3 |
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EN.695.601 - Foundations of Information Assurance must be taken prior to any courses in the Cybersecurity Focus Area and is counted as one (1) of the four courses.
Data Engineering
EN.685.621 - Algorithms for Data Science must be taken prior to any courses in the Data Engineering Focus Area and is counted as one (1) of the four courses.
| Code | Title | Credits |
|---|---|---|
| Data Engineering Focus Area Core Course | Credits | |
| EN.685.621 | Algorithms for Data Science 3 | 3 |
| Data Engineering Focus Area Electives | Credits | |
| EN.685.603 | Foundations of Algorithm Analysis | 3 |
| EN.605.741 | Large-Scale Database Systems | 3 |
| EN.605.788 | Big Data Processing Using Hadoop | 3 |
| EN.635.601 | Foundations of Information Systems Engineering | 3 |
| EN.635.632 | Data Engineering for AI Systems | 3 |
| EN.635.671 | Data Recovery & Continuing Operations | 3 |
| EN.685.701 | Data Science: Modeling and Analytics | 3 |
| AS.470.703 | Urban Data Analytics | 3 |
| AS.470.764 | Survey Methodology | 3 |
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EN.685.621 - Algorithms for Data Science must be taken prior to any courses in the Data Engineering Focus Area and is counted as one (1) of the four courses.
Machine Learning and Cloud Computing
| Code | Title | Credits |
|---|---|---|
| Machine Learning and Cloud Computing Focus Area Core Course | Credits | |
| EN.685.621 | Algorithms for Data Science 4 | 3 |
| Machine Learning and Cloud Computing Focus Area Electives | Credits | |
| EN.605.633 | Social Media Analytics | 3 |
| EN.605.635 | Cloud Computing | 3 |
| EN.605.646 | Natural Language Processing | 3 |
| EN.605.744 | Information Retrieval | 3 |
| EN.625.742 | Theory of Machine Learning | 3 |
| EN.705.601 | Applied Machine Learning | 3 |
| EN.705.742 | Advanced Applied Machine Learning | 3 |
| AS.470.643 | Text as Data | 3 |
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EN.685.621 - Algorithms for Data Science must be taken prior to any courses in the Machine Learning and Cloud Computing Focus Area and is counted as one (1) of the four courses
Simulation and Modeling
EN.625.603 - Statistical Methods and Data Analysis must be taken prior to any courses in the Simulation and Modeling Focus Area and is counted as one (1) of the four courses.
| Code | Title | Credits |
|---|---|---|
| Simulation and Modeling Focus Area Core Course | Credits | |
| EN.625.603 | Statistical Methods and Data Analysis 5 | 3 |
| Simulation and Modeling Focus Area Electives | Credits | |
| EN.605.631 | Statistical Methods for Computer Science | 3 |
| EN.605.716 | Modeling and Simulation of Complex Systems | 3 |
| EN.625.661 | Statistical Models and Regression | 3 |
| EN.625.664 | Computational Statistics | 3 |
| EN.625.695 | Time Series Analysis | 3 |
| EN.625.734 | Queuing Theory | 3 |
| EN.625.740 | Data Mining | 3 |
| EN.625.741 | Game Theory | 3 |
| EN.685.640 | Mathematical Reasoning and Structure for Data Science | 3 |
| AS.470.758 | Data-Driven Campaigns and Elections | 3 |
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EN.625.603 - Statistical Methods and Data Analysis must be taken prior to any courses in the Simulation and Modeling Focus Area and is counted as one (1) of the four courses.