
DATA SCIENCE
The Data Science Master’s degree at the Johns Hopkins University will provide the training in applied mathematics, statistics and computer science to serve as the basis for an understanding, and appreciation, of existing data science tools. Our program aims to produce the next generation of leaders in data science by emphasizing mastery of the skills needed to translate real-world data-driven problems in mathematical ones, and then solving these problems by using a diverse collection of scientific tools.
PROGRAM REQUIREMENTS
In addition to Introduction to Data Science (EN.553.636), students will take one course in each of the four core areas: Statistics, Machine Learning, Optimization, and Computing. Students will decide on an area of focus and take three courses in either Computational Medicine, Computational Machine Learning, Computer Vision, Computational Finance, Mathematics of Data Science, Language and Speech, or Statistical Theory. The final capstone project is course EN.553.806 Capstone Experience in Data Science, and include a research topic approved by the faculty advisor and the Internal Oversight Committee, and a written paper. The goal of the final course and written paper is to allow the student to apply data analysis techniques learned in the program, and possibly to extend those ideas to more general settings or to new application areas. Lastly, the paper will be summarized in a poster session organized at the end of each semester.
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- A two-day orientation program will precede the first Fall semester of enrollment.
- EN.553.636 Introduction to Data Science, and
One course in each of the four Core Areas below. Courses chosen in this section must be distinct from the courses used to satisfy requirements 2 and 3.
- Statistics – Introduction to Statistics (EN.553.630), Bayesian Statistics (EN.553.632), Statistical Theory I (EN.553.730), Statistical Theory II (EN.553.731)
- Machine Learning – Statistical Machine Learning: Methods, Theory, and Applications (PH.140.644), Machine Learning (EN.601.675), Statistical Machine Learning (EN.601.775), Machine Learning (EN.553.740)
- Optimization – Nonlinear Optimization I (EN.553.761), Nonlinear Optimization II (EN.553.762), Convex Optimization (EN.553.765)
- Computing – Computing for Applied Mathematics (EN.553.688), Parallel Programming (EN.601.620)
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Three courses from one of the following focus areas.
Computational Medicine: CHOOSE 3 |
AS.410.633 Introduction to Bioinformatics |
AS.410.635 Bioinformatics: Tools for Genome Analysis |
AS.410.661 Methods in Proteomics |
AS.410.671 Gene Expression Data Analysis and Visualization |
EN.553.650 Computational Molecular Medicine |
EN.605.620 Algorithms for Bioinformatics -or- EN.605.621 |
EN.605.621 Foundations of Algorithms -or- EN.605.620 |
EN.605.653 Computational Genomics |
EN.605.754 Analysis of Gene Expression and High-Content Biological Data |
Computational Machine Learning: CHOOSE 3 |
PH.140.644 Statistical Machine Learning: Methods, Theory, and Applications |
EN.520.612 Machine Learning for Signal Processing |
EN.520.647 Introduction to Information Theory and Coding |
EN.520.648 Compressed Sensing and Sparse Recovery |
EN.520.651 Random Signal Analysis |
EN.520.666 Information Extraction |
EN.553.602 Research and Design in Applied Mathematics: Data Mining |
EN.553.740 Machine Learning I |
EN.553.743 Graphical Models |
EN.601.675 Machine Learning |
EN.601.676 Machine Learning: Data to Models |
EN.601.677 Causal Inference |
EN.601.679 Representation Learning |
EN.601.681 Machine Learning: Optimization |
EN.601.682 Machine Learning: Deep Learning |
EN.601.775 Statistical Machine Learning |
EN.601.800 Unsupervised Learning: Big Data to Low-Dimensional Representations |
Computer Vision: CHOOSE 3 |
EN.520.648 Compressed Sensing and Sparse Recovery |
EN.601.661 Computer Vision |
EN.601.682 Machine Learning: Deep Learning |
EN.601.783 Vision as Bayesian Inference |
Computational Finance: CHOOSE 3 |
EN.553.627 Stochastic Processes and Applications to Finance I |
EN.553.628 Stochastic Processes and Applications to Finance II |
EN.553.641 Equity Markets and Quantitative Trading |
EN.553.642 Investment Science |
EN.553.644 Introduction to Financial Derivatives |
EN.553.645 Interest Rate and Credit Derivatives |
EN.553.646 Risk Measurement and Management in Financial Markets |
EN.553.647 Quantitative Portfolio Theory & Performance Analysis |
EN.553.648 Financial Engineering and Structured Products |
EN.553.649 Advanced Equity Derivatives |
EN.553.753 Commodity Markets and Trade Finance |
Mathematics of Data Science: CHOOSE 3 |
EN.553.633 Monte Carlo Methods |
EN.553.665 Introduction to Convexity |
EN.553.738 High-Dimensional Approximation, Probability, and Statistical Learning |
EN.553.740 Machine Learning I |
EN.553.741 Machine Learning II |
EN.553.761 Nonlinear Optimization I |
EN.553.762 Nonlinear Optimization II |
EN.553.763 Stochastic Search and Optimization |
EN.553.765 Convex Optimization |
EN.533.766 Combinatorial Optimization |
EN.553.792 Matrix Analysis and Linear Algebra |
EN.601.634 Randomized and Big Data Algorithms |
EN.601.635 Appromization Algorithms |
Language and Speech: CHOOSE 3 |
AS.050.617 Semantics I |
AS.050.622 Semantics II |
AS.050.620 Syntax |
AS.050.625 Phonology |
EN.520.666 Information Extraction |
EN.520.680 Speech and Auditory Processing by Humans and Machines |
EN.601.665 Natural Language Processing |
EN.601.765 Machine Learning: Linguistic and Sequence Modeling |
Statistical Theory: CHOOSE 3 |
PH.140.644 Statistical Machine Learning: Methods, Theory, and Applications |
EN.533.630 Introduction to Statistics |
EN.553.730 Statistical Theory |
EN.553.731 Statistical Theory II |
EN.553.632 Bayesian Statistics |
EN.553.735 Topics in Statistical Pattern Recognition |
EN.553.737 Distribution-free Statistics and Resampling Methods |
EN.553.738 High-Dimensional Approximation, Probability, and Statistical Learning |
EN.553.739 Statistical Pattern Recognition Theory & Methods |
EN.601.677 Causal Inference |
EN.601.775 Statistical Machine Learning |
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- The program requires the student to take one elective course. To maximize a student’s flexibility in choosing this course, the student may choose from any course listed in the Areas of Focus as well as EN.520.620.
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Capstone Experience in Data Science (EN.553.806), or another project-oriented course approved by the research supervisor, academic advisor and the Internal Oversight Committee. Students must complete a Proposal Request for the Capstone Experience in Data Science form and follow instructions to submit for approval before being permitted to enroll in EN.553.806.
- The student must find and contact a research supervisor who will agree to supervise the capstone experience. The research supervisor must be a JHU faculty member.
- The student must complete a proposal form, describing the project goals, and submit to their academic advisor, who will in turn send it to the Internal Oversight Committee for approval.
- The proposal will include the following and must be submitted using the approved proposal request form (above):
- Title of proposed project
- Project description, with sufficient details (e.g., 200 words)
- Completion timeline
- Name(s) and signature(s) of faculty supervisor(s)
- Upon approval, the student will be permitted to register for EN.553.806: Capstone Experience in Data Science.
- Upon completion, the research supervisor will provide a Pass/Fail (P/F) grade.
- As part of the experience, the student must write a paper or research report that must be approved the the research supervisor. The final paper should be 6-12 pages in latex full-page format (1 inch margins, 12 point Times font) or ms-word equivalent.
- The written paper will be summarized in a poster presented in a poster session organized at the end of each semester.
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- Minimum Grade Requirements: A course grade of B- or higher is required to meet all course requirements.
- Academic probation: Any student receiving either one grade of D+, D, or F or two grades of C(+/–) during their program of study will be placed on academic probation. If an additional grade below B– is received before the course is repeated and successfully completed, the student will be dismissed. If the student retakes the course with a grade of B- or above, the probationary status will be lifted.
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- Retake policy: Students are allowed to retake a course only once (per course), upon agreement of both the student’s advisor and the course instructor.
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- Students must take an approved online data ethics course, such as the one available at https://www.edx.org/course/data-science-ethics-michiganx-ds101x-1.
- Students are required to complete the online responsible conduct of research training.
- Students are required to complete the Academic Ethics online course. Students will be auto-enrolled in this course upon matriculation.
- Beginning Fall 2021, all foreign students for whom English is not the first language, will be required to undergo assessment, and possible placement in one or more professional communication courses to help with grammar, pronunciation and idiomatic expression. More information about ESL courses, can be found on the Center for Leadership Education website.
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Prior to First Semester: |
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Orientation Program (2 days) |
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Year 1: FALL |
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Introduction to Data Science |
(4 courses) |
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Computing (Core) |
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Optimization (Core) |
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OPTION A |
Core Area 3 |
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OPTION B |
Area of Focus 1 |
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Year 1: Intersession |
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Online Data Science Ethics Course |
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Year 1: SPRING |
OPTION A |
Core Area 4 |
(4 courses) |
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Statistics |
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Area of Focus 2 |
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Area of Focus 3 |
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OPTION B |
Core Area 4 |
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Area of Focus 1 |
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Area of Focus 2 |
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Area of Focus 3 (or Elective) |
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Year 2: FALL OR SPRING |
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Capstone Experience |
(2 courses) |
OPTION A |
Elective |
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OPTION B |
Elective (or Area of Focus) |
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Faculty Name |
Department |
Email |
Arora, Raman |
Computer Science/MINDS |
[email protected] |
Basu, Amitabh |
Applied Mathematics/MINDS |
[email protected] |
Braverman, Vladimir |
Computer Science/MINDS |
[email protected] |
Budavari, Tamas |
Applied Mathematics/MINDS |
[email protected] |
Caffo, Brian |
Biostatistics/MINDS |
[email protected] |
Chellappa, Rama |
Electrical & Computer Eng/MINDS |
[email protected] |
Eisner, Jason |
Computer Science/MINDS |
[email protected] |
Fertig, Elana |
Oncology/MINDS |
[email protected] |
Naiman, Daniel |
Applied Mathematics |
[email protected] |
Patel, Vishal |
Electrical & Computer Eng/MINDS |
[email protected] |
Priebe, Carey |
Applied Mathematics/MINDS |
[email protected] |
Shpitser, Ilya |
Computer Science/MINDS |
[email protected] |
Venkataraman, Archana |
Electrical & Computer Eng/MINDS |
[email protected] |
Vidal, Rene |
Biomedical Engineering/MINDS |
[email protected] |
Xu, Yanxun |
Applied Mathematics/MINDS |
[email protected] |
Younes, Laurent, Program Director |
Applied Mathematics/MINDS |
[email protected] |