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Master's

Data Science, Master of Science in Engineering

Johns Hopkins University Whiting School of Engineering 116 courses

About This Program

Department website: https://engineering.jhu.edu/ams/academics/graduate-studies/ The Data Science Master’s program at the Johns Hopkins University is a fully residential program which 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. The final Capstone Experience in Data Science (EN.553.806) is a research-oriented project which must be approved by the research supervisor and the Internal Oversight Committee. The Capstone Experience can be taken in multiple semesters, but the total number of credits required for successful completion is six (6). 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.

Degree Requirements

Core Requirements

Code Course Title Credits
EN.553.636 or EN.601.675 Introduction to Data Science or Machine Learning 3.0

Machine Learning

Code Course Title Credits
EN.520.612 Machine Learning for Signal Processing 3
EN.520.637 Foundations of Reinforcement Learning 3
EN.520.638 Deep Learning 3
EN.520.648 Compressed Sensing and Sparse Recovery 3
EN.520.651 Random Signal Analysis 4
EN.520.666 Information Extraction 3
EN.525.724 Introduction to Pattern Recognition 3
EN.530.641 Statistical Learning For Engineers 3
EN.535.741 Optimal Control and Reinforcement Learning 3
EN.553.602 Research and Design in Applied Mathematics: Data Mining 4
EN.553.724 Probabilistic Machine Learning 3
EN.553.738 High-Dimensional Approximation, Probability, and Statistical Learning 3
EN.553.740 Machine Learning I 3
EN.553.741 Machine Learning II 3
EN.553.743 Equivariant Machine Learning 3
EN.601.634 Randomized and Big Data Algorithms 3
EN.601.674 ML: Learning Theory 3
EN.601.675 Machine Learning 3
EN.601.677 Causal Inference 3
EN.601.682 Machine Learning: Deep Learning 4
EN.601.779 Machine Learning: Advanced Topics 3
EN.625.692 Probabilistic Graphical Models 3

4 or 5 Additional Courses (depending on whether student chooses Course Only Option or Capstone Option

The following additional courses may be taken to fulfill the elective requirement. Courses listed in the core areas may be taken the elective requirement, however, they may not be double-counted. See our website for concentrations as well as any updates to the course listing.

Code Course Title Credits
EN.520.614 Image Processing & Analysis 3
EN.520.615 Image Processing & Analysis II 3
EN.520.623 Medical Image Analysis 3
EN.520.635 Digital Signal Processing 3
EN.520.640 Machine Intelligence on Embedded Systems 3
EN.520.646 Wavelets & Filter Banks 3
EN.520.650 Machine Intelligence 3
EN.520.659 Machine learning for medical applications 3
EN.520.661 AI and Biometric Systems: Techniques, Applications, and Ethics 3
EN.520.665 Machine Perception 3
EN.520.684 Generative AI for Speech and Audio 3
EN.520.688 Learning-enabled Multi-agent Systems 3
EN.525.733 Deep Learning for Computer Vision 3
EN.553.627 Stochastic Processes and Applications to Finance I 4
EN.553.628 Stochastic Processes and Applications to Finance II 4
EN.553.633 Monte Carlo Methods 4
EN.553.635 Bayesian Statistics for the Physical Sciences 3
EN.553.641 Equity Markets and Quantitative Trading 3
EN.553.642 Investment Science 4
EN.553.644 Introduction to Financial Derivatives 4
EN.553.645 Interest Rate and Credit Derivatives 4
EN.553.646 Risk Measurement/Management in Financial Markets 4
EN.553.647 Quantitative Portfolio Theory and Performance Analysis 4
EN.553.649 Advanced Equity Derivatives 4
EN.553.650 Computational Molecular Medicine 4
EN.553.689 Software Engineering for Data Science 3
EN.553.693 Mathematical Image Analysis 4
EN.553.744 Data Science Methods for Large Scale Graphs 3
EN.553.753 Commodity Markets: Electricity and Natural Gas, Oil, Metals, and Agriculturals 3
EN.553.792 Matrix Analysis and Linear Algebra 4
EN.580.627 Deep Learning for Medical Imaging 3
EN.580.658 Computing the Transcriptome 3
EN.580.691 Learning, Estimation and Control 3
EN.580.707 Algorithms for Analysis of Genomic Sequence Data 3
EN.585.788 Foundations of Computational Biology and Bioinformatics 3
EN.601.615 Databases 3
EN.601.635 Approximation Algorithms 3
EN.601.641 Blockchains and Cryptocurrencies 3
EN.601.642 Modern Cryptography 3
EN.601.661 Computer Vision 3
EN.601.663 Algorithms for Sensor-Based Robotics 3
EN.601.226 Data Structures 4
EN.601.664 Artificial Intelligence 3
EN.601.665 Natural Language Processing 4
EN.601.666 Information Retrieval and Web Agents 3
EN.601.667 Introduction to Human Language Technology 3
EN.601.668 Machine Translation 3
EN.601.670 Artificial Agents 3
EN.601.671 Natural Language Processing: Self-Supervised Models 3
EN.601.690 Introduction to Human-Computer Interaction 3
EN.601.752 Advanced Topics in Single-cell & Spatial Genomics 3
EN.601.773 Machine Social Intelligence 3
EN.601.783 Vision as Bayesian Inference 3
EN.601.788 Machine Learning for Healthcare 3
EN.605.620 or EN.605.621 Algorithms for Bioinformatics or Foundations of Algorithms 3
EN.605.621 Foundations of Algorithms 3
EN.605.626 Image Processing 3
EN.605.653 Computational Genomics 3
EN.650.683 Cybersecurity Risk Management 3

Click a course code or title to read the full course description.

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