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Masters

Data Science, Master of Science

Johns Hopkins University
Baltimore , United States
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Medium of studying
Fully Online
Duration
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Program Details
Degree
Masters
Major
Artificial Intelligence | Data Analysis | Data Science
Area of study
Information and Communication Technologies | Mathematics and Statistics
Education type
Fully Online
Course Language
English
About Program

Program Overview

Data Science, Master of Science

The Data Science Master's program at the Whiting School of Engineering is designed to provide students with a comprehensive education in data science, including the principles and methods of data analysis, machine learning, and visualization.


Admission Requirements

Applicants must meet the general requirements for admission to graduate study. The applicant's prior education must include:


  • Three semesters or five quarters of calculus, which includes multivariate calculus
  • One semester/term of advanced math (Linear Algebra is strongly preferred but Discrete Mathematics or Differential Equations will be accepted)
  • 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.


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.


Core and Required Courses

  • Core Courses:
    • EN.685.621: Algorithms for Data Science 2
    • EN.625.603: Statistical Methods and Data Analysis 2
  • Required Courses:
    • EN.685.648: Data Science
    • EN.685.652: Data Engineering Principles and Practice
    • EN.685.662: Data Patterns and Representations
    • EN.625.661: Statistical Models and Regression
    • EN.625.615: Introduction to Optimization 3

Focus Areas

Select one of the following Focus Areas:


  1. Data Management and Cloud Computing
  2. Information Technology and Computation
  3. Machine Learning and Artificial Intelligence
  4. Operations Research

Courses by Focus Area

Each focus area has a list of courses that students can choose from to fulfill their focus area requirements.


Data Management and Cloud Computing

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

Information Technology and Computation

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

Machine Learning and Artificial Intelligence

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

Operations Research

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

Independent Study

  • EN.685.795: Capstone Project in Data Science
  • EN.685.801: Independent Study in Data Science I
  • EN.685.802: Independent Study in Data Science II
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