1. Johns Hopkins University
  2. Program
  3. Masters
  4. MSE Data Science

MSE Data Science

2 Years On Campus Masters Program

Johns Hopkins University

Program Overview

Johns Hopkins University’s Master of Science in Data Science (MSDS) is a rigorous, STEM-designated graduate program that combines the mathematical foundations of data science with cutting-edge computing and statistical methods. Housed in the Department of Applied Mathematics and Statistics (AMS) at the Whiting School of Engineering, the program prepares students to extract insights from complex datasets and lead innovation across data-intensive industries.

Students build strong competencies in statistical modeling, machine learning, algorithm design, and data ethics—equipping them to manage the full data lifecycle from acquisition to actionable decision-making.

Program Structure

  • Full-time program, typically completed in 3 semesters (1.5 years)

  • STEM-designated (eligible for up to 3 years of OPT for international students)

  • Research-focused with the option for a thesis or course-only track

Core Curriculum Components

A minimum of ten courses (30 credits) are required, including core and elective components:

Mathematical and Statistical Core

  • EN.553.620: Introduction to Probability

  • EN.553.630: Statistical Methods and Data Analysis

  • EN.553.732: Statistical Inference

  • EN.553.761: Machine Learning or EN.553.730: Statistical Computing

Computer Science & Data Engineering Core

  • EN.601.226: Data Structures (or equivalent for students with CS background)

  • EN.601.433: Intro to Algorithms or EN.601.476: Machine Learning: Deep Learning

  • EN.601.476: Data Science or related electives

Electives (sample areas)

Students customize their learning by selecting electives across:

  • Bayesian Statistics

  • Natural Language Processing

  • Big Data Systems

  • Neural Networks and Deep Learning

  • Optimization Theory

  • Applied Econometrics

  • Genomic Data Science (via Bloomberg School of Public Health or Biostatistics Department)

Experiential Learning (Research, Projects, Internships etc.)

The JHU MSDS program offers extensive opportunities to apply data science skills in real-world and research contexts, reflecting the university’s deep strengths in interdisciplinary research, health sciences, and public sector innovation.

Capstone / Research Thesis (Optional)

Students may choose between:

  • A coursework-only track, ideal for industry-bound professionals

  • A research-oriented track with a faculty-supervised thesis project, often aligned with labs in engineering, public health, or AI research

Faculty-Led Labs & Research Centers

Students can contribute to ongoing data science research in labs such as:

  • Johns Hopkins Malone Center for Engineering in Healthcare

  • Institute for Data-Intensive Engineering and Science (IDIES)

  • Center for Language and Speech Processing

  • Applied Physics Laboratory (APL)
    Research topics include:

  • Predictive health analytics

  • Data security and anomaly detection

  • Imaging and sensor-based data modeling

  • Urban informatics and mobility modeling

Industry-Focused Learning

  • Industry-sponsored projects and hackathons

  • Frequent guest lectures from industry leaders in health tech, finance, and AI

  • Access to Baltimore–D.C. tech ecosystem and proximity to institutions like NIH, FDA, and NASA

Technical Skill Development

Students build hands-on proficiency in:

  • Python, R, MATLAB, SQL

  • TensorFlow, PyTorch, Keras

  • Cloud platforms (AWS, Azure, Google Cloud)

  • Big data frameworks (Hadoop, Spark)

  • Data visualization tools (Tableau, Plotly)

Progression & Future Opportunities

Graduates of JHU’s MS in Data Science program are recognized for their mathematical strength, computational depth, and practical experience. The program's engineering orientation and proximity to major research and policy institutions position students for leadership roles in a variety of sectors.

Career Outcomes

Students commonly pursue roles such as:

  • Data Scientist / Machine Learning Engineer

  • Quantitative Analyst / Risk Modeler

  • AI Product Developer / Software Engineer (ML Focus)

  • Biostatistician / Public Health Data Analyst

  • Computational Research Scientist

  • Cybersecurity Data Analyst

Hiring Industries

  • Healthcare & Bioinformatics: Johns Hopkins Medicine, NIH, Pfizer, Merck

  • Technology: Google, Amazon, Microsoft, Palantir

  • Finance & Quant: JPMorgan Chase, Citadel, Goldman Sachs

  • Government & Research: NASA, CDC, U.S. Digital Service

  • Startups & Social Impact: AI for Good, Civic Tech, Urban Labs

Graduate & Doctoral Pathways

Outstanding students may pursue:

  • PhD in Data Science, Applied Math, Statistics, or CS

  • Joint programs with Biostatistics or Biomedical Engineering

  • Interdisciplinary roles in AI ethics, urban science, or health policy

Program Key Stats

$64,730
$ 0

Jan Intake : 15th SepAug Intake : 15th Dec


13 %
No
No
Yes
Yes

Eligibility Criteria

3.0
3 Year

320
160
153
650
7.0
100
2:1

Additional Information & Requirements

Career Options

  • Typical Job Titles & Roles Graduates secure a wide range of analytics-focused roles across industries: Data Analyst Data Scientist Business Analyst Statistician / Statistical Programmer SAS Programmer Machine Learning Engineer Advisory Consultant / Analytics Consultant   Salary Overview Average starting salary: ~$72
  • 241 USD Salary range: $55K–$100K
  • with nearly half reporting a signing bonus averaging $6
  • 350  79% of the class employed within six months of graduation    Top Employers Recent grads have joined prominent U
  • S
  • employers
  • such as: American Express Citi Group Food and Drug Administration (FDA) Leading consulting and tech firms   Geographic Placement US Major Centers: New York City – ~32% of graduates Boston – ~11% Chicago – ~8%  Some alumni pursue international roles
  • but the primary focus is on strong U
  • S
  • placement   Industry Sectors Finance & Banking (credit risk
  • consumer analytics) Tech & Software (data services
  • ML applications) Government & Public Health (regulatory stats
  • FDA roles) Consulting (advisory on analytics strategy) Healthcare & Biotech (data-driven clinical work)   Career Growth & Progression Graduates report clarity in progression from: Analyst-level roles—e
  • g
  • Data Analyst or Statistician Advancing to Machine Learning Engineer
  •  Analytics Consultant
  • or Quantitative Analyst Moving into leadership
  • strategic analytics
  • or domain-specialist roles   Career Highlights Summary Aspect Details Salary Avg $72K ($55K–$100K)
  • ~$6K signing bonus  Roles Data Analyst/Scientist
  • Statistician
  • ML Engineer
  • Consultant  Employers AmEx
  • Citi
  • FDA
  • major tech & consultancies  Locations NYC (32%)
  • Boston (11%)
  • Chicago (8%)  Employment Rate    ~79% employed within 6 months    What This Means for You: Solid salary entry: Positions start around $70K–$100K
  • with strong bonus potential—ideal for new data professionals in the U
  • S
  • Diverse career paths: From healthcare and government to finance and consulting
  • there
  • 's flexibility to pursue your interests
  • Geographically strategic: Major employers in NYC
  • Boston
  • Chicago — leverage strong local recruitment patterns
  • Strong post-degree momentum: High placement rates suggest the degree leads to sustained career growth in analytics
  • ML
  • and consulting roles

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