Eligibility Requirements for Johns Hopkins MSc in Data Science


  • A Bachelor's degree: Applicants must have a Bachelor's degree from an accredited institution. While a degree in a related field is preferred, applicants from other fields will also be considered.

  • Transcripts: Applicants must submit official transcripts from all colleges and universities they have attended.

  • GPA: Applicants must have a minimum undergraduate grade point average (GPA) of 3.0 on a 4.0 scale.

  • Prerequisite courses: Applicants must have completed courses in programming (e.g., Python, Java, C++) and linear algebra.

  • Standardized tests: Applicants must submit scores from the Graduate Record Examination (GRE) or the Graduate Management

  • Admission Test (GMAT). Alternatively, applicants may submit scores from the Medical College Admission Test (MCAT) or the Law School Admission Test (LSAT).

  • English proficiency: Applicants whose native language is not English must demonstrate English proficiency by submitting scores from the Test of English as a Foreign Language (TOEFL) or the International English Language Testing System (IELTS).

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    Bachelor's GPA Requirement

    Engineering degree

    UG Requirement

    Test Scores Requirement

    ExamRequirement

    Program Eligibility URL:https://engineering.jhu.edu/ams/academics/graduate-studies/ms-in-data-science/

    Johns Hopkins MSc in Data Science Rankings

    About Johns Hopkins University

    Johns Hopkins University is a private research university located in Baltimore, Maryland, USA. It was founded in 1876 and named after its benefactor, the entrepreneur and philanthropist Johns Hopkins. Today, the university is widely recognized as one of the top universities in the world, particularly in the fields of medicine, public health, and engineering.


    The university has nine academic divisions, including the Krieger School of Arts and Sciences, the Whiting School of Engineering, the School of Medicine, and the Bloomberg School of Public Health. The university also has a campus in Washington, D.C., and international centers in Italy, China, and Singapore.

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    Johns Hopkins MSc in Data Science Program Description

    Data Science Graduate Program at Johns Hopkins University

    Johns Hopkins Engineering for Professionals online, part time Data Science graduate program addresses the huge demand for data scientists qualified to serve as knowledgeable resources in our ever-evolving, data-driven world.

    Designed specifically with working professionals in mind, you will engage in a number of modern courses created to expand your knowledge for advanced career opportunities in data science, including Machine Learning, Data Visualization, Game Theory, and Large-Scale Data Systems. Learn from senior-level engineers and data scientists who will incorporate realistic scenarios in your studies that you have or will encounter as a professional. 

    You will be prepared to succeed in specialized jobs involving everything from the data pipeline and storage to statistical analysis and eliciting the story the data tells.

    • Gain practical skills and advance your career to meet the growing demand for data scientists.
    • Balance both the theory and practice of applied mathematics and computer science to analyze and handle large-scale data sets.
    • Manage and manipulate information to discover relationships and insights into complex data sets.
    • Create models using formal techniques and methodologies of abstraction that can be automated to solve real-world problems.
    • Select the courses that fit your area of interest.
    • Become a confident data scientist and leader.
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    Program URL:https://engineering.jhu.edu/ams/academics/graduate-studies/ms-in-data-science/

    Johns Hopkins MSc in Data Science Program Curriculum


  • Applied Machine Learning: This course covers the theory and practice of machine learning, including supervised and unsupervised learning, neural networks, and deep learning.

  • Data Visualization: This course covers the principles and techniques of data visualization, including the use of visualization tools and software to create effective visualizations.

  • Cloud Computing: This course provides an introduction to cloud computing and covers topics such as cloud infrastructure, cloud storage, and cloud security.

  • Natural Language Processing: This course covers the processing and analysis of human language, including topics such as sentiment analysis, text classification, and language generation.

  • Time Series Analysis: This course covers the analysis of time series data, including forecasting, smoothing, and seasonality analysis.
    High-Performance Computing: This course covers the principles and techniques of high-performance computing, including parallel computing, distributed computing, and cloud computing.

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    Electives

    • Applied Machine Learning: This course covers the theory and practice of machine learning, including supervised and unsupervised learning, neural networks, and deep learning.
    • Data Visualization: This course covers the principles and techniques of data visualization, including the use of visualization tools and software to create effective visualizations.
    • Cloud Computing: This course provides an introduction to cloud computing and covers topics such as cloud infrastructure, cloud storage, and cloud security.
    • Natural Language Processing: This course covers the processing and analysis of human language, including topics such as sentiment analysis, text classification, and language generation.
    • Time Series Analysis: This course covers the analysis of time series data, including forecasting, smoothing, and seasonality analysis.
      High-Performance Computing: This course covers the principles and techniques of high-performance computing, including parallel computing, distributed computing, and cloud computing.

    Courses included:

    • Python
    • Programming Using Java
    • Data Structures
    • Discrete Mathematics
    • General Applied Mathematics
    • Multivariable and Complex Analysis
    • Ordinary and Partial Differential Equations
    • Computer Science
    • Data Science & Big Data

    Johns Hopkins MSc in Data Science Deadlines

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    Johns Hopkins MSc in Data Science Employment

    Companies Recruiting Johns Hopkins MSc in Data Science Graduates

    Amazon
    Microsoft
    Google
    IBM
    JPMorgan Chase & Co.
    Deloitte
    Booz Allen Hamilton
    Capital One
    Lockheed Martin
    UnitedHealth Group

    Scholarships available for International Students

    The average annual cost of study, including tuition fees and living expenses is around USD$35,000 per year. However, this can rise dramatically as your day-to-day living expenses will vary depending on what state or city you live in.Below are some scholarships you can avail to reduce your study abroad expenses drastically.

    1. The Global Study Awards

      The Global Study Awards is a competition for successful IELTS test takers who have shown exceptional promise. You can enter the competition if you plan to use your IELTS results to apply for an undergraduate or postgraduate degree in an English-speaking country.

    2. Gary Saitowitz Scholarship

      Gary Saitowitz founded the Gary Saitowitz Scholarship with the objective of assisting one deserving student in reducing the stress and also the overall financial burden of costly higher education.

    3. Form Swift Scholarships Program

      The scholarship program is for driven, up-and-coming, entrepreneurs who are ready to commit to building a better world through their business ideas.

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