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AI and Agentic AI in Healthcare

AI and Agentic AI in Healthcare

Application closes 30th Jul 2026

Why should you join this program?

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    Healthcare-Focused AI Curriculum

    Learn from Johns Hopkins University faculty, and industry experts to leverage AI & Agentic AI to improve patient outcomes through real-world case studies, with no coding experience required.

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    Learn from a Global Leader in Medical Research and Healthcare

    Ranked #1 in Biomedical Engineering, #7 National University, #10 Most Innovative Schools by U.S. News and World Report, JHU is a leader in healthcare research and innovation. (2026 Rankings)

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PROGRAM OUTCOMES

What will you learn to build and apply?

Through a structured learning journey, you will build the capability to:

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    Apply the R.O.A.D. Management Framework to align AI initiatives with organizational clinical goals

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    Differentiate key algorithms and evaluate performance via accuracy and F1 score metrics

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    Analyze human baselines, prospect theory, status quo bias, and risk-based approaches in clinical workflows

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    Lead through the shift from manual task execution to human-in-the-loop and human-on-the-loop agent supervision

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    Distinguish reactive AI from goal-directed Agentic AI and evaluate use cases using an evidence-based rubric

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    Address PHI protection, regulatory fit, accountability, audit trail across EHR, payer, and clinical systems

Earn a Certificate from Johns Hopkins University

  • #7 National University Rankings

    #7 National University Rankings

    U.S. News & World Report, 2026

  • #2 Computer Information Technology

    #2 Computer Information Technology

    U.S. News & World Report, 2026

  • #10 Most Innovative Schools

    #10 Most Innovative Schools

    US News and World Report, 2026

  • #14 Best Global University

    #14 Best Global University

    U.S. News & World Report, 2026

  • #1 Biomedical Engineering Program

    #1 Biomedical Engineering Program

    US News and World Report, 2026

KEY PROGRAM HIGHLIGHTS

Why choose the AI in Healthcare Program?

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    Learn from JHU Faculty

    Learn through recorded lectures and attend faculty-led masterclasses covering AI project management, healthcare AI trends, and AI-powered clinical risk assessment.

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    Interactive Mentorship by Industry Experts

    Learn from healthcare AI practitioners through mentored sessions focused on practical applications, implementation challenges, and industry best practices.

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    Healthcare-Focused AI Curriculum

    Build expertise in clinical decision support, predictive analytics, population health, healthcare AI strategy, and responsible AI.

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    Real-World Healthcare Case Studies

    Analyze 8+ healthcare case studies covering disease prediction, clinical decision support, AI adoption, healthcare ethics, and patient care.

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    Earn a Recognized Credential from JHU

    Earn a Certificate of Completion and 6 CEUs from Johns Hopkins University upon successful completion of the program.

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    Personalized Program Support

    Receive 1:1 guidance from a dedicated Program Manager and academic support from subject matter experts.

Skills you will learn

Applying AI Solutions in Healthcare

Predictive Analytics for Disease Management

Ethical AI Practices and Regulatory Compliance

AI-Driven Decision Support Systems

AI Project Management for Healthcare Initiatives

Large Language Models (LLMs) in Healthcare

Machine Learning Algorithms for Clinical Applications

Robotic Process Automation in Clinical Settings

Human-in-the-Loop & Human-on-the-Loop Operating Models

Change Management for AI Adoption

The R.O.A.D. Management Framework

Transition from Task Execution to Agent Supervision

Applying AI Solutions in Healthcare

Predictive Analytics for Disease Management

Ethical AI Practices and Regulatory Compliance

AI-Driven Decision Support Systems

AI Project Management for Healthcare Initiatives

Large Language Models (LLMs) in Healthcare

Machine Learning Algorithms for Clinical Applications

Robotic Process Automation in Clinical Settings

Human-in-the-Loop & Human-on-the-Loop Operating Models

Change Management for AI Adoption

The R.O.A.D. Management Framework

Transition from Task Execution to Agent Supervision

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  • Overview
  • Learning Journey
  • Curriculum
  • Projects
  • Certificate
  • Faculty
  • Mentors
  • Reviews
  • Fees
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Who is the program for?

Professionals Seeking to Harness AI in Healthcare

  • Business and Strategy Leaders in Healthcare

    Lead AI-driven healthcare initiatives, optimize operational efficiency, and drive strategic business outcomes.

  • Medical, Pharmaceutical and Biotech Professionals

    Apply AI to enhance diagnostics, personalize treatment plans, and accelerate medical research.

  • Technical Professionals and Healthcare Consultants

    Master AI techniques to analyze healthcare data, automate routine tasks, and enhance clinical decision-making.

  • Regulators and Healthcare Policymakers

    Leverage AI for policy analysis, data-driven decision-making, and effective resource allocation in public health.

How is the program learning experience?

Through a structured learning approach, develop the strategic judgment and intuition to scale AI

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    Learn from Experts

    Learn from JHU faculty and industry experts to build AI powered healthcare solutions

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    Learn By Doing

    Apply AI concepts through case studies focused on patient outcomes and healthcare innovation

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    Earn a University Credential

    Earn a certificate of completion and 6 CEUs from Johns Hopkins University

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    Get Support Throughout the Learning Journey

    Program managers will help you stay on track, navigate key milestones & complete the program

Elevate Your Skills with an Optional Paid Program

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Applications of AI and Agentic AI in Healthcare

Add this program to your portfolio to:

  • Learn from world-renowned JHU faculty and experts
  • Build AI applications across healthcare from deployment to governance
  • Work with tools like ChatGPT | Claude | Gemini | Codex | n8n
  • Earn 7 Continuing Education units upon program completion

Reach out to your Program Advisor for more details

*image for illustration purposes only.

What will you learn in the program?

Designed by the renowned Johns Hopkins University faculty, the AI and Agentic AI in Healthcare covers foundational AI concepts, clinical decision support, health and disease management, business strategy, plus 3 masterclasses on AI project implementation, future industry trends, and workflow automation. It requires no prior programming experience.

PRE-WORK - HISTORY OF AI IN HEALTHCARE

Participants will explore the evolution of AI in healthcare and its impact on medical practices. They will gain insights into AI's applications in radiology, precision medicine, and surgery, along with its role in enhancing diagnostics and patient care. Additionally, they will utilize predictive analytics to understand AI’s effectiveness in managing patient risks and examine ethical considerations to promote responsible AI adoption.

MODULE 1: FOUNDATIONS OF AI FOR HEALTHCARE

Participants in this module will learn the fundamentals of Artificial Intelligence and its core technologies, focusing on their application in healthcare. They will explore the R.O.A.D. Management Framework for AI integration, define algorithms, and differentiate key machine learning models. Additionally, they will evaluate model performance using metrics like accuracy and F1 score and assess the difference between pseudo-innovation and real innovation in healthcare.

WEEK 1: AI IN HEALTHCARE: FOUNDATIONS AND FRAMEWORKS

In this week, participants will explore the core technologies and terminology of Artificial Intelligence (AI) and its role in healthcare delivery. They will also learn about the importance of randomization in AI applications, as well as the reliability and validity of AI interventions. Additionally, the R.O.A.D. Management Framework will be introduced as a strategic tool to guide the effective integration of AI into healthcare systems, ensuring alignment with organizational goals and optimizing AI-driven outcomes.

WEEK 2: UNDERSTANDING THE BUILDING BLOCKS OF AI IN HEALTHCARE

This week, participants will gain an overview of different machine learning algorithms, exploring their core principles and primary use cases. They will define algorithms as step-by-step problem-solving procedures and examine their fundamental components, understanding how they function in various scenarios. Participants will learn to assess machine learning models using key metrics to evaluate their effectiveness. The module will also delve into the distinction between pseudo-innovation and real innovation in healthcare, highlighting the importance of evidence-based evaluation in identifying genuine advancements over passing trends.

MODULE 2: AI FOR INTELLIGENT DECISION SUPPORT

This module explores how AI has the potential to enhance decision-making in healthcare by leveraging predictive modeling, neural networks, and deep learning. Participants will analyze the human baseline concept in AI, risk-based approaches, and psychological factors such as prospect theory and status quo bias. The course also covers AI's role in managing information overload, the potential of Large Language Models (LLMs) in improving healthcare workflows, and the challenges associated with AI biases and human oversight.

WEEK 3: AI FOR CLINICAL DECISION SUPPORT

This week, participants will explore the concept of the human baseline in AI, comparing it to risk-based approaches while considering manual processes and the effects of prospect theory and status quo bias. They will evaluate the role of predictive modeling, neural networks, and deep learning in healthcare, and examine how these techniques can be applied to address healthcare challenges. Participants will evaluate predictive techniques for hospital complications, analyze the effectiveness of these techniques in real-world applications, and explore data-driven approaches for assessing risk factors and outcomes.

WEEK 4: LARGE LANGUAGE MODELS AND THE SHIFT TO AGENTIC AI

This week, participants examine how AI helps manage information overload in medical literature and supports data synthesis in clinical decision-making. They explore how Large Language Models (LLMs) and Agents automate administrative tasks, facilitate patient interactions, and reduce clinician burnout, while also evaluating LLM limitations such as reliability gaps, training-data bias, and the need for human oversight. The week then introduces the shift from reactive to agentic AI, covering goal-directed, multi-step, autonomous action, and how to match workflow types to the right approach. A case study on the move from ambient scribes to agentic documentation workflows shows how reactive tools evolve into agentic systems in practice.

WEEK 5: AUTOMATION, ROBOTICS, AND AGENTIC AI IN HEALTHCARE

This week, participants learn how robotic-assisted surgery uses automation to improve surgical precision and outcomes, examining current technologies, benefits, risks, and emerging trends. They also explore Robotic Process Automation and how it streamlines repetitive tasks and reduces errors in clinical settings. Using a structured evidence rubric, participants evaluate agentic deployments against accuracy thresholds, select appropriate oversight tiers, and work through the human-in-the-loop and human-on-the-loop spectrum, along with common failure modes and regulatory fit. A case study evaluating an agentic prior authorization deployment applies these frameworks to a real-world scenario.

MODULE 3: AI FOR POPULATION HEALTH AND DISEASE MANAGEMENT

Upon completion of this module, participants will analyze graph analytics related to comorbidity, identifying risk factors and social influences on health interactions for improved patient management. They will explore the cultural impacts on medication adherence and their implications for public health interventions. Participants will apply epidemiological models, such as Markov models and the SEIR framework, to assess disease spread and the effectiveness of AI tools during pandemics. Additionally, they will examine AI's role in precision medicine to enhance health screening, treatment protocols, and early disease detection, optimizing preventive healthcare strategies and improving patient outcomes.

WEEK 6: AI FOR IMPROVED HEALTH OUTCOMES

In this week, participants will analyze comorbidity data to identify key risk factors and assess how social and cultural influences impact health interactions and medication adherence. They will explore graph analytics and epidemiological models, such as Markov and SEIR, to understand disease spread and its impact on public health. The week will also cover the role of AI in enhancing health outcomes, especially in the context of pandemics, where AI tools can be used to improve response strategies. Participants will gain insights into how AI-driven approaches can identify risk factors, inform public health interventions, and support disease spread analysis for better management of health crises.

WEEK 7: LEARNING BREAK

WEEK 8: DESIGNING PREVENTIVE HEALTHCARE STRATEGIES

In this week, participants will analyze AI's role in precision medicine, focusing on how it has the potential to enhance health screening processes and personalized treatment protocols to improve patient outcomes. They will explore predictive models in healthcare, learning how these models can aid in early disease identification, leading to more effective preventive healthcare strategies. By assessing how AI can contribute to disease burden reduction and optimize treatment paths, participants will understand how AI can drive improvements in both individual patient care and broader healthcare practices.

MODULE 4: AI BUSINESS STRATEGY FOR HEALTHCARE

Upon completion of this module, participants will understand the R.O.A.D. Management Framework for AI integration in healthcare and identify common pitfalls in AI projects, proposing risk mitigation strategies. They will analyze ethical, regulatory, and privacy challenges, along with best practices for managing datasets in Electronic Health Records (EHRs). Participants will assess healthcare leadership styles and their impact on AI adoption, explore social network strategies for organizational change, and evaluate methods for scaling AI pilot projects to full hospital implementation. Lastly, they will investigate career paths in AI within healthcare, identify essential skills, and understand AI's role in optimizing pharmaceuticals and medical devices to enhance efficacy and patient safety.

WEEK 9: HEALTH DATA, ETHICS, AND AGENTIC AI GOVERNANCE

This week, participants analyze the ethical, regulatory, and privacy challenges of AI in healthcare, focusing on fairness, equitable access to AI-driven solutions, and robust patient data protection. They examine how to export Electronic Health Records (EHRs) and apply best practices for data cleaning, management, and integrating AI models into existing EHR systems. The week also covers accountability in multi-agent workflows, including how agents operate across EHR, payer, and clinical systems, the audit trails these systems require, and the safeguards needed to protect PHI. Participants consider why agentic AI regulation is still forming and why it remains hard to solve. A case study on governing multi-agent AI across healthcare systems applies these principles to a real-world scenario.

WEEK 10: CHANGE MANAGEMENT AND THE SHIFT TO AGENT SUPERVISION

This week, participants distinguish between formal and informal leadership styles in healthcare and evaluate how each shapes team dynamics, decision-making, and AI implementation. They explore social network-based approaches to change management, examining how interpersonal connections drive AI adoption, and evaluate strategies for scaling AI pilots across health systems, including the challenges of hospital-wide rollout and best practices for sustainable adoption. The week also covers the workforce shift from task execution to agent supervision, including trust-building in human-agent teams, override protocols, and monitoring the performance of deployed agents. A case study on this transition guides participants in developing a practical care team transition plan.

MASTERCLASS 1: AI PROJECT MANAGEMENT AND DESIGN

Participants will understand the R.O.A.D. Management Framework for integrating AI in healthcare and explore key components essential for successful implementation. The session covers strategic AI integration, common pitfalls in AI projects, and the impact of data issues on success of AI projects. Participants will examine the role of stakeholder engagement, the influence of change management, and factors that contribute to successful AI project outcomes. Additionally, the masterclass will provide mitigation strategies to address risks and challenges, increasing the probability of the effective deployment of AI-driven solutions in healthcare.

MASTERCLASS 2: FUTURE TRENDS IN AI AND HEALTHCARE

Participants will explore career paths in AI within the healthcare sector, by developing the skills and competencies required for success. The session will cover strategies for personal career advancement and the growing role of AI in pharmaceuticals and medical devices. They will assess how AI has the potential to enhance efficacy, reduce time-to-market for new treatments, and improve patient safety. Additionally, the masterclass will teach the specific skills to determine whether AI-driven solutions truly optimize medical devices and provide greater precision and reliability in healthcare applications.

MASTERCLASS 3: AI-POWERED PRE-VISIT CLINICAL RISK ASSESSMENT USING n8n

Participants will understand how AI can streamline pre-visit clinical workflows by organizing unstructured patient data, identifying early risk patterns, and improving clinical efficiency. The session covers the challenges of fragmented clinical data, fundamentals of AI-driven pre-visit risk assessment, and core principles such as structured data, visibility, and decision support. Participants will also explore workflow automation using no-code tools like n8n, including an end-to-end patient intake to pre-visit summary pipeline. Additionally, the masterclass examines NLP and AI techniques for clinical data organization, rule-based risk detection, and patient prioritization. It will also highlight the impact of AI-powered pre-visit assessments on patient safety, operational efficiency, and future healthcare innovations, while emphasizing how intelligent workflows can support clinician decision-making.

Note: The curriculum listed above are indicative and subject to updates as technology evolves.

What case studies will you solve?

Work on healthcare AI case studies covering disease prediction, diagnostics, and patient care

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Sample Case Study

AI-Assisted COVID-19 Detection Using Chest X-Rays

Description

Explore how AI-assisted chest X-rays addressed global testing bottlenecks during COVID-19, enabling rapid, scalable screening while uncovering challenges of explainability, bias, and Integration.

Skills you will learn

  • Medical image analysis
  • AI model evaluation (DeepCOVID-XR
  • COVID-Net
  • Qure.ai)
  • Explainability methods (Grad-CAM)
  • Ethical and responsible AI adoption
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Sample Case Study

From Ambient Scribes to Agentic Documentation Workflows

Description

Explore how documentation AI is evolving from passive ambient scribes that transcribe encounters into agentic workflows that draft notes, place orders, and route tasks, while examining accuracy thresholds, human oversight, and safe rollout.

Skills you will learn

  • Agentic workflow design
  • ambient documentation
  • human oversight tiers
  • deployment risk evaluation
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Sample Case Study

Evaluating an Agentic Prior Authorization Tool

Description

Learn how to assess a real agentic prior authorization system using a structured evidence rubric, weighing accuracy thresholds, oversight tier, failure modes, and regulatory fit before deployment.

Skills you will learn

  • Structured evidence rubric
  • agentic use case evaluation
  • oversight tier selection
  • regulatory fit assessment
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Sample Case Study

Governing Multi-Agent AI Across Healthcare Systems

Description

Understand how to govern autonomous agents acting across EHR, payer, and clinical systems, covering accountability, audit trails, PHI protection, and why agentic AI regulation is still forming.

Skills you will learn

  • Multi-agent governance
  • audit trail design
  • PHI protection
  • agentic accountability frameworks
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Sample Case Study

From Task Execution to Agent Supervision

Description

Explore how care teams shift from executing tasks to supervising agents, covering trust-building in human-agent teams, override protocols, and performance monitoring of deployed agents.

Skills you will learn

  • Agent supervision
  • human-agent trust
  • override protocol design
  • deployed agent monitoring

Note: The projects listed above are indicative and subject to updates to the curriculum.

Earn a Professional Certificate from Johns Hopkins University

Stand out in a competitive market with a Certificate of Completion in AI in Healthcare that formally recognizes the expertise developed through rigorous, practical assessments.

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    Biomedical Leadership

    #1 Biomedical Engineering Program by U.S. News & World Report, 2026

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    National Recognition

    #7 National University by U.S. News & World Report, 2026

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    Global Standing

    #14 Best Global University by U.S. News & World Report, 2026

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* Image for illustration only. Certificate subject to change.

Who are the faculty for the program?

Learn from renowned JHU faculty and build the expertise to evaluate and implement AI in healthcare settings.

  • Dr. Ian McCulloh

    Dr. Ian McCulloh

    Manager of Artificial Intelligence Executive and Professional Education, Johns Hopkins University

    Served as Chief Data Science and MD of AI, Accenture Federal Services

    Author of three books and over 100 peer-reviewed papers

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  • Daniel Byrne

    Daniel Byrne

    Award-winning author, Teacher, and Faculty member, Johns Hopkins University

    40+ years in AI, predictive modeling, and healthcare.

    Published 165+ scientific papers on AI and patient outcomes

    Know More
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Who are the mentors for weekly live sessions?

Learn from seasoned AI healthcare mentors to apply concepts and build practical skills.

  •  G Anthony Reina  - Mentor

    G Anthony Reina linkin icon

    Head of Machine Learning, Stealth BioTech Startup
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  •  Sharath M S  - Mentor

    Sharath M S linkin icon

    Staff AI Engineer, GE HealthCare
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  •  Rishov Chatterjee  - Mentor

    Rishov Chatterjee linkin icon

    Director of Data Science and AI, Integra Connect
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Note: The mentors listed above are indicative and subject to change based on availability and scheduling.

Course Fees

The program fee is USD 2,990

Lead AI Innovation in Healthcare

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    Build expertise in applying AI to clinical decision support and predictive analytics for better patient care.

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    Apply the R.O.A.D. Management Framework to align AI initiatives with organizational clinical goals

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    Learn from healthcare AI practitioners through live online sessions focused on industry applications.

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    Receive a Certificate of Completion and 6 CEUs from Johns Hopkins University.

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Application Closes: 30th Jul 2026

Application Closes: 30th Jul 2026

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Admission Process

Admissions close once the required number of participants enroll. Apply early to secure your spot

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    1. APPLY

    Fill out an online application form.

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    2. REVIEW

    Your application will be reviewed by a panel from Great Learning to determine if it is a fit with the program

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    3. JOIN PROGRAM

    After a final review, you will receive an offer for a seat in the upcoming cohort of the program.

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Delivered in Collaboration with:

Johns Hopkins University is collaborating with online education provider Great Learning to offer the AI and Agentic AI in Healthcare program. Great Learning is a professional learning company with a global footprint in 14+ countries. Its mission is to make professionals around the globe proficient and future-ready. This program leverages JHU's leadership in innovation, science, engineering, and technical disciplines developed over years of research, teaching, and practice. Great Learning manages the enrollments and provides industry experts, student counselors, course support and guidance to ensure students get live personalized mentorship on the application of concepts taught by the JHU faculty.

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