Harvard University · edX
Fundamentals of TinyML
4.567 ratingsFocusing on the basics of machine learning and embedded systems, such as smartphones, this course will introduce you to the “language” of TinyML.

Understanding how clinical studies are designed, conducted ethically, and aligned with Good Clinical Practice (GCP) is essential for every Clinical Research Coordinator. This course equips you with the knowledge and practical skills to interpret study designs, apply research ethics, and follow GCP in daily site operations.
You'll learn to frame research questions and hypotheses, distinguish key study design elements such as endpoints and eligibility criteria, apply ethical principles to participant interactions, and implement GCP-aligned practices for protocol compliance and source documentation.
Using Microsoft Word, Excel, and PowerPoint, you'll create study design documents, methodology analyses, ethics case studies, and GCP compliance checklists that reflect real-world coordinator responsibilities.
This course is for learners who understand clinical research fundamentals, regulatory compliance obligations, and IRB oversight functions and are ready to understand the intellectual, ethical, and procedural architecture behind clinical studies.
This module teaches you how broad clinical problems are translated into measurable research questions, testable hypotheses, and specific study objectives. You'll annotate a set of provided research questions in Microsoft Word to identify how each component is framed in a sample clinical study. Unde…
This module teaches you to distinguish between observational and interventional study approaches by analyzing how investigator involvement and data collection intent differ between them. You'll build a side-by-side comparison table in Microsoft Excel using real study examples to classify each appro…
This module introduces the core methodology elements that compose a clinical study design—endpoints, variables, eligibility criteria, and visit schedules—and teaches you to differentiate their roles. You'll organize these elements into a structured diagram in Microsoft Excel using a sample study pr…
This module teaches you to critically evaluate whether a study's chosen endpoint and data-collection approach actually align with its stated objective. You'll review a sample protocol summary in Microsoft Word and provide structured written feedback on alignment strengths and gaps. This analytical…
This module introduces the three foundational ethical principles from the Belmont Report—respect for persons, beneficence, and justice—and explains how they apply to recruitment, consent, confidentiality, and fair participant treatment. You'll complete a structured ethics presentation template in M…
This module teaches you to recognize the common ethical risks that arise in clinical studies—situations that may compromise participant autonomy or welfare. You will review a clinical trial case study in Microsoft Word and identify potential ethical risks using a structured documentation template.…
This module explains why GCP exists, what it protects, and how its principles create a universal standard for clinical trial conduct. You'll complete a structured GCP summary template in Microsoft Word that explains how GCP applies to a simulated study environment. Understanding GCP's purpose gives…
This module teaches you to apply GCP principles directly to the routine tasks coordinators perform: checking protocol compliance, ensuring source document accuracy, and documenting activities contemporaneously. You'll use a GCP compliance checklist in Microsoft Excel during a simulated site visit t…
This project integrates research framing, methodology analysis, ethics assessment, and GCP verification into a cohesive study design and compliance framework for a simulated Phase II immunotherapy trial. You'll produce a research framework annotation, methodology alignment assessment, ethics risk d…
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Microsoft is in Tier 2: excellent universities and the companies that build the technology of our institution ranking (86/100).
Harvard University · edX
Focusing on the basics of machine learning and embedded systems, such as smartphones, this course will introduce you to the “language” of TinyML.
Harvard University · edX
Learn the concepts and techniques that make up the foundation of data science and machine learning.
Harvard University · edX
An introduction to the intellectual enterprises of computer science and the art of programming.
Harvard University · edX
Build a movie recommendation system and learn the science behind one of the most popular and successful data science techniques.
Massachusetts Institute of Technology · YouTube
Instructors: David Sontag, Peter Szolovits View the complete course: https://ocw.mit.edu/6-S897S19 Introduces students to machine learning in healthcare, the nature of clinical data, and the use of machine learning for risk stratification, disease progression modeling, precision medicine, diagnosis…
Harvard University · edX
An introduction to programming using Python, a popular language for general-purpose programming, data science, web programming, and more.