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Get Certified for AI/ML and Bioinformatics in Precision Medicine

Spend as short as 3-5 weeks to get certified by the Department of Genetics at Stanford University. The self-paced courses are hosted on an education platform for Precision Medicine, Stanford Data Ocean. 

The platform currently offers two STAP-eligible certificate programs:

  1. Fundamentals of Data Science in Precision Medicine and Cloud Computing
  2. Fundamentals of AI/ML in Precision Medicine

To learn more about the certificate programs, please visit https://deepdata.stanford.edu/certificate-programs/.

Prerequisites:
This program welcomes individuals from all educational backgrounds. No prerequisites are required. 


Certificate for Fundamentals of Data Science in Precision Medicine and Cloud Computing

Research Ethics:

  • Get Started with Research Ethics

Programming:

  • Introduction to Python
  • Introduction to R Programming
  • Introduction to Pandas

Data Analysis:

  • Introduction to Statistics I
  • Introduction to Data Visualization

Cloud Computing:

  • Introduction to Cloud Computing

Bioinformatics

  • Introduction to Genomics
  • Introduction to Epigenetics
  • Introduction to Statistics II
  • Introduction to Transcriptomics
  • Introduction to Proteomics
  • Introduction to Microbiome
  • Introduction to Metabolomics
  • Introduction to Clinical Data
  • Introduction to AI/ML
  • Introduction to Imaging Data
  • Introduction to Wearable Data 
  • Introduction to Exposome

 

 


Certificate for Fundamentals of AI/ML in Precision Medicine

Research Ethics:

  • Get Started with Research Ethics

Bioinformatics

  • Introduction to Genomics
  • Introduction to Epigenetics
  • Introduction to Transcriptomics
  • Introduction to Proteomics
  • Introduction to Clinical Data
  • Introduction to Wearable Data 
  • Introduction to Imaging Data

AI/ML Fundamentals

  • Introduction to AI/ML
  • Introduction to Supervised Learning
  • Introduction to Unsupervised Learning
  • Introduction to Neural Networks
  • Introduction to Deep Learning
  • Introduction to Large Language Models

 

 


Bonuses for getting the certificate courses

Hands-on practice in Jupyter notebook:
You will learn via real-world examples and interact with public health datasets with Jupyter Notebooks at your own pace. Each module can be completed within one hour, so you could make small progress that fits into your busy schedule. 

Most students who join the weekly study group can finish the certificate in 3-5 weeks.

Get support from AI Tutor:
You will have access to our AI Tutor to get your questions resolved on demand. The AI Tutor is designed to answer questions specifically in Precision medicine. Our students use it to write code and troubleshoot code, and find it effectively supports their learning needs.

Join weekly live study group & office hour:
Each week, you have the option to join your peers and instructors on Zoom to study together. Most students use this dedicated time to ask instructors questions, or create momentum to finish the certificate.

Financial support:

If you have enrolled or plan to enroll in our signature Precision Medicine Fundamentals certificate program, please contact our team at sdo-support@stanford.edu to receive a discount code that gives you a 30% discount on the AI/ML certificate program’s tuition.

We offer scholarships to families with incomes making less than $70,000 USD annually. Apply for the scholarship now, and our team will send the next steps within 3 business days.


Custom training workshops are available for this program

Technology training sessions structured around individual or group learning objectives. Learn more about custom training


University IT Technology Training sessions are available to a wide range of participants, including Stanford University staff, faculty, students, and employees of Stanford Hospitals & Clinics, such as Stanford Health Care, Stanford Health Care Tri-Valley, Stanford Medicine Partners, and Stanford Medicine Children's Health.

Additionally, some of these programs are open to interested individuals not affiliated with Stanford, allowing for broader community engagement and learning opportunities.