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Course Description

Bundle Registration! Save a bundle and receive 30% off the individual course rate when you register for both BIOF 309 and BIOF 475!

Unlocking insights and making informed predictions from data is a fundamental pillar of modern science. In this course, you will be introduced to essential tools and workflows for effective data analysis, with a special emphasis on biological and health-related datasets. You’ll gain hands-on experience with powerful Python-based tools such as Numpy, Scikit-learn, Pandas, and Matplotlib, equipping you with the skills to tackle real-world data challenges and apply these techniques across a range of scientific fields.

Please note that this course, along with many of the accompanying materials, was originally developed by Dr. Patrick McClure, Kiersten Campbell, and Dr. Yuan-Chiao Lu.

Learner Outcomes

When you complete the course successfully, you will be able to:

  • Load and preprocess data for various analyses, including text-based datasets
  • Summarize and describe key characteristics of datasets using statistical measures and data visualizations
  • Apply both supervised (e.g., linear, logistic, and multinomial regression) and unsupervised (e.g., clustering) machine learning techniques
  • Evaluate and compare the performance of different machine learning models
  • Assess model fit and propose strategies for improving model accuracy
  • Clearly communicate the findings from comprehensive data science analyses

Microcredential(s)

This course applies toward the Bioinformatics Endeavor digital badge.

What FAES Learners are Saying

"I was a fan of the lecture to lab format, which made the cross from theoretical and practical material seamless. I also appreciated the weekly discussions, which promoted thinking in a practical sense about how the material we learned can be applied in real life." - FAES Learner
 

Textbook Information

A textbook is available for this course. 

Click here to view a textbook list for FAES courses and purchasing information. Please note that tuition does not include textbooks.

Prerequisites

Foundational understanding of statistics, probability, algebra, and calculus. 
BIOF 309 or previous programming experience suggested.

Additional Information

This course is for learners with introductory knowledge to programming who want to learn more about data science and how to apply data science to their research, especially focused on biological datasets. 

Refund 
Follow the link to review FAES Tuition Refund Policy. 

Scholarship and Funding

Are you a self-funded student? FAES offers scholarship options. Click here for more information and to apply. 

Looking for resources to help you acquire funding for your continued education? Click here for our funding justification guide. 

Photo Release

By registering for this event, you agree to allow FAES to take photographs of you during the event and to use these photos for promotional purposes, including on our website, social media, and marketing materials, without further compensation. You understand that you have no right to review or approve the final use of these images.

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To Register Click on "Add to Cart"

Section Title
Introduction to Data Science for Biomedical Scientists
Type
Online Asynchronous
Dates
Mar 25, 2026 to May 12, 2026
Total Cost (Includes $75 non-refundable technology fee per course when applicable)
Eligible Discounts Can Be Applied at Checkout (2 Credits) $775.00
Potential Discount(s)
Available for Academic Credit
2 Credit(s)
Instructor(s)
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