BIOF 475 - Introduction to Data Science for Biomedical Sciences

Course Description

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Analyzing data to generate meaningful insights and make informed predictions is fundamental to modern scientific research. This course aims to equip students with essential tools and workflows for data analysis, with a specific emphasis on biological and health-related datasets. Throughout the course, students will gain proficiency in Python-based tools, including Numpy, Scikit-learn, Pandas, and Matplotlib.

Learner Outcomes

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

Load and prepare data for downstream analysis, including text-based data
Describe features of the input dataset via summary statistics and informative data visualizations
Utilize supervised (ex: linear, logistic, and multinomial regression) and unsupervised (ex: clustering) machine learning approaches
Compare & contrast different machine learning models
Critique model fit and suggest methods for refining model accuracy
Effectively communicate the results of an in-depth data science analysis

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

There is no textbook for purchase required for this course.

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
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Scholarship and Funding

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