Course Description

Learning from data in order to make useful predictions or obtain insights is a cornerstone of modern science. The goal of this course is to introduce students to the basic tools and workflows for doing this, with a focus on biological- and health-related data. In this course, students will learn how to use Python-based tools, particularly 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


This course applies toward the Bioinformatics Endeavor digital badge.


There are no prerequisites for this course, but some programming experience is helpful.

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Section Title
Introduction to Data Science
Online Asynchronous
Oct 25, 2023 to Dec 12, 2023
Total Cost (Includes $75 non-refundable technology fee per course)
Eligible Discounts Can Be Applied at Checkout (2 Credits) $775.00
Potential Discount(s)
Available for Academic Credit
2 Credit(s)
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