Loading...

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

Microcredential(s)

This course applies toward the Bioinformatics Endeavor digital badge.

Prerequisites

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

REFUND
Follow the link to review FAES Tuition Refund Policy.

Loading...
To Register Click on "Add to Cart"
Section Title
Introduction to Data Science
Type
Online Asynchronous
Dates
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)
Instructor(s)
Required fields are indicated by .