BIOF 475
Introduction to Data Science

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.

Prerequisites

Previous programming experience is not required, but is recommended.

Learning Objectives:

  • Load and clean data
  • Choose what type of model (e.g. supervised or unsupervised) to use based on the questions being asked of the data
  • Build and validate the chosen model
  • Visualize and explain what that model learned from the data

Overview

Program

Class Type

Graduate Course

Credits

2

Availability

Fall 2021

Session

Session A

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