Course Description

General overview of industry standard machine learning techniques and algorithms. Focus on machine learning model building and optimization, real-world applications, and future directions in the field. Hands-on experience with modern data science packages.

Credit(s)

3.0

Prerequisite

IST 687, OR IST 387 with a minimum grade of B or higher.  Exceptions maybe given to students who have acquired skills equivalent to what is taught in IST 687. 

Professor of Record

Stephen Wallace

Audience

Undergraduate students (407) & Graduate students (707)

Learning Objectives

After taking this course, students will be able to: 

  1. Document, analyze, and translate data analytics needs into technical designs and solutions.
  2. Apply data analytics concepts, algorithms, and evaluation methods to real-world problems.
  3. Employ data storytelling and dive into the data, find useful patterns, and articulate what patterns have been found, how they are found, and why they are valuable and

Course Syllabus

IST 407/707 Fall 2021 Syllabus - Joshua Introne

IST 407/707 Spring 2021 Syllabus - Yang Yang


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