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Table of Contents minLevel 2
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Course Description
Introduction to data analytics techniques, familiarity with particular real- world applications, challenges involved in these General overview of industry standard machine learning techniques and algorithms. Focus on machine learning model building and optimization, real-world applications, and future directions of in the field. Hands-on experience with open-source software 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:
- Document, analyze, and translate data mining analytics needs into technical designs and solutions.
- Apply data mining analytics concepts, algorithms, and evaluation methods to real-world problems.
- 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 trustworthy.
Course Syllabus
IST 407/707 Fall 2021 Syllabus - Joshua Introne
IST 407/707 Spring 2021 Syllabus - Yang Yang
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