Table of Contents minLevel 2
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.
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
Undergraduate students (407) & Graduate students (707)
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.
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