Course Description

A broad introduction to analytical processing tools and techniques for information professionals. Students will develop a portfolio of resources, demonstrations, recipes, and examples of various analytical techniques.




IST687 or IST387 with a minimum grade of a B or higher.  Familiarity with command-line interfaces, quantitative skills including statistics, a basic knowledge of linear algebra, basic probability, basic statistics, basic calculus, strong algebra skills, and strong programming skills in Python or some other language. Please refer to 

Professor of Record

Daniel Acuna


Graduate Students

Learning Objectives

After taking this course, students will be able to:

  1. Translate a business challenge into an analytics challenge.  
  2. Use linear and logistic regression, decision trees, and neural networks to make predictions.   
  3. Use data science to gain actionable insights.  
  4. Use Python and Apache Spark to build big data analytics pipelines.  
  5. Learn classic and state of the art machine learning techniques. 
  6. Explain how advanced analytics can be leveraged to create a competitive advantage.   

Course Syllabi

IST 718 Fall 2021 Syllabus - Daniel Acuna

IST 718 Fall 2021 Syllabus- Yang Yang

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