School of Information Studies
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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.

Credit(s)

3.0

Prerequisite/Co-requisite

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. This is not an introductory course; most students who take this course have already taken IST 687 Introduction to Data Science. Please refer to https://acuna.io/teaching/IST718 

Professor of Record

Daniel Acuna

Audience


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 2020 Syllabus- Khan, Humayun

IST 718 Fall 2020 Syllabus- Williamson, Willard


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