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:
- Translate a business challenge into an analytics challenge.
- Use linear and logistic regression, decision trees, and neural networks to make predictions.
- Use data science to gain actionable insights.
- Use Python and Apache Spark to build big data analytics pipelines.
- Learn classic and state of the art machine learning techniques.
- Explain how advanced analytics can be leveraged to create a competitive advantage.
Course Syllabi
IST 718 Spring 2021 Syllabus - Daniel Acuna
IST 718 Spring 2021 Syllabus- Yang Yang