CTS 2450 - Introduction to Business Intelligence

College of Computer & Information Technology

Credit(s): 3
Contact Hours: 47
Effective Term Spring 2021 (585)

Requisites

Prerequisite CTS 2433 with a minimum grade of C and
Prerequisite CTS 2417 with a minimum grade of C

Course Description

This course provides the fundamental concepts and methods needed to understand the emerging role of business intelligence and analytics in organizations. The student will learn how to apply basic business intelligence analytical methods including descriptive and predictive techniques.

Learning Outcomes and Objectives

  1. The student will interpret the value of business information for organizational decision making by:
    1. describing the differences in data.
    2. identifying differences in measures of location.
    3. computing measures of location.
    4. identifying differences in measures of variability.
    5. computing measures of variability.
    6. analyzing numerical distributions.
  2. The student will identify and apply algorithms for predictive analytics by:
    1. using probability to model uncertainty.
    2. comparing and contrasting different types of probability distributions.
    3. describing linear regression, least squares method, and model fitting.
    4. using linear regression to make predictions.
  3. The student will identify and apply algorithms for descriptive analytics by:
    1. describing how sample size affects the analysis results.
    2. applying point estimation techniques.
    3. developing null and alternative hypothesis.

Criteria Performance Standard

Upon successful completion of the course the student will, with a minimum of 70% accuracy, demonstrate mastery of each of the above stated objectives through classroom measures developed by individual course instructors.

History of Changes

C&I Approval: 07/26/2018, BOT Approval: 09/18/2018, Effective Term: Spring 2019 (555).
C&I Approval: , BOT Approval: , Effective Term: Spring 2021 (585)

Related Programs

  1. Business Analyst Specialist (ANLST-ATC) (625) (Active)
  2. Data Science (DATSCI-AS) (640) (Active)