Syllabus

Course Description

Part 1: Business Intelligence

This course provides an introduction to Business Intelligence, including the processes, methodologies, infrastructure, and current practices used to transform business data into useful information and support business decision making. Business Intelligence requires foundation knowledge in data storage retrieval; thus this course will review logical data models for both database management systems and data warehouses. Students will learn to extract and manipulate data from these systems and assess security related issues. Data mining, visualization, and statistical analysis along with reporting options such as management dashboards. 

Part 2: Analytics

This course provides an introduction to Analytics, or the automation of analysis. This course also includes an overview of qualitative and quantitative analysis methods and methods used to automate these processes for speed, interactivity, and quality (reliability and validity). Several examples of modern types of analytics, such as descriptive, diagnostic, discovery, predictive, and prescriptive approaches, will be introduced and explored.

Course Goals

Part 1: Business Intelligence

Students completing this course will understand the following concepts and be able to apply these in various business contexts and through hands-on exercises with leading software applications:

Part 2: Analytics

Students completing this course will understand the following concepts and be able to automate these concepts in various business contexts and through hands-on exercises with leading software applications:

Textbook

Data Smart: Using Data Science to Transform Information into Insight

By: John W. Foreman Publisher: John Wiley & Sons Pub. Date: November 4, 2013 Print ISBN: 978-1-118-66146-8

NOTE: An electronic version is available (free) for BYU-I students.

Compare prices for your textbooks through the University Store Price Comparison site. They will show you all of the options from the University Store plus several online options to help you find the best price.

Project-Based Learning

This course is a project-based learning course. You will complete four projects during the semester. Project-based learning does not end up at a predetermined outcome or take predetermined paths. During the semester you will work relatively autonomously to design, solve problems, make decisions, investigate, and create real products. In a project-based learning course, projects are the curriculum, not supplementary activities.

Characteristic of Project-Based Learning

Instructors play a slightly different role in a project-based learning course. Instructors do not direct the learning. Instead, Instructors are knowledgeable guides, facilitators, or mentors. Your Instructor will not organize or lead your project nor tell you what you must learn, but will help you discover profitable paths, set reasonable scope, and help you find resources, strategies, tools, and information. Individually, you complete a spreadsheet project and a research project. Working with a team, you will complete a BI/Analytics project. Finally, you may work individually or self-select a team to create a Game Theory/AI project.

Spreadsheet project = Weeks 1—6

*BI/Analytics project = Weeks 7—10

*Research project = Weeks 7—12

*Game Theory/AI project = Weeks 10—14

*Note

The research project may overlap both the BI/Analytics project and the Game Theory/AI project. You control the schedule for the Research project. Plan well. Submit early!

Grading

From the Grading System section (pg. 48) of the course catalog, “Grades are determined by each instructor based upon an evaluation of all assigned and completed course work. Classroom/laboratory participation, mastery of subject matter, and promise of continuing success in sequential courses in related fields are all criteria used to evaluate progress.”

Your grade is your responsibility as well as your Instructor's. The Grade Center in I-Learn can be used to gauge your progress throughout the semester. Please monitor the Grade Center on an ongoing basis and contact me as soon as possible if any errors arise. You will have one week from the date any grade is posted to clarify any confusion over a grade. The day after the semester ends is too late for corrections for any grades awarded prior to the end of the semester.

This course is graded on a point system based on assignments, projects, and participation. Point values will be reflected in I-Learn for feedback and tracking purposes. 

*Your section may not participate in these discussions. Contact your instructor for points possible in this category.

Homework

There will be several exercise assignments available to help you strengthen your skills in using Microsoft Excel, databases, data modeling, regression analysis, programming, etc.

Teach One Another

Each student will be expected to independently research, learn about, and then teach the class about topics relating to specific assignments and more general topics in Business Intelligence and Analytics.

Late Work Policy

Academic Dishonesty

Cheating in any form is unacceptable conduct. “For what shall it profit a man, if he shall again the whole world, and lose his own soul?” (Mark 8:36) 

CIT 381 Learning Model

Learning Model