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. Students will learn 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

Based on their focus during the course, students completing this course will understand some of the following concepts and be able to apply these in various business contexts and through hands-on exercises:

Part 2: Analytics

Based on their focus during the course, students completing this course will understand some of the following concepts and be able to automate these concepts in various business contexts and through hands-on exercises:

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.

Helps – Download Examples for Textbook

Downloads for Exercises done in the course can be found on this site (scroll down to the bottom and select the Downloads tab): Data Smart Book Site

Depth of Material Covered

From the reading, students will gain familiarity with some of the most common data science techniques that continue to see increased use out in industry. The BI/Analytics project and Research project will allow the students to gain further mastery and knowledge in some of the tools/techniques read about in the textbook that the course did not have time to cover and practice more in depth. These projects could also be used to look at other tools/techniques related to business intelligence, data analytics, or data science that the course did not have time to cover and practice. There is also option during the reading of week 11-13 to research further into techniques and practice them.

Textbooks to Consider for Further Knowledge

An Introduction to Statistical Learning with Applications in R By: Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani A free PDF copy of this book is provided by the author on his webpage: http://www-bcf.usc.edu/~gareth/ISL/ (Select “Download the book PDF” towards the top right of the page). Additionally, from the author’s page you can find reference for information about their other book, The Elements of Statistical Learning. This goes over even more depth some of the material covered in this course and the Introduction to Statistical Learning book. Note the Data Smart book read in the class references looking at these books above for further depth on some of the concepts/techniques presented in the reading.

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 2—6

*BI/Analytics project = Weeks 7—10

*Research project = Weeks 7—12

*Game Theory/AI project = Weeks 10—14

*Note

The last 3 projects do have weeks that overlap with each other. However there are no assignments during that time except the reading and gospel integration discussions. Make sure to plan well and submit early.

The Game Theory/AI Project will involve some programming so it would be good to be comfortable with the idea of having basic familiarity with R or Python by the end of the course. There will be opportunity to learn R towards the end of the book being read. Additionally, you can focus on learning R or Python skills during the BI/Analytics or Research project. Weeks 11-13 during the Game Theory/AI Project will allow you the freedom to explore additional material to help you in the assignment if needed.

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. 

The instructor for each section will create gospel integration discussions during the semester.

Total Points Available = 840 points

Homework

There will be several exercise assignments available to help you strengthen your skills in using Microsoft Excel, 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.

This course helps encourage this by having many of the assignments require responding to another post. In addition there are some assignments which require a group to complete.

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)  Please make sure if you use other sources in your work that you clearly attribute the source and make clear what you used in your work. Be clear up front instead of waiting for the instructor to grow suspicious about what sources you used and what was your contributed work.

CIT 381 Learning Model

Learning Model