IS 470: BUSINESS INTELLIGENCE Syllabus

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1 sample IS 470: BUSINESS INTELLIGENCE Syllabus IS 470 Business Intelligence Instructor: Vasinee Opland Class hours: Monday 4:00-6:45 PM Classroom: CBA-237B Office CBA-455 Office Phone: TBD Office hours: Monday 3:30 PM-4:00 PM & 9:45 PM-10:15 PM sample Textbooks: (Required) Sharda, R., Delen, D., Turban, E., Aronson, J., Liang, T. P. (2015). Business Intelligence and Analytics: Systems for Decision Support, 10 th Edition, Pearson, ISBN-13: Course Description: Most organizations are data rich and information poor. For instance, Wal-Mart captured 20 million transactions per day in The rate at which data has been accumulating has only increased since, with newer sources like social networks and RFID. However, the complexity of modern information systems, along with the heavy volume of the data accumulated, present a huge barrier for business managers, who need to consume the information and make strategic/operation decisions. The primary objective of this course is to introduce you to various techniques available to extract useful information from the large volumes of data an organization might possess. At the end of the semester, you will not only appreciate the substantial opportunities that exist in the BI realm, but also learn techniques that will allow you to exploit these opportunities. The course will cover general concepts in the BI field, along with many popular data mining techniques such as logistic regression, association rules, and decision trees. The focus will be on how the techniques are to be used, and the details of the methodologies will be covered only to the extent necessary to understand when and how each technique can be used. Students will also gain experience using BI/DM software such as SPSS, MS PowerPivot, and Tableau. Learning Objectives: 1) To gain a general understanding of business intelligence/data mining and to appreciate the data rich environment of today s global economy. 2) To gain a practical understanding of popular data mining methodologies. 3) To be able to design BI analyses/solutions to answer business questions using commercial BI packages. 4) To gain an understanding of when to use which data mining technique. 5) To become aware of some current trends in business intelligence, data mining, data visualization.

2 Grading: Homework/Case Studies 10% Attendance/Class Participation 10% Group Project 25% Mid-Term Exams 25% Final Exam 30% Grades may be viewed via the grading section on BeachBoard. The system will give you your percentage score for each assignment as well as your overall average for all assignments that have been graded. You are required to review your grades online and see me within one week of grades being posted if you would like to review or discuss your grade. I do not negotiate or haggle over final grades your total accrued points solely determine the grade you earn in this course. If you find that you are not doing as well as expected, please make time as early as possible to discuss strategies on how to improve your exam grades. Final course grades will use the following scale: 900 A 800 & < 900 B 700 & < 800 C 600 & < 700 D < 600 F Exams (550 points total) There will be multiple choice and short answer questions. The content of the exams includes everything discussed in class and any other assigned readings and relevant materials distributed in class. I will provide a study guide in advance. Homework/Case Studies (100 points total) To complement the academic skills and knowledge acquired by IS students, there will be several homework, real-world case studies assigned throughout the semester. Assignments that are submitted late may not be graded. Class Participation (100 points total) Attendance is mandatory for this class. Attendance will be randomly checked throughout the semester. Students will get participation points for engaging in class activities and discussions. Students are expected to read textbook chapters indicated in this syllabus BEFORE the class to be able to contribute to class discussion. Group Project (250 points total) To practice written and oral communication, critical thinking, research, and analytical skills, each of you will be assigned to a team responsible for creating a professional business intelligence project using real business analytical techniques. The project has four modules. Each module is worth 50 points and project presentation is worth 50 points. This project will integrate data mining and data visualization skills, and students will present their project in class as if they are presenting to their manager.

3 Course Outline (subject to revision) Week Topic Textbook/Tools Week 1 Introduction to Business Intelligence Read: Sharda Ch 1, 14 1/22/18 - Overview of BI, Business Analytics, and Decision Support Youtube Videos - Emerging trends and future impacts - Job market for BI field Week 2 Foundation and Technologies for Decision Making Read: Sharda Ch 2 1/29/18 - Introduction and definitions - Phases of the decision-making process - Week 2 Case Study: Nationwide Used BI to Enhance Service Week 3 Data Warehousing and SQL Read: Sharda Ch 3 2/5/18 - Data warehousing process Access Database - SQL functions (e.g. COUNT, SUM, CASE, AVERAGE) - Mid-Term Exam 1 Review - Week 2 Case Study due Week 4 2/12/18 Week 5 2/19/18 Mid-Term Exam 1 - Exam: Content from Week Project Module 1 discussion - Hands on in-class SQL exercises Excel in Business (1) - Essential functions in business (e.g. COUNTIFS, SUMIFS, AND, OR, VLOOKUP) - How to use Excel to clean data sets Excel - Pivot Table, Pivot graph Week 6 Excel in Business (2) Excel 2/26/18 - Power Pivot using data from Excel and Access Access Database - Hands on in-class exercise - Project Module 1 due Week 7 Data Visualization Read: Sharda Ch 4 3/5/18 - Introduction to Tableau Tableau - Building interactive dashboards, KPI, and scorecards - Hands on in-class Tableau exercises - Week 7 Case Study: Self-Service Reporting Environment Saves Millions for Corporate Customers - Project Module 2 discussion Week 8 Intro to Data Mining and Predictive Analytics Read: Sharda Ch 5 3/12/18 - Overview of Data Mining process - Supervised vs Unsupervised Learning - Different Types of Data Mining Algorithms - Week 7 Case Study due

4 Week 9 3/19/18 Mid-Term Exam 2 / Regression Analysis - Exam: Content from Week Basic concepts of Regression - Project Module 2 due IS 470 Business Intelligence Last update: 12/31/2017 Powerpoint Handout SPSS Week 10 3/26/18 Spring break No class Week 11 Classification and Regression Tree (CART) Powerpoint Handout 4/2/18 - Basic concepts of Decision Tree SPSS - Project Module 3 discussion Week 12 Neural Network Read: Sharda Ch 6.1, 4/9/18 - Basic concepts of Neural Network 6.2, 6.3 SPSS - Week 11 Case Study: Coors Improve Beer Flavors with Neural Networks Week 13 Association Rule Powerpoint Handout; 4/16/18 - Basic concepts of Association Rule Sharda p Support, Lift, Confidence SPSS - Project Module 3 due - Week 11 Case Study due Week 14 Web Analytics and Social Analytics Read: Shmueli Ch 16, 4/23/18 - Overview of Web Analytics and Web Mining 17, 18 - Social Analytics and Social Network Analysis SPSS - Week 14 Case Study: eharmony Uses Social Media to Help Take the Mystery Out of Online Dating - Project Module 4 discussion Week 15 Big Data Analytics Read: Sharda Ch 13 4/30/18 - Fundamentals of Big Data Analytics Youtube videos - Big Data tools (e.g. Hadoop, NoSQL) - Stream analytics and its application - Case Study Week 14 due Week 16 Project Presentation & Final Exam Review 5/7/18 - Project Module 4 due - Powerpoint Presentation file due Week 17 Final Exam 5/14/18 Content from Week 9-15

5 Last update: 12/31/2017 Excused Absences The University s policy states excused absences are defined as (1) illness or injury to the student; (2) death, injury, or serious illness of an immediate family member or the like; (3) religious reasons (California Education Code section 89320); (4) jury duty or government obligation; and (5) University sanctioned or approved activities. If you cannot make an exam, you must notify me prior to the exam and have a valid excuse (noted above). Please see me for make-up work based on excused absences. If you are going to be tardy or miss a class, there is no need to me in advance check with your classmates to see what you missed. I generally create one make-up exam available at the end of the course (for both the midterm and final). Withdrawal Policy It is your responsibility to withdraw from classes. Instructors have no obligation to withdraw students who do not attend courses. Withdrawal from a course after that date requires the signature of the Dean. You must provide a serious and compelling reason when requesting to withdraw from the course. A W will appear on your transcript. See the schedule of courses for more information. Academic Integrity All students are expected to comply with the CSULB Policy on Academic Integrity. Any student suspected of violating this obligation for any reason during the semester will be subject to the procedural process outlined in the CSULB Catalog. Cheating in any form will not be tolerated. This includes plagiarism. Remember, using a friend s project that was submitted a previous semester is plagiarism. Decisions on repercussions for cheating will be made on a case-by-case basis (i.e., it will depend on the seriousness of the event). Examples include a reduction in grade, receiving an F for a final course grade, or dismissal from the university. Please refer to for more information and guidelines. In nearly all cases, cheating will result in failure of the course. Additionally, no recording devices of any kind (including phones) can be used during exams. Students with Disabilities It is the responsibility of students with disabilities to inform the instructor within the first two weeks of the semester/session of the need for reasonable accommodation of their disabilities. Students are advised to seek assistance from Disabled Students Services (DSS) on campus. As soon as the need for accommodation is determined by DSS and the need is communicated to the instructor by DSS, reasonable accommodation will be made.

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