Department of Accounting & Business Analytics
OM 420, Lecture A2:
Predictive Business Analytics
Course description Fall 2021
Lectures: Monday/Wednesday 2:00 – 3:20 PM, BUS B-18 Instructor: M. Hosein Zare
Office: BUS 4-21F
Email: [email protected]
Office Hours: Wednesdays 3:30 – 5:00 PM or by appointment
COURSE DESCRIPTION AND OBJECTIVES
Learning Outcomes: The objective of this course is for students to build fundamentals of predictive business analytics. Because business analytics has applications in a wide range of area such as finance, marketing and operations, the course covers variety of practical examples. For instance we consider
• How we can predict the risk of an investment
• Which customers we should target directly, given their predicted probability of responding to a certain advertisement
• How we should predict no-show probability of customers.
The goal of the course is twofold:
• To equip a proficient analyst with required practical skills for his/her role: give students the knowledge about how to extract data from relational databases, prepare the data for analysis, build basic predictive models using data mining software, and prepare reports that are easily understandable by managers
• To supply an essential skill for a qualified manager: give students the knowledge to interpret reports and recommendations that a manager might receive from business analysts, and to decide the best course of action
Since understanding the past is a basis for predicting the future, this course covers the following two dimensions of business analytics:
• Descriptive analytics, which “uses data to figure out what happened in the past”
• Predictive analytics, which “uses data to find out what could happen in the future”
The third dimension of business analytics is prescriptive analytics, which “uses data to prescribe the best course of action to increase the chances of realizing the best outcome”. It is covered by other OM courses such as OM461, OM471, and OM422.
COURSE MATERIALS Required Textbook:
• R for Data Science: Import, Tidy, Transform, Visualize, and Model Data by H.
Wickham, G. Grolemund (available online: http://r4ds.had.co.nz/)
• An Introduction to Statistical Learning: with Applications in R by G. James, D.
Witten, T. Hastie, R. Tibshirani (available online: http://www- bcf.usc.edu/~gareth/ISL/
eClass: You will be able to obtain lecture notes and related materials from eClass at https://eclass.srv.ualberta.ca/. Other than information provided in class, eClass will be where announcements and other information regarding the course will be made. Please plan to check this site regularly and/or make sure your settings in eClass will send you emails when there are updates. Note that the lecture notes are NOT an adequate substitute for class attendance. You can access the course web site with your CCID and password. Please contact IST (780-492-9400) or the [email protected], or contact eClass support for eClass issues (780-492-9372), if you need assistance.
Helpful Resources and Additional Readings: Online resources that can be helpful for this course:
• https://www.w3schools.com/sql/default.asp: Great for SQL help
• https://www.rdocumentation.org/: Online R manual
• http://stackoverflow.com/questions/tagged/r: The coding cookbook of the Internet Age!
If you want a book that covers the overall landscape of business analytics:
• Essentials of Business Analytics, 2nd Edition; Camm, Cochran, Fry, Ohlmann, Anderson, Sweeney, and Williams, 2016, South-Western College Pub.
Software: We will use the following software
• Microsoft Access (we mainly use it to write and execute SQL queries)
• R (students can either download R or RStudio)
EVALUATION
Course grades will be based on student performance on the following tasks:
Class participation 22 Sessions 10%
Assignments A1 – A5 20%
Exam 1 Exam 25%
Project Presentation/Report 45%
Letter grades will be assigned to the percentage marks in accordance with University Regulations [Section 23.4(4) of the University Calendar]. Grades in this course will be based on a combination of absolute achievement and relative performance. These grades represent the only marks available to students. No additional work or extra credit is available.
Participation: All students are expected to participate in class discussions and activities as these represent an opportunity for them to develop their understanding of the material.
Assignments: Assignments are individual (i.e., not group) and each assignment will count towards your final mark (i.e., no assignment will be dropped).
Exams: There will be one open-book exam through the semester where the questions will be similar to the assignments.
Final Group Project:
• The project consists of finding interesting information in a real-world data set and using this information to make predictions for better decisions. More details will be given during the course.
• Teams of 2 or 3. Undergrads with undergrads, MBAs with MBAs.
• Project presentation 10% (last week of class), project write-up 35%
Absences from Exams and Projects: Occasionally life events occur that require a student to miss term work, term examinations, or projects. However, excused absences are not granted automatically and will be considered only for acceptable reasons. Unacceptable reasons include, but are not limited to, personal events such as vacations or travel arrangements.
When a student is absent without an acceptable excuse, a final grade will be computed using a raw score of zero for the work missed.
In the case of the final group project, no extension will be provided to the group unless there are indications that all group members were incapacitated by legitimate and adequately documented reasons. Also, there will be no deferred term exams in this course.
COURSE POLICIES
Face masks in the classroom: To promote a healthy and safe learning, working, and living environment, non-medical face masks must now be worn in all public indoor areas on University of Alberta where physical distancing is not possible. The Public Health Agency of Canada (PHAC) and the U.S. Centre for Disease Control (CDC) both formally recommend
mask use indoors. Chief Medical Officer of Health Dr. Deena Hinshaw also recommends mask usage to reduce the spread of COVID-19. Given that there is not a minimum of 2 meters between the instructor or among students in our classroom, students are required to wear face masks while in the classroom. For more information please see the University of Alberta’s measure for fall return to campus and safety guide for students.
Link to official U of A grading policy documents:
https://policiesonline.ualberta.ca/PoliciesProcedures/Pages/DispPol.aspx?PID=101
Course Etiquette: Showing courtesy and professionalism to the professor and other students is an important part of every class. It is expected that students:
• Will arrive in class before it begins and will stay until it is over (a student should inform the professor in advance if they need to leave early on a specific day),
• Will not engage in sidebar conversations with other students,
• Will not read non-course materials.
Personal Electronics in the Classroom: Personal electronics are allowed in the classroom as long as they are used exclusively for class-related work (note taking, viewing files, related spreadsheets, etc.) and not used in a way that is distracting to the other students in the class.
Using cell phones, in any form, is not allowed in this class as it is detrimental to the class.
Class Attendance: Students are expected to attend all classes. If the student misses a class for any reason, they are responsible for all materials covered, announcements made, and handouts provided. The professor will not email handouts distributed in class or solutions to the in-class problems. If a serious conflict arises (religious observance, serious illness, death in the family, etc.), the student should notify the professor as soon as possible so that accommodations can be made.
ACADEMIC SUPPORTS
The Academic Success Centre provides professional academic support to help students maximize their academic success and achieve their academic goals. The Centre offers appointments, advising, group workshops, online courses, and specialized programming year- round to students in all university programs, and at all levels of achievement and study.
Location: 1-80 Students' Union Building, University of Alberta, North Campus Website: https://www.ualberta.ca/current-students/academic-success-centre Phone: 780-492-2682
Email: [email protected]
Accommodating Disabilities: Students who require accommodations in this course due to a disability affecting mobility, vision, hearing, learning, or mental or physical health are advised to discuss their needs with Specialized Support and Disability Services, 2-800 Students' Union Building, 492-3381 (phone) or 492-7269 (TTY) and to contact me as soon as possible so that we can discuss appropriate arrangements.
ACADEMIC INTEGRITY
Because we regard this class as we would any job responsibility, it seems prudent to clarify, in advance, the policy on academic integrity. If we determine that the work is not entirely that of the student(s) whose name(s) appear on the work, the student(s) involved may not pass this course and be further subject to program-level discipline. Specifically, in order to protect the integrity of the degrees, the University may expel, suspend, reprimand, or reduce a course mark of any student who breaches the Code of Student Behavior.
Absolute and complete academic honesty is expected of you in this course. It is important for you as a student to behave in an ethical manner. The University of Alberta is committed to the highest standards of academic integrity and honesty. Students are expected to be familiar with these standards regarding academic honesty and to uphold the policies of the University in this respect.
Students are particularly urged to familiarize themselves with the provisions of the Code of Student Behaviour (online at http://www.governance.ualberta.ca/StudentAppeals.aspx) and avoid any behaviour that could potentially result in suspicions of cheating, plagiarism, misrepresentation of facts and/or participation in an offence. Academic dishonesty is a serious offence and can result in suspension or expulsion from the University.
All assignments (except for the group project) are to be completed individually. However, I recognize the value of studying together. To help you judge what I consider acceptable and non-acceptable collaboration, consider the following.
Do:
• Discuss the course material with other students
• Ask classmates for help when you are stumped
• Offer help to other students
• Do your own work.
Don’t:
• Discuss numerical answers with other students
• Use someone else's words without proper attribution
o The best way to avoid using another student's words is to never look at another student's written answers to an assignment
o If you cite an article, book, web page, or any other source in your project report, then you must include complete information about that source
• Copy another student's spreadsheet file, sql file, or any other computer file o There are no exceptions to this rule. Copying another student's file for an
assignment (or another group's work, for the group project) is not acceptable, under any circumstances. It is irrelevant whether the copying is done
electronically or manually
Policy about course outlines can be found in Section 23.4(2) of the University Calendar.
