MISY 262 Fundamentals of Business Analytics

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MISY 262 Fundamentals of Business Analytics
Winter 2024

Instructor:
Prof. Xiaoye Cheng, PhD ([email protected] (mailto:[email protected]) )
TA: Fariha Tabassum ([email protected] (mailto:[email protected]) )

Topics

This course provides an introduction to the basic tools and methods of business analytics with specific emphasis on decision making. Topics include, but are not limited to, data analysis, correlation, linear regression, logistic regression, regression assumptions, variable selection, outliers, interactions, and issues that arise when using business analytics in practice. Students will learn to use the R programming environment (https://www.r-project.org/about.html) for manipulating data and generating models.
There is no textbook required for this class. All class material will be distributed via Canvas.

Course Format & Requirements

All lectures for this course will be asynchronous with a series of videos available on Canvas that you can watch anytime. The lectures have been assigned a date, just to help you pace yourself with course material throughout the session, but you can watch (and re-watch) them any time once their corresponding Canvas module has become available.
Exams for this course will be given on following dates:
Exam 1: January 12, 2024
Exam 2: January 23, 2024
Final Exam: February 3, 2024

Participation in all exams is mandatory. All exams will require the use of R Software. Students will have 90 minutes to compete Exams 1-2 and 120 minutes to complete the cumulative Final Exam. DSS students will have adjusted time based on each student’s accommodations (Note: DSS students will need to contact the DSS office to make sure that their accommodations are sent to Professor Cheng).

The course also includes nine (9) homework assignments that will be administered on Canvas and will have assigned due dates. Homework submission expires at 11:59pm EST on the due date. Students will have the flexibility to submit before the due date, but not after. There will be no makeups for missed homework assignments, so please make sure you submit on time.

The course will require the use of the R programming language to conduct data analysis, model building, etc. All assignments (exams and homework) will require students to use the R software and Canvas, so high speed uninterrupted internet access is necessary. Instructions on how to
download and use the R software will be provided on Canvas and the course does not require any programming background. R software does not require a license fee and there will be no books for this course, just material that will be provided to you on Canvas. This is an intense hands-on course so please plan to spend several hours every day. It is NOT a course that you can catch up if you fall behind. If you feel that you need more time to learn programming and complete assignments, it is better to take this course during Fall or Spring semesters (the course topics are the same during summer, winter, fall, and spring). The same is true if you do not know whether you will have uninterrupted internet access at your Winter location.

Live Office Hours on Zoom

Professor Cheng, or the course TA will hold LIVE online office hours via Zoom every Tuesday and Thursday (see the LIVE Office Hours page in Modules).

Office hours are optional, but provide students the opportunity to ask questions on challenging topics.

Canvas

Students must access videos, presentations, datasets, homework assignments, and exams via Canvas. Please check Canvas frequently for announcements, updates, assignments, and other materials. Please explore the Canvas course site and familiarize yourself with all sections including Syllabus, Modules, Announcements, Assignments, etc.

Business Analytics Programming

Students will use the programming language R (https://www.r-project.org/about.html) for completion of homework assignments and exams. R is an integrated suite of software facilities for data analytics, data manipulation, and graphical display. Exams and projects will require students to use R, perform data analysis and build models, interpret R output, and make decisions. R is an open source software that can be downloaded at no cost. Programming and business analytics modeling are two of the most important hard skills that today’s employers seek. Former students have been able to leverage the skills learned in this course to pursue employment opportunities. However, these extremely useful but challenging skills take practice and attention to develop. Plan to spend several hours per week on homework problems and exam preparation.

Exams and Homework

The main form of assessment in this class includes 2 online midterm exams (in Canvas) that will account for 50% of final grade, best 8 out of 9 Online Homework assignments (in Canvas) that will account for 20% of final grade, and an online cumulative final in Canvas) that will count for 30% of final grade. The exams and homework will attempt to simulate experiences students will face working on business analytics projects in early career roles. Participation in all exams is mandatory, and ALL exams count towards the final grade.
Exams for this course will be will be given on following dates:
Exam 1: January 12, 2024
Exam 2: January 23, 2024
Final Exam: February 3, 2024
The best 8 out of 9 homework will be counted towards final grade. If you miss a homework assignment for whatever reason during the Winter session, this will be your dropped homework. All homework assignments and Exams should be completed INDIVIDUALLY. Any violations will result in a grade of zero (0) for the respective exam or homework and will be subject to University of Delaware disciplinary rules.

Grading

The grading distribution for this course is as follows:


The grading scale below will be used to calculate final grades. There will be no rounding (e.g., 92.83 is an A-, not an A):

Important Course Policies

Students will need a personal computer (Mac or Windows) and uninterrupted internet access in order to complete this course.

Students are NOT allowed to use advanced automated tools (artificial intelligence or machine learning tools such as ChatGPT or Dall-E 2) in homeworks and exams of this course. Each student is expected to complete all work without substantive assistance from automated tools.

Students must follow the Academic Honor Code. Academic dishonesty will be reported to the University Office of Student Conduct immediately.

If there are any questions about class policies or grading, please ask Professor Cheng via email or during office hours.

Student feedback is always welcome—in person, by email, through occasional surveys, and the end-of-semester course evaluation.

Important University Policies

Our online course environment should be mutually respectful and inclusive of all students. The online course should be an environment with no discrimination, where everyone feels comfortable to contribute to and benefit from the entire online learning experience. Any suggestions to improve class interactions or any concerns should be brought to my attention.

It is unacceptable and a violation of university policy to harass, discriminate against or abuse any person because of a person's race, color, national origin, gender, sexual orientation, disability, religion, age or any other characteristic protected by applicable law. Such behavior threatens to destroy the environment of tolerance and mutual respect that must prevail for this university to fulfill its educational mission. Contact the Office of Equity and Inclusion (http://www.udel.edu/oei/(http://www.udel.edu/oei/) ) if you believe a violation has occurred.

If, at any time during this course, I happen to be made aware that a student may have been the victim of sexual misconduct (including sexual harassment, sexual violence, domestic/dating violence, or stalking), I am obligated by federal law to inform the university’s Title IX Coordinator. The university needs to know information about such incidents to, not only offer resources, but to ensure a safe campus environment. The Title IX Coordinator will decide if the incident should be examined further. I will not disclose information to anyone other than the Title IX Coordinator.

This course is open to all students who meet the academic requirements for participation. Any student who has documented a need for accommodation should contact the Office of Disability Support Services ([email protected] or 302-831-4643) and the instructor to discuss the specific situation and coordinate accommodations as soon as possible.

Winter, 2024 Calendar (subject to change)

Jan 3

Syllabus

Introduction to Business Analytics
Review Syllabus and Explore Canvas Site
Download R & Course Data
Review of Statistical Concepts
Review of Stats Concepts: Module 0
Jan 4
Introduction to R – Part 1
Watch R Videos: Module 1
HW1 ASSIGNED (DUE Jan 8)
Jan 5
Introduction to R – Part 2
Watch R Videos: Module 1
HW2 ASSIGNED (DUE Jan 9)
Jan 8
Lin Reg Basics
Lin Reg Coefficients
HW3 ASSIGNED (DUE Jan 10)
Jan 9
Lin Reg Coefficients
Lin Reg Fit
Jan 10
Lin Reg Multiple
PRACTICE EXAM 1 (No Solution)
HW4 ASSIGNED (DUE Jan 11)
Jan 11
Lin Reg Categorical & No Intercept
STUDY GUIDE FOR EXAM 1
Jan 12
ONLINE EXAM 1
Jan 15
MLK HOLIDAY– NO CLASS
Jan 16
Multicollinearity
HW5 ASSIGNED (DUE Jan 21)
Jan 17
Lin Reg Assumptions
Jan 18
Lin Reg Assumptions
Log Reg Basics
Jan 19
Log Reg Coefficients
Log Reg Fit
PRACTICE EXAM 2 (No Solution)
HW6 ASSIGNED (DUE Jan 22)
Jan 22
Log Reg Assumptions
STUDY GUIDE FOR EXAM 2
Jan 23
ONLINE EXAM 2
Jan 24
Linear Regression Prediction
HW7 ASSIGNED (DUE Jan 29)
Jan 25
Linear Regression Prediction
Jan 26
Log Reg Prediction
HW8 ASSIGNED (DUE Jan 31)
Jan 29
Log Reg Prediction
Jan 30
Log Reg Prediction
Jan 31
Overfitting & Variable Selection
HW9 ASSIGNED (Due Feb 2)
Feb 1
Outliers
Feb 2
Interactions
STUDY GUIDE FOR FINAL EXAM
Feb 3
CUMULATIVE FINAL EXAM

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