Instructor: Dr. Satheesh Aradhyula

Office: McClelland Park, Room 304A

Telephone:  621-6260 (Office)

E-mail:satheesh@arizona.edu

E-mail is the best way to reach me.  Include “Econ453” in the subject header of the email.  I will do my best to          respond to you by the end of the next business day.

Office Hours: MW 1:00 – 2:00 p.m. and by appointment. My office hours are likely to change over

the semester. Please watch d2L for any changes in my office hours. A good time to catch me for a quick chat is right after the class.

Class Hours: TTh 11:00 – 12:15.  Room 127 McClelland Hall.

TA: Risheng Xu. E-mail:rux19@email.arizona.edu

Office Hours: Mondays 10:00 – 11:00 in Room 401B, McClelland Hall.

Pre-requisites: ECON/MGMT 276 or ECON/AREC339 and ECON300 or ECON361.

Course Web Site: This  class  has  a  d2L  course  web  site  that  can  be  accessed  at d2L.arizona.edu. Important announcements, your grades, syllabus, PowerPoint slides, handouts, reading materials, homework, old exams, etc. will be posted on this site. You should visit this site on a regular basis throughout the semester.

Textbooks: The following textbook is required:

Business Analytics: Communicating with Numbers. Second Edition. By Sanjiv Jaggia, Alison Kelly, Kevin Lertwachara, and Leida Chen. McGraw Hill. 2023.   ISBN- 10:

1265897107. ISBN- 13: 978- 1265897109

The  following  book  is  a  very  popular  book  for  statistical  foundations  for  data analytics. This book is not required but students may find it helpful for additional insights:

James, G., Witten, D., Hastie, T., and Tibshirani, R., An Introduction to Statistical Learning with Applications in R, 2017, Springer.

Course Objectives: This course is an introduction to the concepts and applications of data analytics and

applied econometric modeling. An objective of this course is to introduce the basic ideas of “modern” statistical learning and predictive modeling. Most companies and governments  today  collect  an  overwhelming  amount  of data.  However,  gaining insight from analytics continues to elude many organizations. Data analytics require individuals to be knowledgeable in the fundamentals of business (e.g., knowing the right questions to ask) as well as an array of disciplines from information systems, machine  learning  and  statistics.  To  help meet  the  market  demand  for  analytics professionals,  this  course  provides  students  with  analytical  and  econometrics toolsets that enable them to address data-driven (business) problems. The course features case studies and hands-on approaches to demonstrate the analytics concepts and techniques used in the business world. The course makes use of the leading business analytics tools: R and R Studio. We discuss statistical foundations and techniques for predictive and descriptive analytics.

Learning Outcomes: As a result of taking this course, students will be able to:

1.  Summarize data using visualization and descriptive analytics

2.  Formulate relevant hypotheses for various business situations,  construct confidence intervals and perform hypothesis testing

3.  Use appropriate statistical and data-mining techniques for predictive analysis and decision making

4.  Formulate, estimate, and interpret linear and non-linear regression models,

5.  Make predictions with linear and non-linear regression models

6.  Program in R for all the above objectives

7.  Interpret and communicate analysis results to business audience

8.  Demonstrate effective participation in teams

Student Evaluation: Grades will be earned based on student performance in Problem Sets (psets), midterm exam, final exam, and a final project with the following weights:

Midterm and final exam may be take-home. If take-home, students will have 24 hours to complete the exam. When a student has a legitimate reason (documented emergency) for missing a midterm, the weight of the exam may be added to the remaining exams. No make-up or early exams are given. The semester grade will be awarded according to the following scale:

Problem Sets: The number of Problem Sets (psets) is a random variable with an expected value of 5 and

a variance of 0.0025.  Unless stated otherwise, all psets carry equal weight. All psets are due on the announced date. Students will be given one week to complete psets. Late       submissions will not be accepted. Students are expected to solve the psets individually.  All psets require the use of R software.

Final Project: The final project (Term Paper) is one of the most important pieces of this course. It is a

signal to the market that you dominate powerful analytics tools. This is a team project     with each team consisting of three individuals. The Final project is due on the last day of classes. Students may be asked to present the project in the class (or on Zoom).

Absence and class participation policies: The UA’s policy concerning Class Attendance, Participation, and   Administrative   Drops   is   available   at: http://catalog.arizona.edu/policy/class- attendance-participation-and-administrative-drop.  The UA policy regarding absences for any sincerely held religious belief, observance or practice will be accommodated where reasonable, http://policy.arizona.edu/human-resources/religious-accommodation-policy. Absences pre-approved by the UA Dean of Students (or Dean Designee) will be honored. See: https://deanofstudents.arizona.edu/absences.

Attendance is not required  at all lectures and discussion  section meetings. However, participating in the course and attending lectures and other course events are vital to the learning process. Lectures  are the primary means  of conveying the material and the relative importance of the topics covered. Dr. Aradhyula has observed a strong positive correlation between class attendance and marks obtained. Over the past ten years, 69% of students who missed 6 or more lectures in Dr. Aradhyula’s undergraduate classes ended up with a D or E grade. If a class is missed, it is your responsibility to obtain class notes, handouts, etc., from your classmates.

If you anticipate being absent, are unexpectedly absent, or are unable to participate in class activities, please contact me as soon as possible. To request a disability-related accommodation to this attendance policy, please contact the Disability Resource Center at (520) 621-3268 or [email protected].

If you are experiencing unexpected barriers to your success in your courses, the Dean of Students Office is a central support resource for all students and may be helpful. The Dean of Students Office is in the Robert L. Nugent Building, room 100, or call 520-621-

7057.

Accessibility and Accommodations: At the University of Arizona, we strive to make learning experiences as accessible as possible. If you anticipate or experience physical or academic barriers based on disability or pregnancy, you are welcome to let me know so that we can discuss options.   You   are   also   encouraged   to   contact   Disability   Resources   (520-621- 3268,https://drc.arizona.edu/) to   establish   reasonable   accommodations. Please   be aware  that  the  accessible  table  and  chairs  in  this room  should remain  available  for students who find that standard classroom seating is not usable.

Incomplete Grade & Withdrawal Grade: Requests for incomplete (I) or withdrawal (W) must be made in accordance        with        University        policies,        which        are        available        at http://catalog.arizona.edu/policy/grades-and-grading-system#incomplete and http://catalog.arizona.edu/policy/grades-and-grading-system#Withdrawalrespectively.

Academic Integrity: Students are encouraged to share intellectual views and discuss freely the principles and  applications  of the  course  materials.  However,  graded  psets  must  be  executed independently, except as noted by the instructor. For example, if answers to assignments are found to be copied (or plagiarized), students will be severely penalized. Students are expected     to     adhere     to     the     UA     Code     of     Academic     Integrity;     see http://deanofstudents.arizona.edu/academic-integrity/students/academic-integrity.       The University  Libraries  have  some  excellent  tips  for  avoiding  plagiarism,  available  at http://new.library.arizona.edu/research/citing/plagiarism.

Threatening Behavior Policy: The UA Threatening Behavior by Students Policy prohibits threats of physical harm to any member of the University community, including to oneself. See http://policy.arizona.edu/education-and-student-affairs/threatening-behavior-students.

Nondiscrimination and Anti-harassment: The University  of Arizona  is  committed to  creating  and maintaining an environment free of discrimination. In support of this commitment, the University prohibits  discrimination,  including harassment  and retaliation, based  on  a protected  classification,  including  race,  color,  religion,  sex,  national  origin,  age, disability, veteran status, sexual orientation, gender identity, or genetic information. For more     information,     including     how     to     report     a     concern,     please     see http://policy.arizona.edu/human-resources/nondiscrimination-and-anti-harassment-policy. Our classroom is a place where everyone is encouraged to express well-formed opinions and  their  reasons  for  those  opinions.  We  also  want  to  create  a  tolerant  and  open environment  where  such  opinions  can be  expressed without resorting to bullying  or discrimination of others.

Subject to Change Statement: Information in this syllabus, other than the grade and absence policy, may be  subject to  change with reasonable  advance notice,  as  deemed  appropriate by  the instructor.

Covid Protocols: We will follow all University of Arizona guidelines. Please stay home if you test positive or do not feel well. If I were to test positive during the semester, I may be required to stay at home. If that happens, I may deliver lectures on Zoom.

Tentative Course Outline (May be revised as semester progresses)

1.   Introduction to business analytics

2.   Data management

3.   Exploring data with descriptive analytics

4.   Exploring data with visualization

5.   Probability and Probability Distributions

6.   Statistical Inference (sampling distributions, estimation, confidence intervals, hypothesis testing)

7.   Regression analysis

8.   More topics in regression analysis (interaction variables, nonlinear models, cross-validation)

9.   Logistic regression

10. Forecasting with Time-Series Models

11. Fundamentals of data mining techniques

12. k-Nearest neighbors analysis

13. Naive Bayes analysis

14. Decision trees

15. Cluster analysis

16. Optimization with linear and nonlinear programming.

All these topics are covered in the text. Where needed, I may provide supplemental materials. We may not have time to cover all these topics. We will certainly cover topics 1 – 10 and a few of the remaining topics.