22 Jun EBTM 350: Business Analytics

EBTM 350: Business Analytics

 

Semester / Term

Course-Section

Instructor

Email

Phone

 

 

Course Description

 

 

This course addresses the contemporary business issues of using data to support decision-making and implement change. The course focuses on using standard business analytic models to summarize and analyze data, build models, and drive impact through quantitative decision-making. Innovative trends in business will be explored, through methods to create and frame problems. Descriptive, predictive, and prescriptive analytics will be discussed, illustrating the transformation from knowledge gained through problem formation into practice. Creative solutions to open-ended analytics problems will be explored, using data to discover patterns and trends.

Pre-Requisites: EBTM 251; Major in ACCT, BUAD, or EBUS-BSAP; Junior/Senior standing

 

Text

 

 

Evans, James, Business Analytics, 3rd edition, 2020, Pearson Education. TU Direct Access to the etext and Pearson’s MyLab online assignments is required. Students may also upgrade their digital access and add a paper copy as an option. Direct Access will billed to the student individual accounts. eText is accessible via Pearson MyLab with hard copy purchase options.

 

Learning Outcomes

 

After completing this course, students will be able to:

· Analyze data

· Build and use models for common decision scenarios

· Analyze support for decisions involving uncertainty and risk

· Manipulate data and perform quantitative analyses

· Develop optimization models and understand their implementation

· Discover patterns in data through data mining

· Apply the three main types of analytics: descriptive, predictive, and prescriptive

 

 

Content objectives assessed in the comprehensive exam (in MNGT 481) are:

· Analyze patterns and trends in data to create predictive analytics

· Develop prescriptive analytics to identify optimal solutions

· Quantitatively evaluate alternatives to a decision problem

· Understand nuances in single-period demand and predict profit for demand

 

Learning Assessment

 

 

The academic programs at Towson University College of Business and Economics are internationally accredited by the Association for the Advancement of Collegiate Schools of Business (AACSB). Each semester, as an ongoing part of this exclusive certification, the Business Analytics and Technology Management Department conducts routine assessment of students’ proficiency in selected dimensions of knowledge, skills, and attitudes (KSAs), as well as content mastery of selected business topics and students’ satisfaction with the learning experiences provided by the department. The methods of such assessment will be communicated by the instructor. Student participation in these assessments, as part of the overall departmental learning program, is mandatory.

 

Assignments and Examinations

 

Assignments include a mixture of objective, computational analytical and technical questions that require thinking and applying the appropriate concepts and theories. Details regarding the case study will be given online or in class. Plagiarism or missed deadline means 0 grade.

Exams will evaluate concepts understanding and problem-solving skills. The format may include multiple-choice, analytical problems and short essay questions. Plagiarism or missed exam means 0 grade.

 

Evaluation and Grading Policy

 

Weighted Average:

Evaluation Category	Percentage
Exercises and Homework	40%
Exam 1	30%
Exam 2	30%
Total	100%

 

Course Grades:

Percentage	Letter Grade
≥ 93.33%	A
90.00 – 93.32%	A-
86.66 – 89.99%	B+
83.33 – 86.65%	B
80.00 – 83.32%	B-
75.00 – 79.99%	C+
70.00 – 74.99%	C
65.00 – 69.99%	D+
60.00 – 64.99%	D
< 60%	F

Course Policies and Procedures

 

 

Writing Standards Effective managers, leaders, and teachers are also effective communicators. Written communication is an important element of the total communication process. The College of Business & Economics recognizes and expects exemplary writing to be the norm for course work. To this end, all papers, individual and group, must demonstrate undergraduate level writing and comply with the format requirements of the Publication Manual of the American Psychological Association (6th Edition 2009). Careful attention should be given to spelling, punctuation, source citations, references, and the presentation of tables and figures.

 

Timeliness Students are expected to attend class regularly and complete all course work on time and error free. Work submitted online should follow standard procedures for formatting and citations. Missing deadlines means 0 grade and missing exams means 0 grade.

Academic Integrity and Plagiarism Academic integrity is central to the learning and teaching process. Students are expected to conduct themselves in a manner that will contribute to the maintenance of academic integrity by making all reasonable efforts to prevent the occurrence of academic dishonesty. Academic dishonesty includes, but is not limited to, obtaining or giving aid on an examination, having unauthorized prior knowledge of an examination, doing work for another student, and plagiarism of all types.

Plagiarism is the intentional or unintentional presentation of another person’s idea or product as one’s own. Plagiarism includes, but is not limited to, the following: copying verbatim all or part of another’s written work; using phrases, charts, figures, illustrations, or mathematical or scientific solutions without citing the source; paraphrasing ideas, conclusions, or research without citing the source; and using all or part of a literary plot, poem, film, musical score, or other artistic product without attributing the work to its creator. Students can avoid unintentional plagiarism by following carefully accepted scholarly practices. Notes taken for papers and research projects should accurately record sources to material to be cited, quoted, paraphrased, or summarized, and papers should acknowledge these sources. The penalties for plagiarism include a zero or a grade of F on the work in question, a grade of F in the course, suspension with a file letter, suspension with a transcript notation, or expulsion. Students may learn more about Towson University’s formal policies at: https://inside.towson.edu/generalcampus/tupolicies/index.cfm

Disability and Equal Access Policy In accordance with Towson University, State of Maryland and federal government policies, students will not be denied access to this course on the basis of race, color, religion, age, national origin, sex, disability and sexual orientation. Students with documented disabilities and who require accommodations should contact the instructor within the first week of the class and make arrangements for accommodation. Students with disabilities must verify their eligibility for accommodations through the Office of Disability Support Services [AD 232, (410) 704-2638 (Voice or TDD), www.towson.edu/dss. See also the Office of Diversity and Equal Opportunity, 410-704-2360, www.towson.edu/aa.

 

 

Tentative Course Outline

 

 

Week	Topics
1	Introduction (Ch1) with MyLab Registration on Day 1
2	Data Visualization (Ch3)
3	Trendlines and Regression (Ch8)
4	Forecasting (Ch9)
5	Data Mining (Ch10)
6	Spreadsheet Modeling (Ch11)
7	Decision Analysis (Ch16)

 

 

Detailed timelines will be announced in Blackboard as we progress. It is intended to take relatively less time for shorter chapters and more time for longer chapters.

Bibliography

 

1. Drake, M. (2014). The Applied Business Analytics Casebook: Applications in Supply Chain Management, Operations Management, and Operations Research. Upper Saddle River, NJ: Pearson.

2. Watson, M., Nelson, D., & Cacioppi, P. (2014). Managerial Analytics: An Applied Guide to Principles, Methods, Tools, and Best Practices. Upper Saddle River, NJ: Pearson.

3. Camm, J. D., Cochran, J. J., Fry, M. J., Ohlmann, J. W., & Anderson, D. R. (2015). Essentials of Business Analytics. Stamford, CT: Cengage Learning.

4. Winston, W. (2014). Marketing Analytics: Data-driven Techniques with Microsoft Excel. Indianapolis, IN: Wiley.

5. Bell, P. C., & Zaric, G. S. (2013). Analytics for Mangers with Excel. New York, NY: Routledge Taylor and Francis.

6. Heizer, J., Render, B., and Munson, C. (2017). Operations Management: Sustainability and Supply Chain Management (12th ed). Boston: Pearson.