代写BIA 500 Business Analytics: Data, Models, and Decisions Spring 2025代写Java程序
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Syllabus
BIA 500
Business Analytics: Data, Models, and Decisions
BIA 500 6:30 – 9:00 PM Tuesday
Spring 2025
Overview
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Many managerial decisions—regardless of their functional orientation—are increasingly based on analysis using quantitative models from the discipline of management science. Management science tools, techniques and concepts (e.g., data, models, and software programs) have dramatically changed the way businesses operate in manufacturing, service operations, marketing, transportation, and finance. This course explores data-driven methods that are used to analyze and solve complex business problems. Students will acquire analytical skills in building, applying, visualizing and evaluating various models with hands-on computer applications. Topics include descriptive, predictive and prescriptive approaches to data analytics and modeling. Prerequisites: Admission requirements for the program. |
Introduction to Course and course objectives
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This course is designed to introduce students to the fundamental techniques of using data to make informed management decisions. In particular, the course will focus on various ways of modeling, or thinking structurally about, decision problems in order to enhance decision-making skills. Rather than survey all of the techniques of management science, the course stresses those fundamental concepts that we believe are most important for the practical analysis of management decisions. Consequently, the course focuses on evaluating uncertainty explicitly, understanding the dynamic nature of decision-making, using historical data and limited information effectively, simulating complex systems, and optimally allocating resources. The implementation of these tools has been facilitated considerably by the development of spreadsheet-based software packages, and so we will make liberal use of spreadsheet models. The objective of this course is for students to become intelligent users of management science techniques. In that vein, emphasis will be placed on how, what and why certain techniques and tools are useful, and what their ramifications would be when used in practice, all in concert with the overarching goal to become better managers. This will necessitate some mechanical manipulations of formulas and data, but it is not our goal for you to become adept handlers of mathematical equations and computer software. Course Objectives This course is designed to introduce students to the fundamental techniques of using data to make informed management decisions. In particular, the course will focus on various ways of modeling, or thinking structurally about, decision problems in order to enhance decision-making skills. Rather than survey all of the techniques of management science, the course stresses those fundamental concepts that we believe are most important for the practical analysis of management decisions. Consequently, the course focuses on evaluating uncertainty explicitly, understanding the dynamic nature of decision-making, using historical data and limited information effectively, simulating complex systems, and optimally allocating resources. The implementation of these tools has been facilitated considerably by the development of spreadsheet-based software packages, and so we will make liberal use of spreadsheet models. The objective of this course is to students to become intelligent users of management science techniques. In that vein, emphasis will be placed on how, what and why certain techniques and tools are useful, and what their ramifications would be when used in practice, all in concert with the overarching goal to become better managers. This will necessitate some mechanical manipulations of formulas and data, but it is not our goal for you to become adept handlers of mathematical equations and computer software. |
Relationship of Course to Rest of Curriculum
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- Introduce the student to descriptive analytics - Introduce the students to machine learning and predictive analytics - Introduce the student to ethical issues regarding Artificial intelligence and bias - Using excel Tableau and Rapid miner. Using these tools will help the student in projects in subsequent courses |
Learning Goals
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After taking this course, students will be able to: Upon completion of this course, students should be able to: · Understand the role and value of data in business decisions, · Identify and assess opportunities for creating value using data-driven decision making, · Identify and utilize the right data-centric tools and techniques · interpret the output of tools and techniques and run sensitivity analyses to improve business decisions. |
Pedagogy
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The course will employ lectures, class discussion, in-class individual assignments, individual homework and a project. In the project, students will analyze a real problem, formulate a model, collect data, solve the problem using one or more of the techniques discussed in class, and interpret the solution for management. |
Required Text(s)
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Textbook: James R. Evans, Business Analytics, 3rd edition Pearson. |
Required Readings and additional readings
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Additional materials will be posted on Canvas for review Additional course calendar will be posted on canvas and is included at the end of this document. |
Technology
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Please bring your laptop to class and Install Excel, Tableau and RapidMiner on your laptop. - Microsoft Office for Stevens Students: https://sit.teamdynamix.com/TDClient/1865/Portal/KB/ArticleDet?ID=28586 - Tableau student license: https://www.tableau.com/academic/students - RapidMiner student license: https://rapidminer.com/educational-program/ |
Assignments
Individual Homework (20%) To help reinforce the material covered in the lectures, a homework exercise will be assigned each week, which will involve formulating and solving a small but practically-relevant homework problem from the text book. Oral presentations of homework may part of the course.
Homework Submission. All homework must be submitted through the Canvas web site. Presentation of Homework. The Excel assignments will be graded on their clarity as well as numerical accuracy. Homework assignments are due on the day that is indicated within canvas. Please see the Assignment Rubric within this syllabus.
Midterm Examination (25%) This examination will take place shortly after the mid-point of the course. Its purpose is to consolidate the learning on optimization techniques. It will involve the formulation and solution of a number of small but typical problems from business practice.
Course Project (20%) A project relative to material covered in the course will be assigned. Details of this project will be forth-coming.
Final Examination (35%) This examination will take place shortly after the end-point of the course. Its purpose is to consolidate the learning on optimization techniques. It will involve the formulation and solution of several small but typical problems from business practice.
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Letter Grade |
Range |
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A |
>= 94% |
|
|
A- |
>= 90.0% |
< 94.0 % |
|
B+ |
>= 87.0% |
< 90.0 % |
|
B |
>= 84.0% |
< 87.0 % |
|
B- |
>= 80.0% |
< 84.0 % |
|
C+ |
>= 77.0% |
< 80.0 % |
|
C |
>= 74.0% |
< 77.0 % |
|
C- |
>= 70.0% |
< 74.0 % |
|
D+ |
>= 67.0% |
< 70.0 % |
|
D |
>= 64.0% |
< 67.0 % |
|
D- |
>= 60.0% |
< 64.0 % |
|
F |
< 60% |
|
