代写BIA 500 Business Analytics: Data, Models, and Decisions Spring 2025代写Java程序

School of Business

Syllabus

BIA 500

Business Analytics: Data, Models, and Decisions

BIA 500 6:30 – 9:00 PM Tuesday

Spring 2025

Overview

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

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

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

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

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)

Textbook: James R. Evans, Business Analytics, 3rd edition Pearson.  

Required Readings and additional readings

 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

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.

Letter Grade

Range

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%

热门主题

课程名

mktg2509 csci 2600 38170 lng302 csse3010 phas3226 77938 arch1162 engn4536/engn6536 acx5903 comp151101 phl245 cse12 comp9312 stat3016/6016 phas0038 comp2140 6qqmb312 xjco3011 rest0005 ematm0051 5qqmn219 lubs5062m eee8155 cege0100 eap033 artd1109 mat246 etc3430 ecmm462 mis102 inft6800 ddes9903 comp6521 comp9517 comp3331/9331 comp4337 comp6008 comp9414 bu.231.790.81 man00150m csb352h math1041 eengm4100 isys1002 08 6057cem mktg3504 mthm036 mtrx1701 mth3241 eeee3086 cmp-7038b cmp-7000a ints4010 econ2151 infs5710 fins5516 fin3309 fins5510 gsoe9340 math2007 math2036 soee5010 mark3088 infs3605 elec9714 comp2271 ma214 comp2211 infs3604 600426 sit254 acct3091 bbt405 msin0116 com107/com113 mark5826 sit120 comp9021 eco2101 eeen40700 cs253 ece3114 ecmm447 chns3000 math377 itd102 comp9444 comp(2041|9044) econ0060 econ7230 mgt001371 ecs-323 cs6250 mgdi60012 mdia2012 comm221001 comm5000 ma1008 engl642 econ241 com333 math367 mis201 nbs-7041x meek16104 econ2003 comm1190 mbas902 comp-1027 dpst1091 comp7315 eppd1033 m06 ee3025 msci231 bb113/bbs1063 fc709 comp3425 comp9417 econ42915 cb9101 math1102e chme0017 fc307 mkt60104 5522usst litr1-uc6201.200 ee1102 cosc2803 math39512 omp9727 int2067/int5051 bsb151 mgt253 fc021 babs2202 mis2002s phya21 18-213 cege0012 mdia1002 math38032 mech5125 07 cisc102 mgx3110 cs240 11175 fin3020s eco3420 ictten622 comp9727 cpt111 de114102d mgm320h5s bafi1019 math21112 efim20036 mn-3503 fins5568 110.807 bcpm000028 info6030 bma0092 bcpm0054 math20212 ce335 cs365 cenv6141 ftec5580 math2010 ec3450 comm1170 ecmt1010 csci-ua.0480-003 econ12-200 ib3960 ectb60h3f cs247—assignment tk3163 ics3u ib3j80 comp20008 comp9334 eppd1063 acct2343 cct109 isys1055/3412 math350-real math2014 eec180 stat141b econ2101 msinm014/msing014/msing014b fit2004 comp643 bu1002 cm2030
联系我们
EMail: 99515681@qq.com
QQ: 99515681
留学生作业帮-留学生的知心伴侣!
工作时间:08:00-21:00
python代写
微信客服:codinghelp
站长地图