SCHOOL OF BUSINESS
Course Syllabus
Data Driven Decision-Making
BUAD 631 (4 Units)
INSTRUCTOR:
M.S. in Mathematics
M.S. in Physics
M.A. in Education
CATALOG DESCRIPTION
BUAD 631 (4 Credits): The course focuses on descriptive and predictive analytics for
decision-making from a variety of business disciplinary perspectives. Important
elements include identification of data driven decision-making contexts in business,
ascertaining data needs, analyzing data, interpreting and communicating results, and
ultimately the value proposition of analytics.
COURSE GOAL
Managerial decision-making is vital for a firm’s long-term survival. With the explosion of
data availability in organizations and the availability of easy-to-use tools for data analysis,
data driven decision-making is providing increased value to organizations. Thus, analysis
and interpretation of qualitative and quantitative data have become essential parts of a
manager’s job. This course focuses on how business decision-makers use descriptive and
predictive analytics to make effective decisions. Concepts and techniques learned in this
course will be applied in subsequent courses to analyze problems in economics,
accounting, marketing, finance, and operations management.
COURSE LEARNING OUTCOMES
Upon successful completion of this course, students will be able to:
1. Identify organizational opportunities for data-driven decision-making that create
value.
2. Define each opportunity clearly and accurately, and articulate why your definition
is right.
3. Specify the data needs for each opportunity identified, including its collection,
and justify how that data is relevant for the context.
4. Identify and justify the right data analyses that could support decision-making in
the context of the opportunity and data needs specified.
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5. Perform relevant analyses using the right business analytics (BA) tools.
6. Effectively communicate the findings of the analyses performed to the decision
maker(s).
7. Evaluate and articulate the value delivered by data-driven decision-making, for
each opportunity identified.
8. Evaluate the ethical implications associated with BA use in organizations to assist
decision making.
COURSE OBJECTIVES
1. Evaluate business analytics, its characteristics, and value creation
2. Identify organizational opportunities for BA use
3. Identify data and measurement requirements for organizational problems
4. Compare and contrast exploratory and confirmatory data analyses
5. Articulate fit between data and analyses
6. Compare and contrast descriptive and predictive statistical analyses
7. Describe the need for data visualization
8. Interpretation and communication of analyses results
9. Use different tools for analyses
10. Evaluate ethical implications of data-driven decision-making
LEARNING MATERIALS AND RESOURCES
REQUIRED TEXTS:
1. Diez, David M, Barr, Christopher D, and Mine Cetinkaya-Rundel. (2017).
OpenIntro Statistics (Third edition). Openintro.org. Use the following link for a
free download: https://www.openintro.org/stat/textbook.php.
2. Tufte, Edward (1997). Visual and statistical thinking: Displays of evidence for
making decisions. Cheshire, CT: Graphics Press. ISBN-13: 978-0961392130
3. Ayers, I. (2008). Super Crunchers: Why thinking-by-numbers is the new way to
be smart. New York, NY: Bantam Books. ISBN-10: 0553384732; ISBN-13: 9780553384734
4. Davenport, Thomas H, and Jinho Kim. (2013). Keeping up with the quants.
Boston, MA: HBS Publishing. ISBN-13: 978-1-4221-8725-8
REQUIRED ARTICLES
Please see a listing of required articles on the page following the course outline.
ASSIGNMENTS
Please go through the syllabus with some care. Details will be discussed and clarified
during first session of the course.
Homework sets
Each homework set will test students’ understanding of concepts and applications
discussed in one are more sessions preceding the due date. This could consist of
problems and mini cases.
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For each homework set, a separate handout will give more details.
Midterm exam (Take-home)
This exam will test you on the materials assigned/discussed in all sessions preceding
the due date for submitting the exam.
Final Exam
Final exam will specifically test you on the materials covered after the midterm exam.
However, as the course material is sequential in nature, some questions may require
you to integrate prior materials in your answers.
As a part of the in-class final exam, there will be a set of multiple choice questions.
This will be used as the assessment quiz for the course. This assessment quiz is part of
the University of Redlands School of Business continuous quality and process
improvement efforts towards maintaining ACBSP accreditation.
Project
The project will test your ability to identify a business problem (or an opportunity),
develop some descriptive and/or predictive BA analysis, and propose a solution for the
problem defined. You will identify an appropriate problem from a real
business/organization, specify and collect the data needed, perform appropriate
analyses, and present your findings in the form of a report to the sponsor in the
business. A separate handout will give more details on this project.
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Course Assessment and Grading
Your grade will be determined as follows:
Assignment
Homework set#1
Homework set#2
Homework set#3
Midterm exam (Take
home)
Homework set#4
Homework set#5
Project report
Project presentations
(optional)
Final exam (inclass)*
Linkage of
Assignment
to Course
Learning
Outcome(s)
#
1, 2, & 6
2, 3, & 6
3, 4, & 6
1 – 4, & 6
Linkage of
Points Session Interim
Assignment
due
feedback?**
to MBA
Program
Learning
Outcome(s)1#
1&2
5
2
Yes
2
5
3
Yes
6
5
4
Yes
1&2
25(30)
5
No
4
5
1
1
2
2
1, 2, & 4
1, 2, & 4
–6
&6
-8
-8
1, 4, 6, & 7
1
5
5
25
5
6
7
8
8
Yes
Yes
Yes
Yes
25(30)
8
No
TOTAL
105
*This assignment will be used for assessment purposes.
Notes:
The assignments highlighted in bold font are group assignments.
The higher grade between the two exams will be weighted as 30 points.
Lowest grade on the homework sets will be dropped.
**For each of the assignments indicated as “Yes” I will provide one interim
feedback that may assist you in improving the work you turn-in for grading. Just
E-mail me the file(s) and I will provide feedback usually within 24 hours after I
receive it.
• The course learning outcomes (CLOs) are listed on pages 1 & 2 of this syllabus.
• The program learning outcomes (PLOs) are listed on the last page of this
syllabus.
•
•
•
•
Special instructions for turning in assignments
1. Please submit all work for this class in electronic form -- as an attachment to
an E-mail. Please name the (Word, Excel, Tableau, etc.) files as follows:
yourlastname-firstname-cluster-assignmentname (for example, the
submission will be named as Morozov-Michael-Burbank-Homework1.docx,
1
MBA Program-level Learning Outcomes (PLOs) are in the last page of this document.
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Morozov-Michael-Burbank-Case-Analysis.xlsx, etc.)
2. I will always try to provide feedback on most graded works before the next
graded work is due. This should help you use what you learn from one graded
work to improve the next graded work.
3. I like to grade your work without the knowledge of whose work I am grading.
To help me with this objectivity, for all the work you submit for grading in this
class, please include a title page and please put your name on the title page
only.
4. All assignments, other than project presentation and final exam, are due
before 6 pm of the class session.
Assignments should demonstrate that students have analyzed and are thinking critically
about the key issues in the course and relevant materials. Assignments should be
logically presented, adequately supported, and carefully reasoned.
Grades will be assigned according to the following Number/Letter Grading
Relationship Table:
4.0
3.7
3.3
3.0
2.7
2.3
A
AB+
B
BC+
96%
90%
87%
83%
80%
77%
-
100%
95%
89%
86%
82%
79%
2.0
1.7
1.3
1.0
0.7
0.0
C
CD+
D
DF
73% - 76%
70% - 72%
67% - 69%
63% - 66%
60% - 62%
below 60%
The following is the University of Redlands grading criteria.
3.7, 4.0
A
Outstanding
Student displayed exceptional grasp of the material, frequently with evidence of
intellectual insight and original thought.
2.7, 3.0, 3.3 B
Excellent
Work demonstrated a thorough grasp of the material with occasional errors and
omissions. Assignments were thoroughly and completely done, with careful attention to
detail and clarity, and with evidence of intellectual insight.
For Graduate Courses Only
Credit for a course graded below 2.0 cannot be applied toward a graduate
degree. See the “Graduate Grading Section” in the UR Catalog.
1.7, 2.0, 2.3 C
Acceptable
The quality of the work was acceptable, meeting minimal course standards, but not
exceptional. Performance on the examinations and other assignments was satisfactory
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and demonstrated that the student was keeping up with the material and attending to
detail.
0.7, 1.0, 1.3 D
Poor
The quality of the work was not always satisfactory, but overall was passing. Assigned
work was not always done, and when done was inadequate. Performance on
examinations and other work was generally weak with regard to understanding of
subject, proper formulations of ideas, and thoroughness.
0
F
Failing
A grade of "F" indicates that the student failed the course. The quality and quantity of
work was not of college level. A failing grade may be assigned for a variety of reasons
such as failure to complete course requirements as outlined in the syllabus, inability to
comprehend course material or ineptitude in dealing with it, consistently unsatisfactory
performance on examinations and/or assignments, or excessive absences.
Grade of “Incomplete”
An “incomplete” is not given for poor or neglected work. A grade of “incomplete” is to
be granted only for very special reasons and should occur only after a discussion
between faculty and student, initiated by the student. The decision of whether or not to
grant an incomplete is dependent on an emergency situation that prevents the student
from completing (on time) the work necessary for the course. An incomplete grade will
be converted to a permanent grade within eight weeks from the last night of the
course. This means that the instructor must turn in the grade to the Registrar no later
than the eighth week. Any incomplete work must be submitted to the instructor with
enough lead time for the instructor to evaluate the work and issue a grade change.
See U of R catalog for further guidance.
COURSE POLICIES
1) Please attend all class sessions and participate in class discussions.
Data driven decision-making is difficult to learn from the readings only. Lectures and
class discussions are an integral part of your learning experience.
2) If you have to miss a meeting, check with the instructor and classmates for missed
information and come fully prepared for the next session after having worked through
the materials you missed.
3) Please try to keep up with your assignments. This is a subject that cannot be learned
merely by listening to lectures or by reading assigned materials. You should work
through examples and get involved in discussions.
4) Please type all your weekly assignments (questions, problems, and case analysis)
and present them professionally.
5) Do not duplicate copyrighted software. This is software piracy.
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Time Management
Each 4-unit (Carnegie Unit) graduate course is the equivalent of 180 hours. Thus an 8week accelerated course is equivalent to 22.5 hours per week. Four hours are spent in
class each week; the course has been designed with the expectation that homework will
take about 18 hours per week. Although the amount of time that you spend studying
may depend upon the subject matter, a student should expect to spend an average of
18 hours each week.
Disability Services
A student with a documented disability who wishes to request an accommodation
should contact the School of Business Director of Student Services at (909) 748-8743 or
SBStudentServices@redlands.edu for assistance.
Policy for Cell Phones and Laptops in the Classroom
Cell phones will be off or on vibrate during all class sessions (excluding the dinner
break) to avoid distractions. Students should refrain from making or taking non-critical
personal or business cell phone calls during class sessions. If a phone call must be
taken, the student will exit the classroom.
Laptop use during class is limited to taking notes related to the lecture or class
discussions and/or researching material directly requested by the instructor. Internet
searches will not be used to support discussions or interaction during class time unless
specifically requested by the instructor. Students will not use cell phones and/or
laptops to surf the web, play games, read or generate personal or business email, or
text others in class or outside of class for any reason during class time.
Academic Honesty
The University of Redlands Policy on Academic Honesty will be strictly
adhered to and applied. The Procedures for Addressing Academic Honesty
are set forth in the University of Redlands Catalog. It is expected that all
students read and understand the Policy and the provisions outlined in the
Catalog.
The highest standards of academic conduct are required. This is particularly true for
the proper citation of course and research material in all written assignments. If you
did not actually collect the data or independently arrive at the idea presented, then a
proper citation must be used. Citations (in the form of parenthetical notes, endnotes or
footnotes) must be used for quoted or paraphrased text and any time you borrow an
idea from an author, the instructor, or your peers. Using someone else’s sentence or
organizational structure, pattern of argument and word choice, even if not exactly
similar in every respect, warrants citation. It is students’ responsibility to make sure
that their citations and quotation marks unambiguously highlight the ideas, words,
sentences, and arguments that they borrow from other sources. Paraphrasing is not
simply changing one or two words in a sentence; it completely reconstructs someone
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else’s idea in your own words. For guidelines on appropriate citation, quotation,
paraphrasing, and plagiarism, see materials provided by the Indiana University’s Writing
Tutorial Center at http://www.indiana.edu/~wts/pamphlets/plagiarism.shtml or by the
Purdue Online Writing Lab (OWL) at https://owl.english.purdue.edu/owl/resource/560/01/
Discussion with the instructor and your peers is encouraged before the composition of
written work; however, all written work, unless specified by the instructor, is to reflect
independent composition and revision. Students working on group or collaborative
assignments are expected to contribute equally to all tasks necessary for completion of
the assignment.
Students are expected to follow all written and verbal instructions provided by the
instructor with regard to written assignments, quizzes and/or exams. In addition to
plagiarism, other impermissible academic behavior includes, but is not limited to,
collaboration without instructor consent, falsifying research data, illicit possession of
exams, using study aids during exams, unauthorized communication about an
assignment or exam, handing in others’ work as your own, reusing assignments or
papers from other courses, and impeding equal access to educational resources by
other students.
Time constraints, the demands of work and family, failing to read the
University’s Policy on Academic Honesty, unintentional misuse of sources, or
a lack of preparation do not excuse academic dishonesty or otherwise
mitigate the appropriate penalty. Penalty for a first offense is at the
discretion of the instructor.
If a student is uncertain about appropriate methods of citation or has a question about
the academic honesty policy, it is his or her responsibility to seek guidance from the
instructor, a University official, or another reputable source.
Armacost Library Services
Any time you see the word “research” or related concepts in your syllabus or on an
assignment, there is a good chance that you will be required to locate, read, and
incorporate information into your coursework from someplace other than Google. The
University uses part of your tuition to pay for access to a wide variety of tools and
resources located beyond firewalls on the web, undiscoverable or inaccessible to the
casual searcher. Please visit library.redlands.edu/business in order to browse the many
resources available to you. All links requesting a login can be accessed by entering your
myRedlands ID (firstname_lastname) and the same, case-sensitive password you use
for all other University applications.
Feel free to use the navigation on the webpage to explore the resources provided for
many other disciplinary areas you may be interested in exploring. There are
descriptions of which databases contain various types of information, and pictures and
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demos on how to most effectively use them. If you have a question regarding the
research process or gaining access to or using a source, please contact your librarian,
Janelle Julagay, by email at janelle_julagay@redlands.edu or by phone at 909.748.8083
anytime. Drop-in office hours are listed on the website, and she is generally in the
library at the main campus Monday-Friday during normal business hours.
Code of Student Conduct
At the time of new-student orientation, all School of Business students were directed to
read the University’s Code of Student Conduct on the University’s website. If you need
access to the Code of Student Conduct at this time, please visit the University’s website.
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COURSE SCHEDULE (Planned)
DAY/
DATE
TOPIC
TO DO (before you attend that session)
1.
Introduction to the course:
Decision-making (DM),
data, patterns, and solution
Read: Diez et al -- Chapter 1
Davenport et al – Chapter 1
Ayers – Introduction & Chapter 1
Articles: 1-11
Tufte – pages 5-15
Explore:
Think and come prepared to discuss sampling and data collection in your organization.
Read: Diez et al -- Sections 2.1, 2.4, 2.5, & 3.1)
Davenport et al – Chapter 2
Ayers – Chapter 2
Articles: 13-14
Explore: Excel capabilities related to descriptive statistics (functions & graphs)
Think and come prepared to discuss how uncertainty is handled in your organization.
Read: Diez et al -- Chapter 1
Davenport et al – Chapter 3
Ayers – Chapter 3
Articles: 15-20
Tufte – pages 16-31
Explore: Explore descriptive analytics features of Tableau
Think and come prepared to discuss how patterns in data are explored in your organization.
Read: Diez et al – Sections 4.1 – 4.3
Davenport et al – None
Ayers – Chapter 4
Articles: 21-27
Explore: Explore features related to today’s topic in Excel
Think and come prepared to discuss how inferences using data are made in your organization.
Read: Diez et al – Sections 5.5, 6.4, & 7.1
Davenport et al – Chapter 4
Ayers – Chapter 5
Articles: 28-30
Explore: Explore features related to today’s topic in Minitab
Think and come prepared to discuss examples of single and multiple variable(s) inferences in
your organization.
Read: Diez et al – Sections 8.1, 8.2, & 8.4
Davenport et al – Chapter 5
Ayers – Chapter 6
Articles: 31
Explore: Explore features related to today’s topic in Minitab
Think and come prepared to discuss how single and multiple variable(s) inferences are made in
your organization..
Read: Diez et al -- None
Davenport et al – Chapter 6
Ayers – Chapter 7
Articles: 32-34
Explore: Explore features related to today’s topic in Minitab
Think and come prepared to discuss analytics use in your organization.
Read: Diez et al -- None
Davenport et al – Chapter 7
Ayers – Chapter 8
Articles: 35
Explore:
Think and come prepared to discuss ethical and social aspects of analytics use in your
organization.
10/29
2.
11/5
3.
11/12
4.
Uncertainty and patterns in
uncertainty
Data, variables, patterns,
and analytics
One variable inference
11/19
5.
Two variables inference
11/26
6.
Multiple variables inference
12/3
7.
Multiple variables inference
12/10
8.
12/17
Ethical aspects of datadriven DM
Project presentations.
WORK DUE
Homework set
#1
Homework set
#2
Homework set
#3
Take-home midterm exam;
Homework set
#4
Homework set
#5
Project
presentations;
Final exam (inclass)
Note: This schedule may be changed at any regularly scheduled class meeting depending on class requirements/progress.
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List of Articles: Please read the articles assigned for the session before coming to class
Session Article description
1. Buchanan, Leigh and Andrew O’Connell (2006). A brief history of decision making. Harvard Business Review
1
(January), 32-41.
2. Chottiner, Sherman, “Statistics: Toward a Kinder, Gentler Subject,” Journal of Irreproducible Results, Vol. 35,
No. 6.
3. Frick, Walter (2014). An introduction to data-driven decisions for managers who don’t like math. Harvard
Business Review (May), Accessed on 5/7/2018 from https://hbr.org/2014/05/an-introduction-to-data-drivendecisions-for-managers-who-dont-like-math
4. Hammond, John S, Keeney, Ralph L, and Howard Raiffa (2006). The hidden traps in decision making. Harvard
Business Review (January, 118-126.
5. Liebowitz, Jay (2015). Intuition-based decision-making: The other side of analytics. Analytics Magazine
(March/April), 38-43.
6. Lindsay, Matt (2017). The devil is in not having details, so get granular. Analytics Magazine
(January/February), 8-12.
7. Mehrotra, Vijay (2017). Problem-solving: Keep it real with gemba. Analytics Magazine (May/June), 12-15.
8. Michelman, Paul (2017). When people don’t trust algorithms. MIT Sloan Management Review (Fall), 11-13.
9. Mintzberg, Henry and Frances Westley (2001). Decision making: It’s not what you think. MIT Sloan
Management Review (Spring), 89-93.
10. Rigby, Tom (2017). Survey Sampling. Analytics Magazine (November/December), 44-49.
11. The Onion, “U.S. Population at 13,462,” April 5, 2000, retrieved on 2/9/13 from https://politics.theonion.com/us-population-at-13-462-1819565581
Pages
Access
10
Library
3
Handed-out
4
Follow link
9
Library
6
Magazine
5
Magazine
4
3
5
Magazine
Library
Library
6
2
Magazine
Follow link
(57)
2
12. Gould, Stephen Jay, “The Median Isn’t the Message,” Discover, June 1985. Retrieved on 5/7/2018 from
http://www.phoenix5.org/articles/GouldMessage.html
13. Harvard Management Update (2006). Five Guidelines for Using Statistics. (May 22). Retrieved on 5/17/2018
from https://hbswk.hbs.edu/archive/five-guidelines-for-using-statistics#1
14. Hymowitz, Carol, “IN THE LEAD: Grading systems force bosses to honestly assess performance,” The Wall
Street Journal, May 15, 2001.
4
Follow link
4
Follow link
3
Handed-out
15. Berinato, Scott (2016). Visualizations That Really Work. Harvard Business Review (June). Retrieved on
5/7/2018 from https://hbr.org/2016/06/visualizations-that-reallywork?referral=03758&cm_vc=rr_item_page.top_right
16. Davenport, Thomas D (2006). Competing on analytics. Harvard Business Review (January), 98-107.
17. Dhebar, Anirudh (1993). Managing the quality of quantitative analysis. Sloan Management Review (Winter), 6975.
18. Duarte, Nancy (2014). The quick and dirty on data visualization. Harvard Business Review (April 16).
Retrieved on 5/7/2018 from https://hbr.org/2014/04/the-quick-and-dirty-on-datavisualization?referral=03759&cm_vc=rr_item_page.bottom
19. Nickell, Joe Ashbrook (2002). Data mining: Welcome to Harrah’s. Business 2.0 (April), 48-54.
20. Paulos, John Allen, “FDA Caught Between Opposing Protesters,” in A Mathematician Reads the Newspaper,
Anchor Books, 1995.
4
Follow link
10
7
Library
Library
4
Follow link
7
2
Handed-out
Handed-out
21. Burling, Stanley, “Study Links Use of Lights in Youngsters’ Rooms and Future Nearsightedness. Does Baby’s
Night Light Lead to Bad Eyesight?” Philadelphia Inquirer, May 13, 1999.
22. Denman, Chip, “Blinding Insight,” Washington Post, May 8, 1996.
5
Handed-out
3
Handed-out
(11)
3
(34)
4
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23. Gallo, Amy (2016). A refresher on statistical significance. Harvard Business Review (February). Retrieved on
5/7/2018 from https://hbr.org/2016/02/a-refresher-on-statistical-significance
24. Gallo, Amy (2017). A Refresher on A/B Testing. Harvard Business Review (June 28), retrieved on 5/7/2018
from https://hbr.org/2017/06/a-refresher-on-ab-testing?referral=03758&cm_vc=rr_item_page.top_right
http://www.ou.edu/cls/online/lstd2323/pdfs/unit1_lamberth.pdf
25. Kleiman, Carol, “White, male M.B.A.s found to Profit Most from Job Moves,” The Philadelphia Inquirer, April 23,
2001.
26. Krueger, Alan B., “Better Pay for a Better College? Not Really,” NY Times, April 27, 2000.
27. Lamberth, John, “Driving While Black,” Washington Post, August 16, 1998, retrieved on 2/9/13.
7
Follow link
5
Follow link
4
Handed-out
4
2
Handed-out
Handed-out
28. Gallo, Amy (2015). A refresher on regression analysis. Harvard Business Review (November 4), Retrieved on
5/7/2018 from https://hbr.org/2015/11/a-refresher-on-regressionanalysis?referral=03758&cm_vc=rr_item_page.top_right
29. Lindsay, Matt (2016). A picture is worth a thousand words. A regression is worth a few pictures. Analytics
Magazine (July/August), 8-12.
30. Unknown (2015). Beware spurious correlations. Harvard Business Review (June), retrieved on 5/7/2018 from
https://hbr.org/2015/06/beware-spurious-correlations?referral=03759&cm_vc=rr_item_page.bottom
8
Follow link
5
Magazine
5
Follow link
6
31. Salter, Chuck (2007). She’s got their number. Fast Company (February), 100-108.
4
7
32. Kauffman, Elisabeth, and Crab Orchard, “Watch for Huddling Spiders,” Time, October 19, 1998, p. 6.
33. Samuelson, Douglas A (2011). Assessing the analysts. Analytics Magazine (September/October), 8-10.
34. Samuelson, Douglas A (2017). Storytelling: The write stuff. Analytics Magazine (May/June), 64-68.
1
3
5
8
35. Siegel, Eric (2013). The privacy pickle. Analytics magazine (November/December), 40-45.
6
(30)
5
(18)
Handed-out
(4)
Handed-out
Magazine
Magazine
(9)
Accessing articles (as listed in the last column of the table above):
Handed-out
Follow link
Magazine
Library
Magazine
(6)
These will be distributed as a package in class on the first day of class
Click on the link and access the article
click on analytics-magazine.org and find/access all the needed articles
Access the needed articles from the “Business Source Complete” database of the library
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Books (optional reading)
1. Browne, M.N. & S.M. Keeley. Asking the right questions. Upper Saddle River, NJ: Pearson,
2015. (Read chapters 9 through 11. You should have this book from an earlier course!)
2. Campbell, Stephen K, Flaws and Fallacies in Statistical Thinking, Englewood Cliffs, NJ: PrenticeHall, Inc., 1974.
3. Campbell, Steve, Statistics You Can’t Trust, Parker, CO: Think Twice Publishing, 1999.
4. Huff, Darrell, How to Lie With Statistics, New York, NY: W.W. Norton & Company, 1993.
5. Lewis, Michael. (2017). Moneyball: The art of winning an unfair game. New York, NY: W.H.
Norton & company, Inc.
6. Malkiel, Burton G, A Random Walk Down Wall Street, New York, NY: W.W. Norton & Company,
Inc., 2000.
7. O’Neill, Kathy. (2017). Weapons of math destruction: How big data increases inequality and
threatens democracy. New York, NY: Crown Publishing Group.
8. Tufte, Edward (2006). Beautiful evidence. Graphics Press: Cheshire, CT. (Chapters 6 & 7)
9. Tufte, Edward R. (2001). The visual display of quantitative information. Graphics Press LLC:
Cheshire, CT.
Other interesting articles
1. Gass, Saul I, “Model World: When is a Number a Number?” Interfaces, September-October 2001,
pp. 93-103. [on Moodle]
2. Ittner, Christopher D, and David F. Larcker, “Coming up Short,” Harvard Business Review,
November 2003, p. 88-95. [from Business source complete – Library database]
3. Morrel-Samuels, Palmer, “Getting the Truth into Workplace Surveys,” Harvard Business Review,
February 2002, 111-118. [from Business source complete – Library database]
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MBA Program Learning Outcomes
Revised by the School of Business faculty at an Assessment Retreat on March 3, 2017 and subsequently at the School of Business Faculty
Assembly on April 12, 2017; Reviewed by School of Business Curriculum Committee in April 2017, and approved by School of Business Faculty
Assembly on May 17, 2017.
Upon completion of the MBA Program, graduates will:
Application of Functional Business Knowledge
PLO 1. Use and apply business knowledge from disciplines such as accounting, finance,
marketing, management, information systems, operations, and global business to generate/create
business solutions.
Communication, Leadership and Teamwork Skills
PLO 2. Illustrate persuasive communication using written, oral, and analytical expression.
PLO 3. Apply managerial skills to collaborate and lead effectively.
Critical Reasoning
PLO 4. Evaluate societal, economic, environmental, spatial, and ethical implications of business
decisions holistically.
Multi-Level Perspective
PLO 5. Synthesize functional knowledge across disciplines to solve business problems to aid
strategic planning and decision-making in a rapidly changing environment.
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