Discussion: Data Driven Decision Making Analysis Project

Discussion: Data Driven Decision Making Analysis Project ORDER NOW FOR CUSTOMIZED AND ORIGINAL ESSAY PAPERS ON Discussion: Data Driven Decision Making Analysis Project i have attached the question that need to be answered in the pdf, answer them by order and number them another doc is my syllabus.University of Redlands BUAD631 Data Driven Decision Making Analysis Project buad_631_data_driven_decision_making__fall_2019.docx statistic_data_analysis. 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. Discussion: Data Driven Decision Making Analysis Project 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: Identify organizational opportunities for data-driven decision-making that create value. Define each opportunity clearly and accurately, and articulate why your definition is right. University of Redlands BUAD631 Data Driven Decision Making Analysis Project Specify the data needs for each opportunity identified, including its collection, and justify how that data is relevant for the context. Identify and justify the right data analyses that could support decision-making in the context of the opportunity and data needs specified. Perform relevant analyses using the right business analytics (BA) tools. Effectively communicate the findings of the analyses performed to the decision maker(s). Evaluate and articulate the value delivered by data-driven decision-making, for each opportunity identified. Evaluate the ethical implications associated with BA use in organizations to assist decision making. COURSE OBJECTIVES Evaluate business analytics, its characteristics, and value creation Identify organizational opportunities for BA use Identify data and measurement requirements for organizational problems Compare and contrast exploratory and confirmatory data analyses Articulate fit between data and analyses Compare and contrast descriptive and predictive statistical analyses Describe the need for data visualization Interpretation and communication of analyses results Use different tools for analyses Evaluate ethical implications of data-driven decision-making LEARNING MATERIALS AND RESOURCES REQUIRED Texts : 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 . Tufte, Edward (1997). Visual and statistical thinking: Displays of evidence for making decisions. Cheshire, CT: Graphics Press. ISBN-13: 978-0961392130 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: 978-0553384734 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.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. Course Assessment and Grading Your grade will be determined as follows: Assignment Linkage of Assignment to Course Learning Outcome(s) # Linkage of Assignment to MBA Program Learning Outcome(s) [1] # Points Session due Interim feedback? ** Homework set#1 1, 2, & 6 1 & 2 5 2 Yes Homework set#2 2, 3, & 6 2 5 3 Yes Homework set#3 3, 4, & 6 6 5 4 Yes Midterm exam (Take home) 1 – 4, & 6 1 & 2 25(30) 5 No Homework set#4 4 – 6 2 5 6 Yes Homework set#5 5 & 6 2 5 7 Yes Project report 1 – 8 1, 2, & 4 25 8 Yes Project presentations (optional) 1 – 8 1, 2, & 4 5 8 Yes Final exam (in-class)* 1, 4, 6, & 7 1 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. University of Redlands BUAD631 Data Driven Decision Making Analysis Project 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, 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 A 96% – 100% 2.0 C 73% – 76% 3.7 A- 90% – 95% 1.7 C- 70% – 72% 3.3 B+ 87% – 89% 1.3 D+ 67% – 69% 3.0 B 83% – 86% 1.0 D 63% – 66% 2.7 B- 80% – 82% 0.7 D- 60% – 62% 2.3 C+ 77% – 79% 0.0 F below 60% The following is the University of Redlands grading criteria. 3.7, 4.0 A OutstandingStudent displayed exceptional grasp of the material, frequently with evidence of intellectual insight and original thought. 2.7, 3.0, 3.3 B ExcellentWork 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 AcceptableThe quality of the work was acceptable, meeting minimal course standards, but not exceptional. Performance on the examinations and other assignments was satisfactory and demonstrated that the student was keeping up with the material and attending to detail. 0.7, 1.0, 1.3 D PoorThe 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 FailingA 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. University of Redlands BUAD631 Data Driven Decision Making Analysis Project 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. Time Management Each 4-unit (Carnegie Unit) graduate course is the equivalent of 180 hours. Thus an 8-week 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 [email protected] 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 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. Discussion: Data Driven Decision Making Analysis Project. 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 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 [email protected] 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.Discussion: Data Driven Decision Making Analysis Project 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. COURSE SCHEDULE (Planned) Day/ Date Topic TO DO (before you attend that session) WORK DUE 1. 10/29 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. 2. 11/5 Uncertainty and patterns in uncertainty 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. Homework set #1 3. 11/12 Data, variables, patterns, and analytics 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. Homework set #2 4. 11/19 One variable inference 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. Discussion: Data Driven Decision Making Analysis Project Homework set #3 5. 11/26 Two variables inference 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. Take-home mid-term exam; 6. 12/3 Multiple variables inference 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.. Homework set #4 7. 12/10 Multiple variables inference 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. Homework set #5 8. 12/17 Ethical aspects of data-driven DMProject presentations. 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. Project presentations;Final exam (in-class) Note: This schedule may be changed at any regularly scheduled class meeting depending on class requirements/progress. List of Articles: Please read the articles assigned for the session before coming to class Session Article description Pages Access 1 1. Buchanan, Leigh and Andrew O’Connell (2006). A brief history of decision making. Harvard Business Review (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-driven-decisions-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). Discussion: Data Driven Decision Making Analysis Project 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/u-s-population-at-13-462-1819565581 10 3 49 6 5 435 62 ( 57) Library Handed-out Follow linkLibrary Magazine Magazine MagazineLibraryLibrary MagazineFollow link 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 4 3 (11) Follow link Follow link Handed-out 3 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-really-work?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), 69-75.18.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-data-visualization?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. 4 107 4 72 (34) Follow link LibraryLibrary Follow link Handed-outHanded-out 4 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, 1996.23. 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. 5 37 54 42 (30) Handed-out Handed-outFollow link Follow linkHanded-out Handed-outHanded-out 5 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-regression-analysis?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 Discussion: Data Driven Decision Making Analysis Projecthttps://hbr.org/2015/06/beware-spurious-correlations?referral=03759&cm_vc=rr_item_page.bottom 8 5 5 (18) Follow link Magazine Follow link 6 31. Salter, Chuck (2007). She’s got their number. Fast Company (February), 100-108. 4 (4) Handed-out 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. 135 (9) Handed-outMagazineMagazine 8 35. Siegel, Eric (2013). The privacy pickle. Analytics magazine (November/December), 40-45. 6 (6) Magazine Accessing articles (as listed in the last column of the table above): Handed-out These will be distributed as a package in class on the first day of classFollow link Click on the link and access the articleMagazine click on analytics-magazine.org and find/access all the needed articlesLibrary Access the needed articles from the “Business Source Complete” database of the library. Discussion: Data Driven Decision Making Analysis Project Books (optional reading) 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!) Campbell, Stephen K, Flaws and Fallacies in Statistical Thinking , Englewood Cliffs, NJ: Prentice-Hall, Inc., 1974. Campbell, Steve, Statistics You Can’t Trust , Parker, CO: Think Twice Publishing, 1999. Huff, Darrell, How to Lie With Statistics , New York, NY: W.W. Norton & Company, 1993. Lewis, Michael. (2017). Moneyball: The art of winning an unfair game . New York, NY: W.H. Norton & company, Inc. Malkiel, Burton G, A Random Walk Down Wall Street , New York, NY: W.W. Norton & Company, Inc., 2000. O’Neill, Kathy. (2017 ). Weapons of math destruction: How big data increases inequality and threatens democrac New York, NY: Crown Publishing Group. Tufte, Edward (2006). Beautiful evidence . Graphics Press: Cheshire, CT. (Chapters 6 & 7) Tufte, Edward R. (2001). The visual display of quantitative information . Graphics Press LLC: Cheshire, CT. Other interesting articles Gass, Saul I, “Model World: When is a Number a Number?” Interfaces , September-October 2001, pp. 93-103. [on Moodle] Ittner, Christopher D, and David F. Larcker, “Coming up Short,” Harvard Business Review , November 2003, p. 88-95. [from Business source complete – Library database]. Discussion: Data Driven Decision Making Analysis Project Morrel-Samuels, Palmer, “Getting the Truth into Workplace Surveys,” Harvard Business Review , February 2002, 111-118. [from Business source complete – Library database] 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.University of Redlands BUAD631 Data Driven Decision Making Analysis Project 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 Illustrate persuasive communication using written, oral, and analytical expression. Apply managerial skills to collaborate and lead effectively. Critical Reasoning Evaluate societal, economic, environmental, spatial, and ethical implications of business decisions holistically. Discussion: Data Driven Decision Making Analysis Project Multi-Level Perspective Synthesize functional knowledge across disciplines to solve business problems to aid strategic planning and decision-making in a rapidly changing environment. [1] MBA Program-level Learning Outcomes (PLOs) are in the last page of this document. Discussion: Data Driven Decision Making Analysis Project Get a 10 % discount on an order above $ 100 Use the following coupon code : NURSING10

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