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La Salle University/School of Business
MBA 820 Information Technology for Decision Making
Course Information MBA 820-HA Fall 2016 3 credits Course Dates: August 29 – October 21, 2016 Class Location: Online and La Salle School of Business Building 382 Class Meeting Times: Monday, 6:15 – 8:55 pm. Pre-requisites: While there are no academic pre-requisites, you must complete the self-paced Student Guide to Canvas and be prepared to participate in a fully hybrid course.
Instructor Information Instructor: Madjid Tavana, Ph.D. Professor and Chair Business Systems and Analytics Department Email: [email protected] Phone: (215) 951-1129 Fax: (267) 295-2854 Web: http://tavana.us Skype: madjidtavana Office Location: La Salle School of Business Building 635 Office Hours: By Appointment Only Best way to contact me: Through Email
Course Structures Course Description
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This course is about the manager’s responsibilities for problem solving and decision making, and those areas in which information technology can be used to gain the insight needed to support selection of decision alternatives. Students learn about the role of data, information, and knowledge in managerial problem solving and decision making. Transactional processing and database management systems (DBMS) are used to store, manage, and retrieve data in organizations. Decision support system (DSS) tools and technologies (such as natural language programming and influence diagramming) are used to organize data into information for decision analytics. Expert systems (ES) are used to synthesize information into knowledge for knowledge management. Students are required to use DBMS, DSS and ES software packages in a hands-on environment.
Student Learning Outcomes Upon completion of prescribed work for this course, the student should be able to:
Understand the emerging technological issues facing managers.
Understand the value of data, information, and knowledge to organizations.
Utilize information technology tools to design operational, managerial, and strategic systems.
Utilize a series of decision analytics tools in a hands-on environment.
Design and develop Database Management Systems, Management Information Systems, Decision Support Systems, and Expert System in support of the organizational decision making and problem solving.
Understand when and how Management Support Systems may be used to complement more analytic decision making frameworks.
Work in interdisciplinary groups on real-world big data and decision analytic problems.
Course Overview Computers are data, information, and knowledge processing machines. Billions of dollars are spent every year collecting, storing, processing, and retrieving data; whether it is for financial, marketing, or operational analysis and decision making. Data is a collection of bits, bytes, and characters. Raw data by itself has little or no use. No decision can be made without organizing and synthesizing data. Information is derived from organizing data and knowledge is extracted from synthesizing information. The evolution of data and its relationship with information and knowledge can be visualized as a pyramid of interdependent layers on the top of each other with data at the bottom, information in the middle, and knowledge on the top.
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Computers are used to collect, store, and process (organize and synthesize) data, information, and knowledge. Data is collected by a Transactional Processing System (TPS) and stored in a Database Management System (DBMS). Data is organized into information by an Information System (IS) and used in a Decision Support System (DSS) for decision making. Information is organized, meaningful, and useful data. Information is synthesized into knowledge by a Knowledge Based system (KBS) and used in an Expert Systems (ES) for problem solving.
Consider the scenario depicted in the pyramid below where a retailer collects data
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on their revenues and expenses every time a purchase is made or every time an expense is paid. That data is then stored in the retailer’s DBMS. The accounting department can retrieve and organize this data into meaningful information by way of a report such as income statement. The operations department can use this information in a DSS to assess profitability. The finance department can synthesize this information in a KBS and make expansion or contraction decisions. This course focuses on data, information, and knowledge by utilizing a series of tools to develop Database Management Systems, Management Information Systems, Decision Support Systems, and expert System in support of the decision making and problem solving processes in a hands-on environment.
Instructional Methods This hybrid course uses lectures, videos, assignments, case reports, in-class discussion, an optional final exam, and an optional project to foster learning. Your participation is key to your success. Discussing issues, raising and answering questions, and offering critical analysis are all ways you can show full engagement in this course. See more specific grading information in the Grading Methods and Evaluation section of the syllabus.
Instructional Technologies You will find all of our course materials on Canvas, which is accessible from the Tools menu in the mylasalle portal. We will use GoToTraining for our synchronous
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sessions. Please check Canvas for details about those sessions. All email communication should be conducted using your official La Salle email address. To complete the case reports, you must have access to the following software:
MS PowerPoint
MS Access
MS Excel
Natural Language Programming at D-code.us
Influence Diagramming at D-cide.us
Knowledge Engineering at B-wise.us
Course Materials Required readings
All readings for this course will be available on Canvas. Major topics include:
The World of Information Systems and Decision Sciences
Transacitonal Processing Systems and Database Management Systems
Decision Support Systems and Spreadsheets
Decision Support Systems and Natural Language Programming
Decision Support Systems and Influence Diagramming
Knowledge-Based Systems
Required materials In order to participate in this hybrid course, you must have access to a:
laptop or desktop (Mac, Windows PC, or Chromebook) with an up-to-date Windows operating system. Students must have access to Windows Operating system regardless of computer operating system.
Canvas-supported Internet browser (Firefox or Chrome are recommended).
high-speed Internet connection.
headset with a microphone for synchronous sessions. For more information about what you need for Canvas, including information about screen reader requirements, please visit review their computer specifications. Keep in mind that while you can use mobile devices to access course content and online meetings, you may find that the Canvas apps lack the features and functionality you need to participate fully.
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Course Schedule This course begins on August 29 and ends on October 21, 2016. The course week begins on Monday at 12:00AM, EST, and ends on Friday at 11:59PM, EST. The last day to withdraw from this course is October 3, 2016.
Direct Contact Hours
Activity Hours
Videos 1-3 Hours/Week
Assignments 3-5 Hours/Week
Case Reports 4-6 Hours/Week
Weekly Schedule
Week/Date Topic Readings/Videos Activities/Assignments
Week 1: August 29
Course Overview and Introduction to Decision Analytics
Video Discussion: Dan Gilbert - Why we make bad decisions? (dangilbert.mp4)
Watch this TED Talk by Dan Gilbert video prior to our week 1 class.
Week 2: September 5
Labor Day Holiday: No Class
Week 3: September 12
Database Managament Systems and Access
Lecture Notes: Database Management Systems (SA-DBM-S.pdf) Lecture Notes: Database Management Systems - ACCESS Applications (SA-ACC-H.pdf) Video: Access Demo (acc.mp4) Sample Case Report: PowerPoint (Access.ppt) and Access file (Access.accdb) Video Discussion: Adam Savage:Problem Solving - How I Do It? (adamsavage.mp4)
Classroom Exercise(s): Happyville Casino Online Assignment(s): Due Prior to Week 4 Class
Explorer Healthcare Great Times Summer
Camp Jimmy “the Body Bag”
Biggs Online Access Case Report: Due Prior to Week 4 Class
Week 4: September 19
Decision Support Systems and Traditional
Lecture Notes: Decision Support Systems and EXCEL (DS-EXL-H.pdf)
Classroom Exercise(s): American Appliances and The Heinrich Company
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Week/Date Topic Readings/Videos Activities/Assignments
Spreadsheet Modeling (Excel)
Video: Excel Demo (xls.mp4) and Heinrich (hrich.mp4) Sample Case Report: PowerPoint (Excel.ppt) and Excel file (Excel.xls) Video Discussion: Hans Rosling - The best stats you've ever seen (hansrosling1.mp4) Video Discussion: Hans Rosling - 200 Countries, 200 Years - The Joy of Stats (hansrosling2.mp4)
Online Assignment(s): Due Prior to Week 5 Class
Francois Stealth Titanic Cruise Company Planters Nut Company Built-4-U Golden Palace Casino
Online Excel Case Report: Due Prior to Week 5 Class
Week 5: Sepetmber 26
Decision Support Systems and Natural Language Programming (D-code)
Lecture Notes: Decision Support Systems and Decision Analytics - Natural Language Programming (DS-COL-H) Lecture Notes: Decision Support Systems and Decision Analytics - D-code Applications (DS-COP-H) Video: Natural Language Programming (nlp.mp4) Sample Case Report: PowerPoint (Dcode.ppt) and D-code file (Dcode.nlp) Video Discussion: David McCandless - The beauty of data visualization (davidmccandless.mp4)
Classroom Exercise(s): Keystone Corporation and The Prolog Corporation Online Assignment(s): Due Prior to Week 6 Class
Starship Technologies Dr. Warped Jungle Jim
Online D-code Case Report: Due Prior to Week 6 Class
Week 6: October 3
Startegic Information and Management Support Systems (SAS)
Lecture Notes: André and Angelique – Problem (poitiers.pdf and poitiers.xls) Lecture Notes: André and Angelique – Solution (poitiers--solution.pdf and poitiers--solution.xls)
Classroom Exercise(s): André and Angelique Online SAS Project Report: Due Prior to Week 8 Class Banking in Emerging Economies
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Week/Date Topic Readings/Videos Activities/Assignments
Project Data: bank.xlsx Sample Project Report: project.pdf
Week 7: October 10
Decision Support Systems and Influence Diagramming (D-cide)
Lecture Notes: Decision Support Systems and Decision Analytics - Influence Diagramming and D-cide Applications (DS-DCD-H) Video: D-cide (mod.mp4) Sample Case Report: PowerPoint (Dcide.ppt) and D-cide file (Dcide.mod) Video Discussion: Kevin Slavin - How algorithms shape our world (kevinsalvin.mp4)
Classroom Exercise(s): The Procom Corporation and Thrills R Us Online Assignments: Due Prior to Week 8 Class
Parrot Club Southwest Airline Pick Your Own Jungle Jim
Online D-cide Case Report: Due Prior to Week 8 Class
Week 8: October 17
Knowledge-Based/Expert Systems (B-wise)
Lecture Notes: Knowledge Engineering and Expert Systems B-wise (DS-BWL-H) Lecture Notes: Knowledge Engineering and Expert Systems - B-wise Applications (DS-BWP-H) Video: Knowledge-Based Systems (kbs.mp4) Sample Case Report: PowerPoint (Bwise.ppt) and B-wise file (Bwise.kbs) Video Discussion: Kenneth Cukier - Big data is better data (kennethcukier.mp4)
Classroom Exercise(s): Check Cashing Problem and College Hill Tavern Online Assignments: Due at the end of the week
Police Academy Corleone Family La Boutique Gateway Computers
Online B-wise Case Report: Due at the end of the week
Grading Methods and Evaluation Assessment Methods:
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Access Case Analysis (20% without exam/project or 12% with exam/project): A PowerPoint Presentation with Problem Description, Initial Table, Split Tables, Relationships, and Query (see sample case on Canvas for more details). Excel Case Analysis (20% without exam/project or 12% with exam/project): A PowerPoint Presentation with Problem Description, Excel Spreadsheet, Solver Formulation, and Optimal Solution (see sample case on Canvas for more details). D-code Case Analysis (20% without exam/project or 12% with exam/project): A PowerPoint Presentation with Problem Description, Influence Diagram, Model Lines, and Spreadsheet Solution (see sample case on Canvas for more details). D-cide Case Analysis (20% without exam/project or 12% with exam/project): A PowerPoint Presentation with Problem Description, Influence Diagram Screenshot, and Spreadsheet Solution (see sample case on Canvas for more details). B-wise Case Analysis (20% without exam/project or 12% with exam/project): A PowerPoint Presentation with Problem Description, A listing of Factors and Choices, and Decision Tree Screenshot (see sample case on Canvas for more details). Optional Final Exam (40% of the Final Grade): A hands-on exam focusing on the systems described throughout the course (Open Book and Open Notes). Optional Project (40% of the Final Grade): An individual project using Strategic Assessment System. Project details are available on Canvas.
Assignments All assignments and cases should be submitted through Canvas. Assignments are not graded, but there is a penalty associated with the late or missing assignments (see Professionalism section of the course syllabus for more details).
Case Reports Case reports are problems created by you and solved by you. They could be interesting real-life or hypothetical problems. All case reports are evaluated according to the following five criteria (see sample cases):
Originality/creativity: How creative are you and how interesting is your problem?
Presentation: How clean and professional looking is your PowerPoint slideshow?
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Problem description: How well have you descried your problem and how much detail is provided?
Complexity: How complex is your problem? Is it a straight-forward problem or difficult problem to solve?
Solution: Is your solution correct and how well are you presenting your solution?
Grading Criteria Point Scale 0 1 2 3 4 5 6 7 8 9 10
Originality/Creativity
Presentation
Problem Description
Complexity
Solution
Overall score
Video Discussions Students are expected to watch videos on most weeks and participate in related discussions with their classmates on Canvas. Discussions are not graded, but there is a penalty associated inactive participation (see Professionalism section of the course syllabus for more details).
Grading Scale The student earns points which will be calculated on a 100 point scale. There is no extra credit. The following are the cutoff points for each grade:
Letter Grade
Description Numerical Grade
A Indicates the demonstration of a superior level of competency 93.0-100
A- Indicates the demonstration of a very good level of competence 90.0-92.9
B+ Indicates the demonstration of a good level of competency 87.0-89.9
B Indicates the demonstration of an average, satisfactory level of competency
83.0-86.9
B- Indicates the demonstration of a less than average level of competency
80.0-82.9
C Indicates a below average and marginally satisfactory level of competency
70.0-79.9
F Indicates failure to demonstrate a satisfactory level of competency 00.0-69.9
Professionalism (Course Policies) The majority of this class is engaged in full time employment or is seeking to enhance their employment opportunities. I would like to help you enhance your career by asking you to behave as you would in a professional setting. To do this,
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I suggest that you think of me as your manager, and our class sessions as regularly scheduled meetings. Following are some of the things that professionals would never do:
Miss a regularly scheduled meeting. People can’t always attend all meetings, but professional courtesy dictates informing the manager ahead of time when a conflict has arisen. If you do need to miss class due to a conflict, please send me an email or call me before class and leave a message on my voice mail system.
Arrive late for a meeting without explaining the circumstances ahead of time. I expect you to be on time for class, not saunter in five or ten minutes late.
Leave early from a meeting, without explaining the circumstances ahead of time. If you need to leave early, let me know beforehand.
Sleeping or being inattentive during a meeting. Can you imagine someone sleeping during a meeting with his/her manager? In my professional experience, such behavior is never tolerated.
Being unprepared or being unwilling to participate in class and group discussions. I expect you to come prepared for class and participate actively in class discussions and your group project.
To encourage development of professional habits, the following reductions will be applied to the final grade:
Each missing face-to-face class (10%) Each late assignment or case report (5%) Each missing assignment (10%) Inactive participation in video discussions (5%)
Changes to the Syllabus This syllabus is a guide and every attempt is made to provide an accurate overview of the course. However, circumstances and events may make it necessary for me to modify the syllabus during the semester and may depend, in part, on the progress, needs, and experiences of the students. I will give notice when changes to the syllabus are made.
Essential University Policies La Salle University is a community of shared academic values, foremost of which is a strong commitment to intellectual honesty, honorable conduct, and respect for others. In order to meet these values, students are expected to adhere to the highest standards of academic integrity. These standards are embodied in the La Salle University Academic Integrity Policy and are available in links below.
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You will find links to these important polices in our Canvas course. Click on Help at the bottom of the global navigation menu or click on Getting Help in the course navigation menu.
American Disability Act of 1990
Academic Integrity Policy
Student Guide to Rights and Responsibilities
Student Responsibility Statement Academic standards and rigor in online and hybrid courses are equivalent to those for traditional courses offered at La Salle. Student success in an online or hybrid course is dependent on the individual student’s intrinsic motivation, ability to be a self-regulated learner, and technology skills. Online and hybrid courses require you to take initiative to complete coursework on your own and in groups without the direct supervision of the professor. You will be expected to study and complete assignments independently as well as logging into the Canvas course on a daily basis to participate in online discussions, group work, and synchronous meetings regularly. If you encounter any technical difficulties when trying to access coursework online, you should notify the instructor immediately as well as contact the La Salle University Helpdesk for technical support.
Communication Policy The instructor will periodically send out emails and announcements to remind students of deadlines or readings, to provide details on assignments and examinations, and to tell students about University events of interest. It is the student’s responsibility to check their University emails on a regular basis to stay informed. There will be no allowances made for a failure to read your official University email. Similarly, the student must also be certain that they are receiving appropriate notifications from Canvas. The instructor will make every effort to respond to your messages within 24 – 48 hours during the work week and within 48 hours for emails sent on the weekend. It is the expectation that you will adhere to these timeframes as well when communicated with the instructor or classmates about matters pertaining to your coursework in this class.
University Resources
La Salle University Helpdesk The La Salle University Helpdesk can assist you with technology issues you may encounter. You can reach the Helpdesk by calling 215.951.1860, emailing [email protected], or by clicking on Helpdesk from the Tools menu in the
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mylasalle portal. Check out the Technology at La Salle webpage for more information about technology help and support.
The Connelly Library The experts at the Connelly Library can help you focus your search to find the best resources for your research projects. They can also help you find background information to help you better understand the content in your courses. This self-paced guide provides a quick overview of the library resources and services used for college-level research. Get in touch by phone at 215.951.1287 or by email.
Academic & Learning Support Services Need help with your coursework? Get in touch with Academic and Learning Support Services. They offer subject tutoring, writing support, peer mentoring, and study strategy and time management workshops.
Counseling Center Need to talk to someone confidentially about an emotional, behavioral, or health issue? Check out the Student Counseling Center*. Services include:
The Student Counseling Center ( 215.951.1355)
The Alcohol and Other Drug Education Center (215.951.1357)
The Student Health Center (215.951.1565) * Full time Undergraduate Students and Residential Graduate students are eligible to utilize these services. If you are a part-time student or non-residential Graduate student, you may call the above resources for suggested referrals.
