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Graduate Course Proposal Form Submission Detail - ACG5555

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Current Status: SCNS Liaison Notified of Graduate Council Approval - 2016-04-21
Campus: Tampa
Submission Type: New
Course Change Information (for course changes only):
Comments: In review - Elective for MAcc, MBA. States repeat twice - diff how diff two times or remove repeat. Emailed Fac 3/11/16. Updated number from 5505 to 5555 per fac req. Not Repeatable.; To USF Sys 4/21/16; to SCNS after 4/28/16


  1. Department and Contact Information

    Tracking Number Date & Time Submitted
    5353 2016-01-04
     
    Department College Budget Account Number
    School of Accountancy BA 1402
     
    Contact Person Phone Email
    Uday Murthy 8139746516 umurthy@usf.edu

  2. Course Information

    Prefix Number Full Title
    ACG 5555 Analytics in Accounting

    Is the course title variable? N
    Is a permit required for registration? Y
    Are the credit hours variable? N
    Is this course repeatable? N
    If repeatable, how many times? 0

    Credit Hours Section Type Grading Option
    3 O - Other R - Regular
     
    Abbreviated Title (30 characters maximum)
    Analytics in Accounting
     
    Course Online? Percentage Online
    O - Online (100% online) 100

    Prerequisites

    ACG 4632 or its equivalent, or admission to the Muma COB MBA program

    Corequisites

    Course Description

    This course deals with analytics, understood as the discovery and communication of meaningful patterns. The focus is on accounting applications of analytics, after first understanding statistical techniques and data manipulation processes and tools.


  3. Justification

    A. Please briefly explain why it is necessary and/or desirable to add this course.

    Needed to compete with national trends

    B. What is the need or demand for this course? (Indicate if this course is part of a required sequence in the major.) What other programs would this course service?

    This course meets the needs of employers of Accountancy graduates who are increasingly demanding that graduates have good analytical skills. Analytics and Big Data are increasingly being leveraged in the accounting profession for generating fresh insights from massive quantities of data. This course will equip students with the skills needed to be competitive in the workforce of tomorrow.

    C. Has this course been offered as Selected Topics/Experimental Topics course? If yes, how many times?

    No

    D. What qualifications for training and/or experience are necessary to teach this course? (List minimum qualifications for the instructor.)

    PhD in Accounting, with experience teaching accounting information systems at the graduate level


  4. Other Course Information

    A. Objectives

    The course learning objectives are organized around foundational principles, descriptive analytics, predictive analytics, prescriptive analytics, and applications of analytic techniques in accounting domains. The specific objectives of this course are indicated below in each category:

    1. Foundational: Principles of data analytics, analysis versus analytics, and the various categories of analytics—descriptive, prescriptive, and predictive.

    2. Foundational: basic descriptive statistical measures for describing, summarizing, and exploring large data sets.

    3. Foundational: Business process modeling and data modeling techniques for understanding associations between related accounting data sets.

    4. Descriptive: Using spreadsheet tools for analyzing and summarizing data sets.

    5. Descriptive: Using data mining and analytic techniques to identify anomalies and risk factors in underlying accounting data.

    6. Predictive: Exploratory multivariate statistics and inferential statistics for understanding patterns in accounting data and for developing predictive models.

    7. Predictive: Using data analysis tools and languages such as SQL to join and query related accounting data sets to draw meaningful insights for decision making.

    8. Prescriptive: Linear optimization techniques, Monte Carlo simulations and other stochastic modeling techniques on accounting data sets.

    9. Descriptive, predictive, and prescriptive: How to create interactive data visualizations of data to provide clear insights into associations, relationships, outliers and other patterns in accounting datasets.

    10. Applications in accounting: How analytic techniques can be applied to generate insights in different areas of accounting, including financial accounting, managerial accounting, auditing, and taxation.

    B. Learning Outcomes

    After completing this course the student will have demonstrated the ability to:

    1. Explain the principles of data analytics, distinguish between analysis and analytics, and describe the various categories of analytics—descriptive, prescriptive, and predictive.

    2. Generate basic descriptive statistical measures for describing, summarizing, and exploring large data sets.

    3. Apply business process modeling and data modeling techniques for understanding associations between related accounting data sets.

    4. Use spreadsheet tools for analyzing and summarizing data sets.

    5. Use data mining and analytic techniques to identify anomalies and risk factors in underlying accounting data.

    6. Apply exploratory multivariate statistics and inferential statistics for understanding patterns in accounting data and for developing predictive models.

    7. Use data analysis tools and languages such as SQL to join and query related accounting data sets to draw meaningful insights for decision making.

    8. Apply linear optimization techniques, Monte Carlo simulations and other stochastic modeling techniques on accounting data sets.

    9. Create interactive data visualizations of data to provide clear insights into associations, relationships, outliers and other patterns in accounting datasets.

    10. Apply analytic techniques to generate insights in different areas of accounting, including financial accounting, managerial accounting, auditing, and taxation.

    C. Major Topics

    Introduction, principles of data analytics

    Business process modeling techniques

    Spreadsheet functionality for grouping, summarizing, and aggregating data

    Descriptive statistics

    Data mining and statistical techniques for identifying anomalies

    Multivariate statistics and inferential statistics

    Data analysis tools and SQL

    Introduction to the R programming

    Linear optimization techniques, Monte Carlo simulations and other stochastic modeling techniques with non-discrete inputs and outputs

    Creating interactive data visualizations using Tableau

    Analytics in accounting: Applications in financial accounting

    Analytics in accounting: Applications in managerial accounting

    Analytics in accounting: Applications in auditing

    Analytics in accounting: Applications in taxation

    D. Textbooks

    1. James R. Evans, Business Analytics, 2nd Ed., Pearson

    2. Edward Tufte, The Visual Display of Quantitative Information, 2nd Edition; http://www.edwardtufte.com/tufte/books_vdqi

    E. Course Readings, Online Resources, and Other Purchases

    1. Tableau learning site: http://www.tableau.com/learn

    2. Getting started with R: http://scs.math.yorku.ca/index.php/R:_Getting_started_with_R

    3. EY Academic Resource Center (EYARC)

    F. Student Expectations/Requirements and Grading Policy

    Two exams, each worth 25%

    Eight quizzes, each worth 1.25%

    Four assignments, each worth 7.5%

    Participation, worth 5%

    G. Assignments, Exams and Tests

    Four assignments using different analytical tools (e.g., spreadsheets, Tableau, R). Two tests -- one midterm and one final exam.

    H. Attendance Policy

    Course Attendance at First Class Meeting – Policy for Graduate Students: For structured courses, 6000 and above, the College/Campus Dean will set the first-day class attendance requirement. Check with the College for specific information. This policy is not applicable to courses in the following categories: Educational Outreach, Open University (TV), FEEDS Program, Community Experiential Learning (CEL), Cooperative Education Training, and courses that do not have regularly scheduled meeting days/times (such as, directed reading/research or study, individual research, thesis, dissertation, internship, practica, etc.). Students are responsible for dropping undesired courses in these categories by the 5th day of classes to avoid fee liability and academic penalty. (See USF Regulation – Registration - 4.0101,

    http://usfweb2.usf.edu/usfgc/ogc%20web/currentreg.htm)

    Attendance Policy for the Observance of Religious Days by Students: In accordance with Sections 1006.53 and 1001.74(10)(g) Florida Statutes and Board of Governors Regulation 6C-6.0115, the University of South Florida (University/USF) has established the following policy regarding religious observances: (http://usfweb2.usf.edu/usfgc/gc_pp/acadaf/gc10-045.htm)

    In the event of an emergency, it may be necessary for USF to suspend normal operations. During this time, USF may opt to continue delivery of instruction through methods that include but are not limited to: Blackboard, Elluminate, Skype, and email messaging and/or an alternate schedule. It’s the responsibility of the student to monitor Blackboard site for each class for course specific communication, and the main USF, College, and department websites, emails, and MoBull messages for important general information.

    I. Policy on Make-up Work

    If a student has a valid university-approved reason for being unable to take an online examination when scheduled, a make-up exam will be given but only if the student has notified the instructor in advance that s/he cannot take the exam. Make-up exams may be scheduled online.

    J. Program This Course Supports

    The Master of Accountancy program in the Lynn Pippenger School of Accountancy and the MBA program in the Muma College of Business


  5. Course Concurrence Information



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