Charles W. Lamden School of Accountancy
ACCTG 729
“Analytics in Accounting”
Spring 2015
Instructor Contact Information
Nancy Jones, SSE2420, (619)594-5331 (email is the preferred means of communication),
njones@mail.sdsu.edu
Office Hours:
Tuesdays 4:00 – 6:00 PM and Wednesdays 2:00 – 3:00PM
Prerequisites
ACCTG 621, “Accounting Information Systems”
Course Description
Analysis of data as it pertains to Accountants and Finance professionals. Focuses on analytic
techniques for decision making and examination of “big data” involving accounting information.
Hands-on experience to develop skills with select software tools used in data analytics for
accounting professionals.
Course Objectives
The era of “big data” is upon on us and unlike prior decades, data is so easy to acquire that the
sheer volume of data availability is sometimes daunting. Accounting professionals have access to
the data that can help them maintain or develop a strategic advantage or to remain competitive in
today’s fast-paced business environment. Those who fail to do so may put their organization at a
disadvantage. Data analytics has become a must-have skill for all business managers and
particularly accountants who often know both internal and external data, better than their
counterparts in other areas of the business.
“Data analytics is imperative for companies that wish to employ successful financial
accounting practices. It’s the key to being able to maximize profits from the supply chain,
manage production flows, score credit loans, predict customer churn and optimize
scheduling.” [Matthew France, Chief Financial Officer at GCE, EPM Channel, June 2013].
This course focuses on data analytics for accountants and finance professionals. Technical
aspects of data acquisition, cleansing and loading into info cubes and other data warehouse
structures are discussed at a theoretical level. In-depth discussion and skill development is
concentrated on analysis and use of the data acquired. During this class you will gain hands-on
experience with some of the information technology used for analysis and learn about its uses in
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the accounting profession. Project-oriented computer-based assignments will be used in the class
to enhance your knowledge and skills.
Note: this is not a computer-skills course. However, it does require extensive use of the computer
as a tool. To accomplish the objectives of this course, you will spend a significant portion of
your time both in and out of the class learning and using computerized information systems and
their vagaries. All of the class assignments will involve the use of the computer in one way or
another.
MSA Program Goals
MSA students will graduate being able to:

Compare, contrast, interpret, or criticize accounting and business decisions and
information using professional business communication

Actively participate in team decision making.

Apply ethical judgment and professional standards in analyzing situations and formulating
accounting and business decisions

Use relevant research tools and academic/professional literature to analyze or take a
position in accounting and business situations

Address unstructured problems in the areas of accounting information systems, financial
reporting, or taxation

Identify and discuss the significance of diversity and cultural differences in the global
business environment
This course contributes to these goals through its course learning outcomes noted below.
Course Learning Outcomes
1. Solve accounting and business related problems using appropriate data modeling tools.
2. Understand how both financial and managerial accountants can benefit from using data
analytics.
3. Understand how to use data mining techniques to discover fraud and anomalies in
accounting and financial data.
4. Use Monte Carlo simulations and other stochastic modeling for budgeting, credit scoring
and other accounting metrics with non-discrete inputs and outputs.
5. Explore the use of data warehouses and other data stores to acquire data.
6. Identify and evaluate the veracity of sources of unstructured and structured data for use in
analysis.
7. Create visualizations of data to provide clear insights into associations, relationships,
outliers and other data intimations related to accounting anomalies.
8. Explore use of computational intelligence methods such as neural networks, fuzzy logic
and genetic algorithms in accounting applications such as fraud detection, credit scores,
risk assessment, and cash flow predictions.
Required Reading
There is no required textbook for this course. Required reading will consist of articles and links
posted on BlackBoard and class handouts. I will also post my PowerPoint lecture slides.
Use of Technology
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Computer work is required throughout this course. For most of the semester, we will be meeting
in the Learning Research Studio, which has a limited number of computers and monitors. Since
much of our in-class work will utilize a computer, you will need to (want to) bring your laptop
to each class session unless I tell you otherwise. A PC-based computer is preferable to those
with Apple operating systems as some of the software that we will be using either works better
on a PC or will not work on an Apple machine at all.
During the semester, you will be asked to download and install software on your computer or to
access systems online. Everyone’s computer is set up a bit differently and you may experience
technical difficulties. Therefore do not assume that everything will run smoothly every time. Be
sure to start assignments early to allow enough time to work through any glitches. The worst that
can happen is that you get your assignment done and turned in before the deadline. Problem
solving is a highly desirable skill in the business world, so I expect you to do your own problem
solving and work your way through any technical issues so that you can successfully complete
your assignments. Some class time may be spent using the software, but do not expect to spend
class time working through software download issues.
Software Tools Used in this Class:
The following is a list of software we may use during the semester. With the exception of Excel,
this software is currently available to you at no charge via the academic alliances with these
organizations.




Microsoft Excel 2010 or newer and Power Pivot add-in
R statistical computing software www.r-project.org
GoldSim MonteCarlo simulation software www.goldsim.com
SAP Predictive Analysis, BW, and Lumira Cloud
You will be provided with complete instructions on how to load the software to your laptop or
access it online and some assistance may be available through the SAP lab.
Add/Drop Policy
You may not be allowed to add this course if you have missed the first class period. Also, any
student who does not attend class during the first week of classes may be dropped from the course.
Exams and Quizzes
There will be two midterm exam covering the material in that section of the course. There will
be a comprehensive final exam covering all of the material in the course with more emphasis on
the content of the last part of the semester. Exams may include essay, short answer, multiplechoice, and other types of objective or subjective questions and will contain questions on the
reading, lectures, and any activities or other content.
Quizzes may be announced or unannounced at the instructor’s discretion.
Assignments
Assignments are designed to reinforce the concepts of the lectures and discussions. Most
assignments will be started in class in a collaborative environment to reduce the technology
learning curve and insure understanding of assignment instructions. Even so, you should plan to
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spend time and energy outside of class to complete these assignments. Expect them to challenge
you.
To receive credit for an assignment you must complete the assignment in the proper format and
submit the assignment electronically before the deadline. Failure to do so results in a zero on
the assignment.
Complete all assignments in a professional manner, so that the physical appearance is neat and
orderly, the assignment is complete and your thoughts are organized in a logical sequence.
Unless otherwise indicated, handwritten documents are unacceptable.
Follow assignment instructions carefully to maximize your grade.
Missed Exam/Late Assignment Policy
All exams are to be taken in class and students must take the exam during the regularly
scheduled time. If you believe you are going to miss an exam, you must notify me before the
exam is given. Only in the rare instance that it isn’t possible to notify me (for example, you are
in a serious auto accident on the way to take the exam and end up in the hospital), will there be
consideration for not following this rule. No matter the reason for missing the exam, proof must
be brought to the instructor to validate the claim. Otherwise, this will be an unexcused absence
and the grade on the exam will be zero.
Assignments must be submitted according to the instructions, but generally via BlackBoard, by
the due date. Assignments turned in after the due date will receive no (zero) credit. In other
words, no late assignments will be accepted for grading. You are responsible for knowing due
dates. If you believe that you will have a conflict with an assignment due date, you are strongly
encouraged to submit assignments early.
Class Attendance and Participation
Regular attendance is expected. Since there is no assigned textbook, the lectures and in class
activities are all that much more important for acquisition of the knowledge which will help you
succeed in this course (and hopefully in your career as well). If you do have to miss a class, you
are responsible for finding out what occurred during the missed class meeting. I recommend that
you ask another student to share notes or otherwise get you up to speed.
Because this is an interactive course and we may need more or less than the originally scheduled
time to discuss a topic, the course schedule may (probably will) change. Any changes will be
announced in BlackBoard and/or in class.
Grading Policies
Because activities may be added, changed, or omitted during the semester, the following point
assignments are approximate.
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Table 1: Possible Grade Points by Activity
Activity
Midterm Exams
Final Exam
Quizzes
Assignments
Total
Points
200
150
50
300
700
Course grades will be determined by your percentage of the total points earned based on a
standard grading scale.
Other Student Responsibilities
Your first responsibility is to read and understand this syllabus and the class schedule. If you
have questions, ask them now.
It is your responsibility to come to class prepared. Read assigned material before attending class.
This will increase your comprehension of the material and will allow you to contribute to the
class in a meaningful way. You may also wish to do your own research into topics so that you
can contribute to the class discussion.
You are also responsible for checking BlackBoard on a regular basis. Announcements, due dates,
changes to the syllabus or schedule, additional activities and other communications will be
posted on the site and failure to access it could mean that you miss out on important information
and any associated remuneration. Report any grade discrepancies you find in the BlackBoard
grade book within two weeks of posting. You should also retain any returned assignments for the
duration of the semester to compare against the grade book.
You are expected to behave professionally at all times during class sessions:
 Regularly attend class,
 Arrive in class on time,
 Do not leave class until the class period is complete,
 Come prepared for class – this means that you have completed the required readings and
assignments prior to class,
 Pay attention during class - this means that you will not talk to other students during class
unless the conversation is about a class topic and then is not disruptive to the other
students,
 Do not do other work or other tasks not related to the class during the class:
o Do not use computers, tablets, cell phones or other devices for non-class work
during class. For example, this means that you will not surf the Internet, play
computer games, text message, or send emails during class,
 Respect your classmates by being a productive, non-disruptive, member of the class.
Unprofessional behavior may result in your dismissal from class and could adversely affect your
grade.
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Academic Integrity
Students are expected to behave ethically in all aspects of this course. When in doubt, ask your
instructor. Cheating of any kind is an unacceptable behavior and will not be tolerated. Some of
the more common types of academic dishonesty relate to the following:
 Plagiarism - Do not use published and/or unpublished material without acknowledging
the source.
 Cheating on assignments or projects – Do not collaborate with other students unless it is
specifically stated by the instructor that working with others is allowed (e.g., a team
project).
 Cheating on exams – Do not acquire from, or give information to, other students about
exams. Do not use materials or resources during exams that are not expressly permitted
by the instructor.
 For additional information on plagiarism and cheating, refer to
http://www.sa.sdsu.edu/srr/cheating-plagiarism.html.
With the exception of specifically designated group work, the assignments, and of course, the
exams each need to represent your own independent, individual effort. Cite all sources of
information. In those cases where collaboration is allowed, list specifically those individuals
with whom you may have collaborated.
Any observed or reported instance of academic dishonesty, as defined in the San Diego State
University Student Handbook, will be prosecuted to the extent possible. During any stage of the
semester, if you deviate from the standards of academic integrity you will at minimum receive a
zero on the assignment and may receive a grade of F for the course. In addition, the instructor
may report the event to the Department and the University. The University may decide to apply
additional penalties.
Please refer to San Diego State University Academic Integrity Policy for Student Discipline Rights and Responsibilities at http://www.sa.sdsu.edu/srr/conduct1.html.
Students with Disabilities
Upon identifying themselves to the instructor and the university, students with verified
disabilities will receive reasonable accommodation for learning and evaluation. If you are a
student with a disability and believe you will need accommodations for this class, it is your
responsibility to contact Student Disability Services at (619) 594-6473. To avoid any delay in the
receipt of your accommodations, you should contact Student Disability Services as soon as
possible. Please note that accommodations are not retroactive, and that I cannot provide
accommodations based upon disability until I have received an accommodation letter from
Student Disability Services. For more information, go to the Disabilities Services website at
http://go.sdsu.edu/student_affairs/sds/Default.aspx or call (619)594-6473.
Proposed Course Schedule
The course schedule that follows, gives you a week-by-week description of the course activities.
It includes the planned topics, readings, assignments, exams and due dates. The pace of this
course is fast and you are strongly advised to keep up with the reading and assignments.
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Note: it is impossible to predict the precise flow of the course and the activities and dates may
have to be adjusted from time to time.
Table 2: Proposed Schedule for Accounting 729 Analytics in Accounting
Class
Week
Date
1
21-Jan
2
3
28-Jan
4-Feb
4
11-Feb
5
18-Feb
6
7
25-Feb
4-Mar
8
9
11-Mar
18-Mar
10
25-Mar
11
12
8-Apr
15-Apr
13
22-Apr
14
29-Apr
15
6-May
Finals 13-May
Week
Discussion Topics
Course Overview, What is Data Analytics &
how is it important to Accountants?
Sources of Accounting Data
Data Storage
LO
3
Financial Reporting & Analysis: slicing &
dicing, queries, reports
Data Visualization: Charts, Dashboards &
Advanced Visualization Techniques in
Accounting & Finance
EXAM
Introduction to Audit Analytics
2
Data Mining & Fraud
Descriptive Models for Accounting Decision
Making
Descriptive Models for Accounting Decision
Making (continued)
Spring Break
EXAM
Predictive Accounting Models including
Predictive Models with Non-discrete Variables
and Outcomes
Computational Intelligence for Credit Scoring,
Cash flows & Fraud Detection
1, 5
2
Interpretation & Evaluation of results for
Internal & External Reporting and Audit
Pulling it all together: Recap
3
6
5
Assignment
Due Dates
Pivot Table
Assignment
7
Data Warehouse
Assignment
2
Data
Visualization
Assignment
2
Data Mining
Assignment
2, 3,
4, 8
8
all
Predictive
Analysis
Assignment
Simulation
Assignment
FINAL EXAM
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