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FIN3103 Business Data Forensics

Semester 1, 2012 On-campus Toowoomba
Units : 1
Faculty or Section : Faculty of Business and Law
School or Department : School of Accounting, Economics and Finance
Version produced : 30 December 2013

Contents on this page


Examiner: Glenda Adkins
Moderator: Peter Best


Pre-requisite: STA2300

Other requisites

Students are required to have access to a personal computer, e-mail capabilities and Internet access to UConnect. Current details of computer requirements can be found at


All organisations and business managers operate in a climate of uncertainty. Data and information are the lifeblood of organisations. Managers need it to manage current operations for optimal performance and to develop corporate strategies. However, inaccurate, out-dated and excessively large volumes of data can make it difficult to make decisions and manage the business. Business Data Forensics, using the SAS system, allows for the analysis of data across multiple disciplines - including competitive data, demographic, sales, customer, financial and market data - so as to gain new insights into an organisation's current performance, its risks and opportunities. Using data mining techniques, managers can gain improved insight into business operations, improved accuracy of projections and forecasts and more confidence that investments will deliver the expected returns. Business data mining is the process of extracting or uncovering hidden patterns in large amounts of data and analysing and summarising it into useful information. Data mining tools and techniques predict future trends and behaviours and can generate new business opportunities by allowing businesses to make proactive, knowledge-driven decisions. It requires the identification of a problem, the collection of data and models that provide statistical or other means of analysis. Data mining can be used by a number of different types of industries (for example, financial institutions, health care providers, telecommunication companies and law enforcement agencies) in a number of different ways, including forecasting stock prices, forecasting bankruptcies, fraud detection, delinquent bank loan detection, forecasting defaulting loans, risk classification, portfolio management, call tracking, market segmentation, healthcare exception reporting, insurance claim estimation, and so on.


The course aims primarily to introduce students to a variety of data mining, statistical and forecasting tools and techniques and the situations in which they are applicable. The course concentrates predominantly on identifying the appropriate tools and techniques applicable to a variety of business problems and making judgments as to the type of analysis that should be used and drawing conclusions about the accuracy of that analysis. We also discuss the identification of a business problem, the collection and preparation of data and, using SAS software, provide examples of predictive and descriptive models that provide statistical or other means of analysis. Formerly FIN2103.


On successful completion of this course, students should be able to:

  1. analyse appropriate data using statistical tools and graphics; apply tools and techniques, including data collection and preparation with the use of the SAS programme; undertake tasks using Base SAS and SAS Enterprise Guide, where appropriate, evaluate results from such programmes and determine adequacy of methods
  2. describe and apply single and/or multiple regression models using the SAS computer package; interpret and comment on analysis of results; use the SAS computer package to refine such models
  3. describe and apply multivariate analysis, including correlation analysis and cluster analysis using the SAS computer package to appropriate data; interpret and comment on the analysis of results; use the SAS computer package to interpret and comment on the analyses
  4. describe and apply appropriate forecasting techniques to appropriate data, using the SAS computer package; interpret, analyse and comment on the results
  5. apply assessment techniques to compare the outcomes of models; make judgements on the type of analysis that should be used and the accuracy of the analysis; draw conclusions as to the adequacy of predictive models, that are relevant and important to a variety of business problems.


Description Weighting(%)
1. Introduction to business data forensics, data mining processes, the SAS system and software installation 10.00
2. Base SAS and SAS enterprise guide 15.00
3. Statistical tools and techniques 15.00
4. Multivariate analysis 20.00
5. Regression analysis 20.00
6. Forecasting techniques 20.00

Text and materials required to be purchased or accessed

ALL textbooks and materials available to be purchased can be sourced from USQ's Online Bookshop (unless otherwise stated). (

Please contact us for alternative purchase options from USQ Bookshop. (

  • SAS on demand (to be advised).

Reference materials

Reference materials are materials that, if accessed by students, may improve their knowledge and understanding of the material in the course and enrich their learning experience.
  • Berry, MJA & Linoff, GS 2004, Data mining techniques for marketing, sales, and customer relationship management, 2nd edn, Wiley Publishing, Indianapolis, Indiana.
  • Carter Hill, R, Griffiths, WE & Judge, GG 2001, Undergraduate econometrics, 2nd edn, John Wiley & Sons, New York.
  • Olson, D & Yong, S 2007, Introduction to business data mining, McGraw-Hill/Irwin, New York.
  • Wilson, JH & Keating, B 2007, Business forecasting with accompanying Excel-based ForecastX software, 5th edn, McGraw-Hill/Irwin, Boston, Massachusetts.
  • SAS Institute Inc 2004, SAS OnlineDoc 9.1.3, Cary, North Carolina - at

Student workload requirements

Activity Hours
Assessments 15.00
Directed Study 25.00
Lectures 30.00
Others 3.00
Private Study 60.00
Tutorials 30.00

Assessment details

Description Marks out of Wtg (%) Due Date Notes
ASST 1 (PROGRAMMING PROBLEM) 20 20 30 Mar 2012
ASST 2 (REPORT) 30 30 11 May 2012
ASST 3 (REPORT) 50 50 08 Jun 2012

Important assessment information

  1. Attendance requirements:
    It is the students' responsibility to attend and participate appropriately in all activities (such as lectures, tutorials, laboratories and practical work) scheduled for them, and to study all material provided to them or required to be accessed by them to maximise their chance of meeting the objectives of the course and to be informed of course-related activities and administration.

  2. Requirements for students to complete each assessment item satisfactorily:
    To satisfactorily complete an individual assessment item a student must achieve at least 50% of the marks. (Depending upon the requirements in Statement 4 below, students may not have to satisfactorily complete each assessment item to receive a passing grade in this course.)

  3. Penalties for late submission of required work:
    If students submit assignments after the due date without prior approval of the examiner, then a penalty of 5% of the total marks gained by the student for the assignment may apply for each working day late up to ten working days at which time a mark of zero may be recorded.

  4. Requirements for student to be awarded a passing grade in the course:
    To be assured of receiving a passing grade a student must achieve at least 50% of the total weighted marks available for the course.

  5. Method used to combine assessment results to attain final grade:
    The final grades for students will be assigned on the basis of the aggregate of the weighted marks obtained for each of the summative assessment items in the course.

  6. Examination information:
    There is no examination in this course.

  7. Examination period when Deferred/Supplementary examinations will be held:
    Not applicable.

  8. University Student Policies:
    Students should read the USQ policies: Definitions, Assessment and Student Academic Misconduct to avoid actions which might contravene University policies and practices. These policies can be found at

Assessment notes

  1. Assignments: (i) The due date for an assignment is the date by which a student must submit the assignment to the USQ. (ii) Students must retain a copy of each item submitted for assessment. This must be produced within 24 hours if required by the examiner. (iii) In accordance with university policy, the examiner may grant an extension of the due date of an assignment in extenuating circumstances.

  2. Referencing in assignments: Harvard (AGPS) is the referencing system required in this course. Students should use Harvard (AGPS) style in their assignments to format details of the information sources they have cited in their work. The Harvard (AGPS) style to be used is defined by the USQ Library's referencing guide at

  3. Course weightings: Course weightings of topics should not be interpreted as applying to the number of marks allocated to questions testing those topics in an examination paper. The examination may test material already tested in assignments.

  4. Deferred work: Students who, for medical, family/personal, or employment-related reasons, are unable to complete an assignment or to sit for an examination at the scheduled time may apply to defer an assessment in a course. Such a request must be accompanied by appropriate supporting documentation. One of the following temporary grades may be awarded: IDS (Incomplete - Deferred Examination); IDM (Incomplete Deferred Make-up); IDB (Incomplete - Both Deferred Examination and Deferred Make-up).

Other requirements

  1. Computer, e-mail and Internet access: Students are required to have access to a personal computer, e-mail capabilities and Internet access to UConnect. Current details of computer requirements can be found at