University of Technology Sydney

22576 Fundamentals of Business Data Analytics

Warning: The information on this page is indicative. The subject outline for a particular session, location and mode of offering is the authoritative source of all information about the subject for that offering. Required texts, recommended texts and references in particular are likely to change. Students will be provided with a subject outline once they enrol in the subject.

Subject handbook information prior to 2025 is available in the Archives.

UTS: Business: Accounting
Credit points: 6 cp

Subject level:

Undergraduate

Result type: Grade and marks

Requisite(s): 26134 Responsible Evidence-Based Decisions
These requisites may not apply to students in certain courses.
There are course requisites for this subject. See access conditions.

Description

Business analytics is concerned with the use of data, quantitative analysis, predictive models and fact-based analysis to create value for organisations. In Fundamentals of Business Data Analytics, students are provided with the foundations of business analytics, as well as an introduction to techniques and methods required to investigate and address practical problems. Students gain the necessary knowledge to initiate and conduct small-scale business analytics projects and begin to develop skills related to communicating their findings to stakeholders.

Subject learning objectives (SLOs)

Upon successful completion of this subject students should be able to:
1. Evaluate the role and impact of business analytics for accounting, reporting and decision making
2. Apply appropriate quantitative analytical techniques to organisational decision making using appropriate technology (software tools)
3. Effectively interpret results and assumptions of data analysis and analytical modelling and communicate them – verbally and in written form – to relevant stakeholders
4. Critically reflect on the impact of business analytics on individuals, organisations and society

Contribution to the development of graduate attributes

This subject builds on the foundation provided in the core subjects of the Bachelor of Business. It extends the accounting and analytical knowledge and skills gained in those study components and contributes to the objectives of the Bachelor of Business by providing students with contemporary data management, data analysis and business analytics skills necessary for managerial decision support in for-profit and not-for profit organisations. The subject contributes to all four graduate attributes of the Bachelor of Business, with particular emphasis on intellectual rigour and innovative problem solving (GA 1), social responsibility and cultural awareness (GA 3), and professional and technical competence (GA 4).

Teaching and learning strategies

This subject uses a variety of teaching and learning strategies to provide students with a hands-on approach to learning about data and analytics utilizing multiple technologies to analyse business data and communicate the outcome. Classes are interactive and are used to impart important theoretical and practical concepts. Students work through and are assessed on several lab exercises that utilize data from real companies.

Feedback is provided regularly in several different formats: discussions of questions and problems with peers, feedback from the lecturer/tutor on ideas presented and results/feedback of assessment tasks and automated feedback through learning activities on the UTS LMS (Canvas). This feedback is provided timely throughout the session.

Pre-class work: Students are required to work through learning content presented on the UTS LMS (Canvas) or other platforms before attending class and acquaint themselves with skills and technologies used in the subject (e.g., Excel, KNIME).

In-class: In the class, students will complete questions individually or as a group on concepts and different business analytic problems. Students will enhance their understanding and learning experience by engaging in the solution of the problems in small groups and presenting their ideas to peers or the entire class. Students are encouraged to become active engaged learners and they need to understand both fundamental methods/concepts as well as the tools to be able to deliver solutions.

Content (topics)

  • Types of BA, data and tools/applications;
  • Statistical methods for BA;
  • Descriptive BA;
  • Predictive modelling;
  • Adaptive/autonomous BA and social and socio-political implication

Assessment

Assessment task 1: Online Quizzes (Individual)*

Objective(s):

This addresses subject learning objective(s):

1 and 2

Weight: 25%
Length:

20-30 minutes per quiz. Four quizzes in total.
Criteria:
  • Understanding of theoretical concepts
  • Application of theoretical concepts to business problems

*Note: Late submission of the assessment task will not be marked and awarded a mark of zero.

Assessment task 2: Tutorial Problems (Group)*

Objective(s):

This addresses subject learning objective(s):

1, 2 and 3

Weight: 25%
Length:

Varies: It is usually an Excel file including answers to open ended questions, may include KINME workflows (exports) or a short WORD/PDF report.
Criteria:
  • Ability to explain theoretical concepts and apply them to the problems.
  • Ability to work together in a group, convey information and engage in class discussion.

*Note: Late submission of the assessment task will not be marked and awarded a mark of zero.

Assessment task 3: Integrated Project (Individual)

Intent:

Part 1: Descriptive Statistics and Data Preparation (20%)
Part 2: Predictive and Prescriptive Analytics (30%)

Objective(s):

This addresses subject learning objective(s):

1, 2, 3 and 4

Weight: 50%
Length:

Part 1: 600 to 1,200 words + data file;

Part 2: 4 to 5 minutes video (strict minimum and maximum length)
Criteria: Part 1:
  • Logic of the rationale for data collection
  • Quality of data preparation steps
  • Quality of the communication of the data preparation process

Part 2:

  • Accuracy and relevance of data analysis
  • Alignment/implementation of the proposed plan
  • Effectiveness of presentation of processes and results
  • Persuasiveness of the data narrative and quality of communication

Minimum requirements

Students must achieve at least 50% of the subject’s total marks.

Required texts

  • Camm, J.D., Cochran, J.J., Ohlmann, J.W., Fry, M.J. (2024), Business Analytics, 5th Edition, Cengage, 9780357902202
  • Additional Readings: Lecture notes, additional texts/links, e.g. LinkedIn Learning, KNIME forum/help, etc. (see information in Canvas)

References

  • Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C. and Wirth, R. (2000), CRISP-DM 1.0 Step-by-step data mining guide
  • Albright, C.S.; Winston, W. (2025), Business Analytics, 8th Edition, Cengage Learning
  • Evans, J.R. (2020), Business Analytics, 3rd Edition, Pearson Education Limited
  • [See also 'Reading List' in Canvas]

Other resources

SOFTWARE used:

  • MS Excel (current version installed at UTS computer labs)
  • KNIME (free download available at https://www.knime.com/)

Most topics will provide a recommendation for specific MS Excel tutorial videos available at LinkedIn Learning. Linkedin Learing.com is an online video learning platform available to all UTS Students through the UTS Library website (www.lib.uts.edu.au).

The LinkedIn learning content is not per se assessable, unless it overlaps with equivalent textbook content. Students are encouraged to use the LinkedIn learning content:

a) for preparation for the respective unit;
b) for deepening their knowledge and skills acquired during each week's sessions.