INF207 Business Data Analytics

Unit Code

INF207

Level

Undergraduate

Campus

Brisbane | Sydney

Prerequisites

INF201, STA101

Credit Points

Overview

This Business Data Analytics exposes students to several types of business problems which are suitable for using data analytics. By using popular data mining tools and techniques used in the industry the students will understand how big data analytics works and the key role data insights have into business decision-making. Students will differentiate the key aspects of data analytics principles and tools in a business context whilst identifying the strengths and limitations of data analytics technologies such as statistics and machine learning and evaluate appropriate applications. Finally, they will propose a data analytics road map for use in a modern organisation which will automate data collection and mining and utilise techniques to discover previously hidden insights.

Associated Degrees

Bachelor of Business (Information Systems)
Bachelor of Business (Marketing)

Duration | AQF Level

One Semester | Level 7

Core or elective Subject

☐ core subject
☐ elective subject
☒ other (please specify below): Core for BBIS and elective for BBM

Study Modes

√ Face to face on site
√ Full-time
√ Part-time

Subject Learning Summary

The subject introduces business analytics and develops practical skills in descriptive statistics, data visualisation, data cleaning and data management. It covers key data science and machine-learning techniques, including association-rule mining, clustering, regression and classification, together with methods for evaluating model performance. Students also examine big data, artificial intelligence and the ethical, security and privacy considerations associated with analytics and AI.

Learning Outcomes

Analyse the appropriate use of business data analytics for solving business problems
Explain the key role data insights have into the business decision-making process
Differentiate the key aspects of data analytics principles and tools in a business context
Critically analyse the strengths and limitations of data analytics technologies such as statistics and machine learning
Implement data analytics techniques on some supplied big data relating to a business problem
Propose data analytics solutions for use in modern organisations which will automate data collection and mining and use techniques to discover previously hidden insights

Assessment

AIIHE uses a variety of assessment tools to guide and assess each student’s achievement of their learning outcomes. In this subject there will be in–class activities intended to support you to engage in and reflect on your learning journey and understanding of the subject. You will also undertake assessment tasks related to your skills development, your effective engagement in group work and delivery of collaborative outcomes, including a written report and an oral presentation.

Summary of Assessment

Assessment Task	Due Date	Weighting
Assessment 1: ICSR and Business Analytics using Excel	Week 2-12	20%
Assessment 2: Group Project In-class Presentation	Week 9-10	50% (35% Report and 15% Presentation)
Assessment 3: Practical Test	Week 12	30%

Learning Resources

AIIHE will update the topic coverage of the subject and the available relevant learning resources at the time of subject delivery. In the interim, AIIHE provides the following indicative list of relevant reference material. AIIHE also subscribes to eLearning Resources through the Canvas portal, and where relevant material is available, AIIHE will seek to source reference material from this system for staff and student access.

Prescribed text

Texts and References

Loshin, D. (2014). Big Data Analytics: From Strategic Planning to Enterprise Integration with Tools, Techniques, NoSQL and Graph (1st Edition). Cengage.
Foreman, J. (2014). Data Smart: Using Data Science to Transform Information into Insight. Wiley.
Provost, F., & Fawcett, T. (2013). Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking (1st Edition). O’Reilly.