BUS303 Data Analytics

Unit outline

Important Update: Our aim is to provide you with an optimal learning experience, regardless of how this unit is delivered. Teaching will be delivered in line with the most current COVID Safe health guidelines.  This may include a mix of online and face-to-face.  Please check the learning management system for announcements and updates. Thank you for your flexibility and commitment to studying with Sydney Institute of Higher Education. 
Enrolment modes: Year 3, Semester 1
Credit point(s): 12.5
EFTSL value: 0.125
Prerequisite: BUS103 Business Statistics; BS101 Introductory Accounting; BUS107 Database I.
Typical study commitment: Students will on average spend 10 hours per week over the teaching period undertaking the teaching, learning and assessment activities for this unit.
Scheduled learning activities: 4 timetabled hours per week, 6 personal study hours per week

Unit description 

This unit will build upon students’ basic understanding of data analytic techniques and introduce more advanced methods of data analysis. Students will learn how predictive analytics, data mining, big data analytics and location intelligence are used to assist managerial decision making. Students will also learn techniques and methods of different analytical approaches, such as neural networks, univariate and multivariate data, the Bayesian approach and sparse linear methods.


Unit Outline Outcomes (ULO)

  On the successful completion of this units student will be able to:  
ULO1 Identify a range of business applications for data tools and analytics  
ULO2 Use data analytics to understand the role of information technology and its complex concepts.  
ULO3 Explore data analytics methods to make predictions from a dataset.  
ULO4 Investigate background factors relevant to data analytics, including business and societal context.  
ULO5 Calculate and interpret appropriate numerical and graphical summaries for univariate and bivariate data.  
ULO6 Implement and appraise a data analytics project in a business environment.  

Topics to be included  

1. Data mining and exploratory data analysis
2. Data classifications and clustering techniques
3. Big data analytics
4. Univariate and multivariate statistical analysis
5. Neural networks
6. Bayesian networks
7. Sparse linear methods
8. Social media analytics, methods, processes, applications and ethics 
9. Location intelligence
10. Time series forecasting
11. Data analytic decision models
12. Model evaluation techniques

Assessment

Assessment Description Grading and weighting
(% total mark for unit)
Indicative due week 
Assessment 1: Mid-Semester Exam 30% Mid-semester exam week 
Assessment 2: Group Assignment 30% 10
Assessment 3: Final Exam 40% Final exam week

The assessment due weeks provided may change. Your lecturer will clarify the exact assessment requirements, including the due date, at the start of the teaching period.