BIT307 Data Mining

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, 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
Other resource requirements:  R, R Studio

Unit description 

The subject introduces data mining techniques. It equips students to design a predictive model using data mining and machine learning techniques. Topics include data preparation, fundamentals of data mining, business intelligence, data warehousing, data exploration, classification and clustering, web mining, social media analytics, privacy, and the ethical challenges of data mining. Practical experience is gained using Python as a data mining tool.

Unit Outline Outcomes (ULO)

  On the successful completion of this units student will be able to:  
ULO1 Analyze the value, rationale, and applications of data mining for organizations.  
ULO2 Critically evaluate and recommend different data preparation methods and strategies.  
ULO3 Apply various data mining methods and models to provide results to enhance business decision making.  
ULO4 Design a predictive model using data mining and machine learning techniques.  
ULO5 Design a data warehouse and data visualization.  

Topics to be included  

1. Introduction to Data Mining
2. Data summarization and visualization
3. Data summarization and visualization with python
4. Data warehousing
5. Data querying and analyzing
6. Association rule mining
7. Clustering
8. Classification 1
9. Classification 2
10. Regression
11. Big data
12. Reporting results and review


Assessment Description Grading and weighting
(% total mark for unit)
Indicative due week 
Assessment 1: Weekly Tutorials 10% 11
Assessment 2: Group Assessment (Project Stage 1) 15% 6
Assessment 3: Group Assessment (Project Stage 2) 20% 11
Assessment 4: Group Assessment (Project Stage 3) 5% 12
Assessment 5: Final Exam 50% 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.