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. |
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| 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
| 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.