Faculty of Health, Arts and Design Unit Outline STA30004 Data Mining Semester 2 2019 Please read this Unit Outline carefully. It includes: PART A Unit summary PART B Your Unit in more detail PART C Further information "Swinburne University of Technology recognises the historical and cultural significance of Australia's Indigenous history and the role it plays in contemporary education Each day in Australia, we all walk on traditional Indigenous land We therefore acknowledge the traditional custodians of the land that our Australian campuses currently occupy, the Wurundjer| people, and pay respect to Elders past and present, including those from other areas who now reside on Wurundjeri land" SWIN BUR NE SWINBURNE UNIVERSITY OF TECHNOLOGY
PART A: Unit Summary Unit Code(s) STA30004 Unit Title Data Mining Duration One semester/teaching period Total Contact Hours 48hrs Requisites: STA20006: Analysis of Variance and Regression Pre-requisites Co-requisites Concurrent pre-requisites Anti-requisites Assumed knowledge Credit Points 12.5 Campus/Location Examinations (50%) Quizzes (10%) Assignment (30%) Powerpoint Presentation (10%) Mode of Delivery On-Campus Assessment Summary Aims Hawthorn This unit introduces students to the definition and scope of modern data mining. Students will learn how to manage large data sets and to appropriately select and transform variables. The unit builds understanding of the importance of appropriate data mining procedures in analysing "big data", also developing student capability in conducting appropriate data mining approaches to investigate relationships in large data sets and in critically assessing reports derived from such data. STA30004_Unit Outline Semester 2_2019 Version: Unit of Study Outline_V1 15_7_18 Page 2 of
Unit Learning Outcomes Students who successfully complete this Unit should be able to: 1. Appropriately apply common data mining terms. 2. Critically read and evaluate data mining reports that appear in media and other publications. 3. Understand, identify and describe the essential elements of unsupervised and supervised learning. 4. Select, transform and visualize the variables and relationships to be used for particular data mining purposes. 5. Choose and successfully apply query tools and exploratory, predictive, classification and segmentation data mining procedures in a variety of areas. 6. Interpret outputs from appropriate data mining software to report on copious amounts of data. Key Generic Skills You will be provided with feedback on your progress in attaining the following generic skills: · Analysis Skills · Problem Solving Skills . Communication Skills · Ability to tackle unfamiliar problems · Ability to work independently Content . Introduction to Data Mining and Data · Warehousing Introduction to Rattle · Exploratory Data Analysis · Data Transformation · Association Analysis · Regression for Classification and Prediction . Trees for Classification and Prediction . Random Forests and Boosting . Neural Networks for Classification and Prediction · Support Vector Machines STA30004_Unit Outline Semester 2_2019 Version: Unit of Study Outline_V1 15_7_18 Page 3 of