Course Title: Data Analysis

Part A: Course Overview

Course Title: Data Analysis

Credit Points: 12.00

Terms

Course Code	Campus	Career	School	Learning Mode	Teaching Period(s)
BUSM3298	City Campus	Undergraduate	620H Business IT and Logistics	Face-to-Face	Sem 1 2009, Sem 2 2009
BUSM3304	City Campus	Postgraduate	620H Business IT and Logistics	Face-to-Face	Sem 2 2009
BUSM3305	City Campus	Research	620H Business IT and Logistics	Face-to-Face	Sem 2 2009
ISYS2449	City Campus	Undergraduate	620H Business IT and Logistics	Face-to-Face	Sem 1 2010, Sem 2 2010, Sem 1 2011, Sem 1 2012, Sem 1 2013, Sem 1 2014
ISYS2450	City Campus	Postgraduate	620H Business IT and Logistics	Face-to-Face	Sem 1 2010
ISYS2451	City Campus	Research	620H Business IT and Logistics	Face-to-Face	Sem 1 2010

Course Coordinator: Dr. Siddhi Pittayachawan

Course Coordinator Phone: +61 3 9925 1357

Course Coordinator Email: siddhi.pittayachawan@rmit.edu.au

Course Coordinator Location: Building 80, Level 7

Pre-requisite Courses and Assumed Knowledge and Capabilities

It is expected that you have successfully completed a statistical course or have equivalent experience in fundemental statistics; are able to write a report academically; are an independent and a self-motivated learner who undertakes additional study outside of class.

Course Description

This course aims to provide you with hands-on experience of analysing qualitative and quantitative data. In qualitative analysis, you will learn to use classical techniques (e.g. a highlighter and paper) and modern techniques (e.g. NVivo) for coding data and answering research propositions. In quantitative analysis, you will learn how to explore data, to validate research hypotheses, and to use various statistical techniques for explaining phenomenon with statistical software (e.g. SPSS). Furthermore, you will learn how to interpret and present research findings academically. By completing this course, you will be equiped with knowledge and experience in qualitative and quantitative analysis which will allow you to further pursue postgraduate research programs.

If you are undertaking this course in Melbourne from semester 2, 2012 onward your class will be held in a device-equipped teaching space. Each student group will have access to a laptop. It is however recommended that you have access to a mobile computing device to allow greater flexibility in terms of where you can work on campus both in and outside class times.

Objectives/Learning Outcomes/Capability Development

The capabilities that are developed through the program in which you are enrolled are described in the Program Guide. This course contributes to the development of the capabilities in the following way.

It is expected that you will acquire the following capabilities:

1. Critical thinking: you are required to demonstrate sound justification for using particular analytical techniques.
2. Analytical and problem solving skills: this course provides you with opportunities to analyse data with a series of techniques. In many circumstances, data does not match an idealistic requirement, especially in statistics; therefore, you learn to demonstrate you capabilities of addressing these issues.
3. Communication of complex ideas, idea design and presentation, and logical reasoning: you are required to demonstrate communication and presentation skills after data analysis. In fact, it is essential for stakeholders to be convinced in your research and to understand how they can implement research findings in the real world.

Upon the completion of this course, you are expected to be able to:

1. Appreciate the use of different analytical techniques for different purposes
2. Use tools or software to analyse data
3. Interpret and present findings in a sensible manner
4. Write an analytical, understandable report for stakeholders

•  

Overview of Learning Activities

Lecturer inputs: A range of learning experiences are planned for you including lectures and laboratories. Lectures will introduce to you key concepts and techniques of data analysis and their applications in research. In addition, it will be used to develop critical thinking by examining academic papers regarding how analytical techniques are used in extant studies. Laboratories will provide opportunities for you to apply analytical techniques, which you will learn in lectures, with actual data using different techniques and tools.

Your inputs as learners: Your active and constructive participation in group discussions is expected in addition to weekly reading, group and individual exercises and careful planning and completion of assessment tasks.

Overview of Learning Resources

RMIT university will provide you with resources and analytical tools for learning in this course through our online systems and computer laboratories. You have access to extensive course materials on myRMIT Studies, including digitised readings, lecture notes and a detailed study program, external internet links and access to RMIT Library online and hardcopy resources.

Overview of Assessment

The assessment in this course may consist of a combination of class discussion, laboratory work, oral presentation, reflective journals, and reports. This breadth of assessment provides opportunities for you to demonstrate your understanding of data analysis by stimulating you to use a deep-learning approach.

Feedback will be provided throughout the semester in class and/or online discussions, through individual and group feedback on practical exercises and by individual consultation.

If you have a long term medical condition and/or disability it may be possible to negotiate to vary aspects of the learning or assessment methods. You can contact the program coordinator or the Disability Liaison Unit if you would like to find out more.

Your course assessment conforms to the RMIT university assessment principles, regulations, policies and procedures which are described and referenced in a single document Assessment Policies and Procedures manual. An 1.2.4 Assessment Charter section of this document summarises your responsibilities as an RMIT student as well as those of your teachers.