Course Title: Data Preprocessing

Part A: Course Overview

Course Title: Data Preprocessing

Credit Points: 12.00

Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

MATH2382

City Campus

Undergraduate

171H School of Science

Face-to-Face

Sem 1 2021

Course Coordinator: Dr. Sona Taheri

Course Coordinator Phone: +61 3 9925 2526

Course Coordinator Email: sona.taheri@rmit.edu.au

Course Coordinator Location: 15.04.02

Course Coordinator Availability: By appointment, by email


Pre-requisite Courses and Assumed Knowledge and Capabilities

A working knowledge of basic mathematics and familiarity with computers.


Course Description

Real-world data are commonly incomplete, noisy, and inconsistent. This course will cover a wide range of topics designed to equip you with the skills needed to prepare all forms of untidy data for statistical analysis. The course will cover the core concepts of data preprocessing, namely tidy data, data integration, data cleaning, data transformation, data standardisation, data discretisation, and data reduction. You will develop and apply your data preprocessing skills to complex, noisy, and inconsistent real world data using leading open source software.

This course includes a Work Integrated Learning experience in which your knowledge and skills will be applied and assessed in a real workplace context. Any or all of these aspects of a WIL experience may be simulated.


Objectives/Learning Outcomes/Capability Development

On completion of this course you should be able to: 

  1. Accurately, logically and ethically combine data from multiple sources to make suitable for statistical analysis and draw valid interpretations.
  2. Articulate how data meets the best practice standards (e.g. tidy data principles).
  3. Select, perform and justify data validation processes for raw datasets.
  4. Use leading open source software (e.g. R) for reproducible, automated data processing. 


This course contributes to the following Program Learning Outcomes for BP330, Bachelor of Space Science.

Understanding science and engineering

  • You will demonstrate an understanding of the scientific method and engineering fundamentals and an ability to apply them  in practice.

Knowledge and technical competence

  • You will have broad knowledge in space science and technology with deep knowledge in its core concepts.
  • You will have knowledge in at least one discipline other than your primary discipline and some understanding of interdisciplinary linkages.

Inquiry and Problem Solving

  • You will be able to choose appropriate tools and methods to solve scientific problems within your area of specialisation.
  • You will demonstrate well-developed problem solving skills, applying your knowledge and using your ability to think analytically and creatively.

Information literacy

  • You will develop a capacity for independent and self-directed work.
  • You will work responsibly, safely, legally and ethically.
  • You will develop an ability to work collaboratively.


Overview of Learning Activities

This course uses highly structured learning activities to guide your learning process and prepare you for your assessments. The activities are a combination of individual, peer-supported and facilitator-guided activities, and where possible project-led, with opportunities for feedback throughout.  

Authentic and industry-relevant learning is critical to this course and you will be encouraged to critically compare what is happening in your context and in industry, and to use your insights.  

Social learning is another important component and you are expected to participate in class and group activities, share drafts of work and resources and give and receive peer feedback. You will be expected to work efficiently and effectively with others to achieve outcomes greater than those that you might have achieved alone.  

Above all, the learning activities are designed to maximise the likelihood that you will not only understand the course learning resources but also apply that learning to improving your own practice, for example by producing real-world artefacts and engaging in scenarios and case studies.   


Overview of Learning Resources

You will be able to access course information and learning materials through RMIT’s Learning Management System (LMS).  

The LMS will give access to important announcements, a discussion forum, staff contact details, the teaching schedule, course contents, notes, learning materials and data sets, and all assessment briefs.  

A list of recommended textbooks for this course will also be provided on Canvas as reference sources. 

A Library Guide is available at: http://rmit.libguides.com/mathstats 


Overview of Assessment

This course has no hurdle requirements.

Assessment tasks

Practical Assessments  

Weighting 45%  

CLOs 1,2,3 & 4 

This Module Discipline Based Assessments   

Weighting 10%   

This assessment task supports CLOs 1,2,3 & 4   

Formative Assessments 

Weighting 45% 

This assessment tasks supports CLOs 1,2,3 & 4