DATA472-18S2 (C) Semester Two 2018

Special Topic

15 points

Details:
Start Date: Monday, 16 July 2018
End Date: Sunday, 18 November 2018
Withdrawal Dates
Last Day to withdraw from this course:
  • Without financial penalty (full fee refund): Friday, 27 July 2018
  • Without academic penalty (including no fee refund): Friday, 12 October 2018

Description

Special Topic

Special Topic: Medical Data Informatics

This course explores statistical models, algorithms, and programming platforms for medical data including imaging, clinical and research text reports, lab results, and patient records.

The course will explore detailed aspects of data science research and applications such as reinforcement learning, natural language processing, deep learning, model selection, visualisation, and parallel programming. This course will offer students an opportunity to deepen their understanding in these topics using medical data to explore active research and applications to improve quality of medical care and advance knowledge of big data's role in diagnosis and treatment. Topics include statistical models, algorithms, and programming platforms for processing medical data including medical imaging data (ECG, CT, MRI, fMRI, ultrasound), medical texts (clinical notes, lab reports, published research), and patient medical records (EHR). Students will complete lab assignments to show competency in usage of software platforms for visualisation, parallel processing, and model selection. Students will design and implement a project using machine learning to research possible solutions to real-world problems in the medical data domain. Students will self-reflect on aspects of data science to improve quality, access, and efficacy in medical care.

Learning Outcomes

  • Students who successfully complete this course will be able to:
  • Demonstrate knowledge of models and algorithms for reinforcement learning, natural language processing, and deep learning
  • Show competency in usage of software platforms for visualisation, parallel processing, and model selection
  • Show competency in applying skills to process medical imaging data (ECG, CT, MRI, fMRI, ultrasound), medical texts (clinical notes, lab reports, published research), and patient medical records (EHR)
  • Apply data science competencies to design and implement a research project using machine learning to research possible solutions to real-world problems in the medical data domain.
  • Self-reflect on selected aspects of data science to improve quality, access, and efficacy in medical care

Prerequisites

Subject to the approval of the Head of School

Course Coordinator

James Atlas

Indicative Fees

Domestic fee $905.00

* All fees are inclusive of NZ GST or any equivalent overseas tax, and do not include any programme level discount or additional course-related expenses.

For further information see Mathematics and Statistics .

All DATA472 Occurrences