Data Science Master Class 2

Subject code: MA5852:03 

Communication skills and cloud computing are essential industry skills which are highly relevant to the data science profession. Model derivation and data insights are the products of data science and the communication of these products in business is key. In this subject, oral, video and project planning communication skills are developed in conjunction with learning Amazon Web Services (AWS) Sage Maker. Amazon is Australia, and the worlds, largest cloud provider of computational services, with over 85% of the Australian market, and over 40% globally.

While learning the Amazon Web Services platform, the mechanics of modern neural networks are extended from previous subjects. The concepts of regularisation, pooling, optimisation momentum and convolution are introduced and applied in the field of computer vision neural networks. Midway in the subject, Amazon is used to apply machine learning optimisation to machine learning tasks. This methodology of using machine learning for optimisation greatly enhances the outcomes of model development and removes some of the intuition needed for model development. At the conclusion of the subject, students will provide a detailed computer vision project plan using industry standard planning method, CRSIP-DM.

It is a two-part subject, you will need to complete Data Science Master Class 1 before enrolling into Master Class 2.

Software platform: Python Python and Amazon Web Services  


Learning outcomes

  • Apply and develop data science oral and video communication skills
  • Develop multi-layer perceptron and convolution neural networks using regularisation and advance optimisers
  • Use the costings, S3 (data), Sage Maker and IAM (security) elements of Amazon Web Services (AWS)
  • Programmatically interact with AWS using Python Jupyter Notebooks
  • Develop and deploy machine learning models on AWS
  • Tune hyperparameters for machine learning models using AWS
  • Demonstrate and apply time and project management skills
  • Demonstrate and apply advanced theoretical and technical knowledge of data science to an industry or research problem
  • Apply advanced research, consultancy, and presentation skills
  • Communicate the findings of a formal piece of work and meet a deadline
  • Reflect on knowledge learned of theory and business practices for future learning and ongoing professional development.

Assessment

Assessment for this course will occur at various times across the seven-week study period. Tasks may include online quizzes, discussion board activity, portfolio development, case studies, reflection, literature reviews presentations and reports. Feedback will be provided to you throughout the study period as well as a final grade at the conclusion of the study period.


This is one of the interdisciplinary subjects studied in the online Master of Data Science (Professional)

Please note, course structure and content are subject to change. For information on all course subjects download the course guide.

Download Course Guide

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