Course Title: Statistical Data Science

Part A: Course Overview

Course Title: Statistical Data Science

Credit Points: 12.00


Course Coordinator: Prof Inge Koch

Course Coordinator Phone: +61 3 9925 0519

Course Coordinator Email: inge.koch@rmit.edu.au

Course Coordinator Location: B15.04.13

Course Coordinator Availability: by appointment and by email


Pre-requisite Courses and Assumed Knowledge and Capabilities

Recommended Prior Study 

It is recommended to have satisfactorily completed the following course/s before you commence this course: 

Alternatively, if you have the equivalent skills and knowledge covered in the above course/s you may be eligible for recognition of prior learning.  

Please contact your course coordinator for further details. 


Course Description

This course introduces fundamental concepts and contemporary methods of Statistical Data Science – component analysis and feature selection, supervised and unsupervised approaches, and shows how to apply these methods to different data science domains (e.g. physical sciences, medical and biological sciences, engineering, business and social sciences). Focus will be on the interaction between methods and data, on learning to choose suitable methods of data analysis for particular data and on interpreting the results. Statistical computing will form an essential part of this course. 


Objectives/Learning Outcomes/Capability Development

Program Learning Outcomes 

This course contributes to the program learning outcomes for the following program(s), MC004 Master of Statistics and Operations Research and MC242 Master of Analytics:  

Personal and professional awareness 

  • the ability to contextualise outputs where data are drawn from diverse and evolving social, political and cultural dimensions 
  • the ability to reflect on experience and improve your own future practice 
  • the ability to apply the principles of lifelong learning to any new challenge. 

Knowledge and technical competence 

  • an understanding of appropriate and relevant, fundamental and applied mathematical and statistical knowledge, methodologies and modern computational tools. 

Problem-solving 

  • the ability to bring together and flexibly apply knowledge to characterise, analyse and solve a wide range of problems 
  • an understanding of the balance between the complexity / accuracy of the mathematical / statistical models used and the timeliness of the delivery of the solution. 

Communication 

  • the ability to effectively communicate both technical and non-technical material in a range of forms (written, electronic, graphic, oral), and to tailor the style and means of communication to different audiences.  Of particular interest is the ability to explain technical material, without unnecessary jargon, to lay persons such as the general public or line managers. 

Information literacy 

  • the ability to locate and use data and information and evaluate its quality with respect to its authority and relevance. 

 

For more information on the program learning outcomes for your program, please see the program guide.  


Upon successful completion of this course, you will be able to:  

  1. Construct and interpret visual presentations of data;
  2. Choose appropriate multivariate methods, including checking the applicability of the underlying assumption of the method, for analysing your data;
  3. Apply modern programming languages to analyse data and interpret results of the analyses;
  4. Clearly and concisely communicate results of multivariate data analyses to peers and the community, and in the format of a scientific report. 


Overview of Learning Activities

The course introduces and familiarise students with important concepts, ideas and approaches in Statistical Learning which underpin Statistical Data Science. The course includes the interaction between methods and data, choosing suitable methods of data analysis for particular goals and datasets, applying and implementing these methods on the computer to a range of datasets, and interpretating and communicating the results. 

This course will use a range of learning activities including recorded lectures and flexible learning resources organised into learning modules to provide the knowledge for the successful completion of assessments. Throughout the course you will be able to work both independently and in groups to aid development of your computational skills and statistical knowledge to apply to real-world data. 

You will be expected to participate in online classes and online discussions to contribute to the learning experiences of your student colleagues. 

Self-Directed Learning: You are expected to undertake self-directed learning activities to develop and consolidate knowledge throughout this learning experience. The activities associated with this course are designed to enable you to better develop independent learning skills to support your commitment to lifelong learning as a professional and a university graduate. The self-directed learning activities will also support your knowledge of the material covered in the formal learning program and enhance the consolidation and application of that knowledge.  


Overview of Learning Resources

RMIT will provide you with resources and tools for learning in this course through myRMIT Studies Course. 

There are services available to support your learning through the University Library. The Library provides guides on academic referencing and subject specialist help as well as a range of study support services. For further information, please visit the Library page on the RMIT University website and the myRMIT student portal. 


Overview of Assessment

Assessment Tasks 

Assessment Task 1: Weekly Quizzes (x10) 
Weighting 10%  
This assessment task supports CLOs 1, 2, and 3.  

Assessment Task 2: 3 Group Assessment Peports 
Weighting 45%  
This assessment task supports CLOs 1, 2, 3 and 4.  

Assessment Task 3: End of Semester Test  
Weighting 45% 
This assessment task supports CLOs 1, 2, 3 and 4. 

 

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 Equitable Learning Services if you would like to find out more.