Master of Data Science (MDS)
Master of Data Science (MDS)
Program Code
MDSC
Program Faculty
Faculty of Sciences, Engineering and Technology
Academic Year
2019
These Program Rules should be read in conjunction with the University's policies (https://www.adelaide.edu.au/policies).
Overview
The Master of Data Science is a conversion program designed for students who wish to develop new skills in the growing area of data science, with a specific focus on large-scale data analysis, or big data. It is suitable for students with no prior experience in computer science as well as those with existing qualifications. In this program, students undertake a variety of core and elective courses, designed to provide skills in computer science, mathematics, data science and data science analysis, as well as a significant project designed to combined skills developed across the program.
The program provides the necessary skills for entering the world of Big Data and Data Science, an emerging area of necessity for many fields, including Science, Engineering, Economics and Digital Humanities. The program will help learners understand how data is changing our world, and how learners can apply data science techniques to drive changes in their organisation or area. The program will provide an introduction to the emerging field of data, and introduce fundamental analysis techniques, explored through a range of case studies, gaining deeper knowledge of relevant computational and mathematical techniques, such a machine learning and data mining.
In the Master of Data Science, students undertake a specialised introductory program in their first semester, designed to address fundamental requirements in programming, mathematics and data science. Students then proceed with a program of courses tailored to their particular interest, building skill and knowledge in data science. The program includes a 15-unit research project in the area of data science. The remaining 36 units consist of approved coursework.
The Master of Data Science is an AQF Level 9 Qualification with a standard full-time duration of 2 years.
Conditions
Condition of Enrolment
1. Interruption of program: Students must apply for permission from the Executive Dean or delegate before taking a Leave of Absence. Any extension of the leave without approval will result in the loss of place in the program but an application may be made to be re-admitted to the program subject to the admission procedures in place at the time.
Academic Program Rules for Master of Data Science
There shall be a Master of Data Science.
Qualification Requirements
To qualify for the degree of Master of Data Science, the student must complete satisfactorily a program of study consisting of the following courses with a combined total of not less than 48 units comprising:
Core courses to the value of 24 units;
Elective courses to the value of 9 units; and
A Research Project to the value of 15 units
Core Courses
-
Master of Data Science
To satisfy the requirements for Master of Data Science students must complete courses to the value of 48 units.
Core Courses
All of the following courses must be completed:
Subject/Catalogue Course Title Unit Value COMP SCI 7094 Distributed Databases & Data Mining 3 COMP SCI 7201 Algorithm & Data Structure Analysis 3 COMP SCI 7208 Programming and Computational Thinking for Data Science 6 COMP SCI 7209 Big Data Analysis and Project 3 COMP SCI 7306 Mining Big Data 3 COMP SCI 7314 Introduction to Statistical Machine Learning 3 MATHS 7103 Probability & Statistics 3 Research Project
All of the following courses must be completed:
Subject/Catalogue Course Title Unit Value COMP SCI 7405 Research Methods in Software Engineering and Computer Science 3 COMP SCI 7097A Master Data Science Research Project Part A 6 COMP SCI 7097B Master Data Science Research Project Part B 6 Electives
Courses to the value of 9 units from the following:
Subject/Catalogue Course Title Unit Value COMP SCI 7007 Specialised Programming 3 COMP SCI 7059 Artificial Intelligence 3 COMP SCI 7076 Distributed Systems 3 COMP SCI 7088 Systems Programming 3 COMP SCI 7305 Parallel and Distributed Computing 3 COMP SCI 7407 Advanced Algorithms 3 STATS 7004 Statistics Topic A 3 STATS 7008 Statistics Topic D 3 STATS 7014 Statistics Topic B 3 STATS 7016 Statistics Topic C 3 STATS 7054 Statistical Modelling 3 STATS 7056 Biostatistics 3 STATS 7057 Sampling Theory and Practice 3 STATS 7058 Time Series 3 STATS 7059 Mathematical Statistics 3 STATS 7069 Statistics Topic E 3 STATS 7070 Statistics Topic F 3 STATS 7107 Statistical Modelling and Inference 3