Program Overview

Master of Applied Data Science

The Master of Applied Data Science is a postgraduate degree that focuses on the technical skills required for wrangling data and analyzing it. This course is designed for students who have previous knowledge in the fields of IT and/or science or maths.

Structure

The Master of Applied Data Science consists of 12 units, divided into four parts, and takes two years of part-time study to complete. The course is structured into six-week semester blocks, allowing for flexibility in study duration.

Part A: Foundational Studies

• Introduction to databases: This unit provides a thorough introduction to database management, covering relational modeling, analysis, and design.
• Introduction to Python: This unit teaches programming fundamentals and the Python language, developing a foundational understanding of program design and implementation of algorithms.
• Mathematical foundations for data science and AI: This unit covers mathematical topics fundamental to computing and statistics, including trees and other graphs, counting in combinatorics, principles of elementary probability theory, linear algebra, and fundamental concepts of calculus.

Part B: Core Studies

• Introduction to data science: This unit teaches various ways of working with data to draw actionable insights, exploring industry-relevant case studies and archival and architectural practice.
• Data wrangling: This unit focuses on the problems that prevent raw data from being effectively used in analytics and how rigorous data cleaning and pre-processing is fundamental to preparing data for analytics.
• Statistical data modeling: This unit covers statistical modeling, focusing on how modeling is used in a wide range of professional contexts, including analytic tasks and concepts fundamental to data modeling and prediction.

Part C: Specialist Studies

Students must complete four of the following units:

• Data exploration and visualization: This unit introduces statistical and visualization techniques for the exploratory analysis of data.
• Applied data analysis: This unit examines data analysis in its professional context, introducing contemporary methodologies such as machine learning techniques.
• Machine learning: This unit explores the impact machine learning is having on the major statistical learning models and algorithms used in data analysis.
• Data processing for big data: This unit focuses on big data processing, including volume, complexity, and velocity using the latest big data technologies.
• Data analysis for semi-structured data: This unit explores basic forms of semi-structured data and applies basic machine learning algorithms to solve industry problems.

Part D: Applied Practice

• Applied practice 1: This unit provides a practical and theoretical understanding of what being a modern IT professional means and how the role adapts.
• Applied practice 2: This unit is an opportunity for students to apply the skills and knowledge they have already gained in a practical setting, researching and creating a specific problem or question and then developing a solution according to industry standards.

Entry Requirements

There are multiple ways to be eligible for studying the Master of Applied Data Science:

• Entry Level 1: An Australian bachelor's degree in a relevant discipline (or equivalent) with a credit average of 60%, or an Australian bachelor's degree (or equivalent) in a relevant discipline and a Graduate Certificate or Graduate Diploma in a relevant discipline with a credit average of 60%.
• Entry Level 2: A Monash University Graduate Certificate of Applied Data Science with a credit average of 60%, or an Australian bachelor's degree (or equivalent) in a relevant discipline and a Graduate Certificate or Graduate Diploma in a relevant discipline, with a credit average of 60% in the higher degree.
• Entry Level 3: A Monash University Graduate Diploma of Applied Data Science with a credit average of 60%.

Fees

The estimated cost is $5,200 per unit, with a total of 12 units, making the approximate total cost $62,400. Unit fees are subject to change annually.

Career Pathways

The Master of Applied Data Science can lead to various career paths, including:

• Data Analyst: Helping organizations reduce costs and grow revenue by collecting, analyzing, and interpreting data.
• Data Scientist: Analyzing raw data to uncover patterns and solve business problems, with expertise in programming and analytics.
• Business Analyst: Reviewing and analyzing processes, creating efficiencies, and leading teams to map out company requirements.
• Business Intelligence Analyst: Using data to guide strategy and improve performance, with expertise in analysis and problem-solving.
• Data Architect: Designing data structures for organizations, specializing in data collection, analysis, and warehousing, with strong problem-solving and programming skills.

Academic Team

The course is led by experienced academics, including Dr. Yi-shan Tsai, Dr. Yasmeen George, and Dr. Guanliang Chen, who have expertise in data science, AI, and related fields.

Research Areas

The Master of Applied Data Science covers various research areas, including data science, AI, machine learning, and big data processing, with a focus on practical applications and industry-relevant case studies.