Master [Data Science] From University of Melbourne

You'll enter the Master of Data Science with a background in computer science or statistics (or both), and the course will be tailored to build your skills in the other discipline. 

Core subjects will give you a solid grounding in data science, so you’ll have the technological and analytical abilities that are vital for managing and interpreting large, complex collections of data.

Beyond the core subjects, elective subjects give you the freedom to dive deeper into a specialist area of data science.

The Master of Science in Data Science and Machine Learning (DSML) is an interdisciplinary graduate degree programme designed to nurture the next generation of leaders in data science. It is jointly offered by the Faculty of Science’s Department of Mathematics and Department of Statistics and Data Science and the School of Computing’s Department of Computer Science. The Faculty of Engineering and the Saw Swee Hock School of Public Health are also our teaching partners.
The programme is supported by leading NUS researchers in data science as well as data scientists from industry, and offers multiple data science specialisations. Its curriculum incorporates interdisciplinary learning from fields such as computer science, mathematics and statistics, as well as data analytics and machine learning.

The Master of Data Science is designed to prepare students to work on the forefront of data-driven decision-making and forecasting.

Build on your existing undergraduate qualification and/or industry experience as you develop an in-depth understanding of activities and processes related to managing, interpreting, understanding and deriving knowledge from large data sets.

In this course, you’ll learn how to gain meaningful insight from data obtained from business, government, scientific and other sources. Expand your knowledge and understanding of computer science and data analytics, develop skills in state-of-the-art techniques and contemporary tools covering the entire data management lifecycle.

The Data Science master's program, jointly led by the Computer Science and Statistics faculties and administered through the Institute for Applied Computational Science (IACS), trains students in the rapidly growing field of data science. 

Data Science lies at the intersection of statistical methodology, computational science, and a wide range of application domains.  The program offers strong preparation in statistical modeling, machine learning, optimization, management and analysis of massive data sets, and data acquisition.  The program focuses on topics such as reproducible data analysis, collaborative problem solving, visualization and communication, and security and ethical issues that arise in data science.

This interdisciplinary programme will provide you with training in fundamental aspects of applied data science, computation and programming, and quantitative methods.

With the rise of new and big forms of data, and computation and analytics forming ever-increasingly important elements of a wide range of professions, this programme will prepare you for a variety of careers in the private, non-profit and public sectors.

With a background in social sciences, you will be trained to use data to answer interesting social science questions. You will take a series of project-based programming courses specifically designed for students without a formal computing or statistical background. A typical student will also have taken a prior course in quantitative methods or applied statistics at a basic level, although this is not a formal requirement. 

You will become fluent in a variety of programming languages and applications, particularly R and Python, and will learn to create and manipulate large databases and think creatively about how to deploy these skills in the context of specific projects.

Data science is the study of the computational principles, methods, and systems for extracting and structuring knowledge from data; and the application and use of those principles. Large data sets are now generated by almost every activity in science, society, and commerce - ranging from molecular biology to social media, from sustainable energy to health care.

As an MSc Data Science student, you will explore how to efficiently find patterns in these vast streams of data. Many research areas have tackled parts of this problem:

Machine learning focuses on finding patterns and making predictions from data.
Ideas from algorithms and databases are required to build systems that scale to big data streams.
Separate research areas have grown around different types of unstructured data such as text, images, sensor data, video, and speech.

• Good Bachelor’s degree (Honours) from a reputable university.

• A completed bachelor degree (or higher award) in any discipline from a recognised higher education institution or equivalent

The candidates must have demonstrated a capacity for advanced computational work by 

• excelling in courses in math, computer science, statistics or scientific computing;
• exploring computational  or statistical approaches in undergraduate research;
• or through distinctive professional accomplishment.

• Upper second class honours (2:1) degree or equivalent in social science, data science, statistics or a quantitative field. Work experience is advantageous but not required.