Graduate Diploma of Data Science
Course code: C5003
Welcome to the world of big data. At Monash Online, we offer a 100% online Graduate Diploma of Data Science that puts you at the forefront of this exciting field.
This postgraduate qualification offers:
- 100% online learning – Study and take exams at home.
- Six-week teaching periods – Focus on one unit at a time.
- Six intakes throughout the year – Achieve your goals sooner.
Following the curriculum of the Graduate Diploma of Data Science will improve your problem-solving skills and broaden the creativity behind your data analysis. You’ll experience full-circle data science from engineering and wrangling to visualisation and data management.
You’ll be exploring:
- Solid analytical theory
- R and Python language for data science
- The latest big data processing tools (Hadoop and Spark)
- Relevant case studies.
These areas are fundamental to performing as a data scientist – ensuring everything you learn is applicable to the real world outside of a study environment.
The Graduate Diploma of Data Science comprises 8 units covered over 1.4 years. Each unit is worth 6 credit points (with 8 units totaling 48 points). To graduate from the data science course, you’ll need to complete:
a) Up to 2 foundation units (0-12 points)
b) 3 core units (18 points)
c) 3-5 other units (18-30 points).
Some units require a foundation unit to be taken first. When planning your degree, make sure you consider each unit’s prerequisites to help make sure you graduate on time.
a) Foundation units
This unit introduces programming fundamentals and the Python language to students. The unit provides a foundational understanding of program design and implementation of algorithms to solve computational problems. Fundamental programming control structures, built in and complex data types and mechanisms for modularity will be presented in Python.
Topics covered will include basic input and output, program control structures, basic data structures and modular program structures, problem solving strategies and techniques for algorithm development, iteration and recursion.
This unit will introduce the concept of data management in an organisation through relational database technology. Theoretical foundation of relational model, analysis and design, implementation of relational database using SQL will be covered.
b) Core units
This unit looks at processes, case studies and simple tools to understand the many facets of working with data, and the significant effort in Data Science over and above the core task of Data Analysis. Working with data as part of a business model and the lifecycle in an organisation is considered, as well as business processes and case studies. Data and its handling is also introduced: characteristic kinds of data and its collection, data storage and basic kinds of data preparation, data cleaning and data stream processing. Styles of data analysis and outcomes of successful data exploration and analysis are reviewed. Standards, tools and resources are also reviewed. Basic curation and management are reviewed: archival and architectural practice, policy, legal and ethical issues.
Here you will develop the skills required to prepare raw data for analytics, including data cleansing and pre-processing. Python and the Pandas environment will be used as part of this unit.
c) Further units
This unit provides students with an understanding of the tasks and the main issues associated with the management of data in modern organisations and communities for business and societal purposes. The unit will examine some of the key issues that affect the data management function, incorporating recordkeeping, information accessibility, knowledge management and the governance and accountability for the data repositories. This will be used as a basis for explaining the nature of specialist work in this field and associated professional roles and responsibilities. Topics cover digital repository infrastructures, digital continuity planning; data archiving; data migration; the development of systems to support data discovery and reuse; mediated access to digital information; negotiation of data rights (ownership, copyright, access, privacy etc); utilisation of cloud computing platforms, and the data curation continuum.
This unit explores statistical and visualisation techniques and their role in data science. Different types of visualisation techniques will be presented and evaluated.
This unit introduces the software tools and techniques for data engineering. It covers traditional methods of data processing, introduction to distributed databases and the handling and processing of big data.
This unit will give you the analytical and data modeling skills you will need to data scientist or business analyst. You will be introduced to various data analysis techniques and learn how to use them to address characteristic problems.
This unit presents foundational concepts in machine learning and statistical learning theory. Various different models and algorithms will be presented and interpreted.
Companies around the world are searching for data scientists to manage and analyse their data.
In the US, for example, a December 2017 report by LinkedIn included four data-related roles (machine learning engineer, data scientist, big data developer, and director of data science) in the top 10 emerging positions. This appetite for skilled data professionals is worldwide – especially in Australia, where there’s a shortage of data scientists.
As you further your career, you can apply your new skills as a data scientist in:
- Global financial institutions
- Government organisations
- Technology start-ups
- Many other businesses
Regardless of whether you want to work in the health care, finance or IT industry (among many others), data scientists are sought after. They help a range of organisations manage the complex data sets being collected to expertly solve problems for businesses.
Learn more about the various career options you’ll have upon graduation in this infographic.
Average salaries for data professionals are now well into six figures and continue to grow, with the average income expected to be $130,176 between 2021 and 2022 (Deloitte). The Institute of Analytics Professionals of Australia (IAPA) Skills and Salary Survey 2017 revealed that data scientists can earn as much as $200,000 plus.
For entry into the Graduate Diploma of Data Science, you’ll need to prove you have relevant work experience or previous study in programming, databases, or mathematics. This can be done if you:
- Possess a bachelor’s degree in a similar field (from a recognised Australian institution)
- Meet minimum entry requirements for admission to Monash University
- Meet the English language requirements.
You’ll also need to fill out the data science online course prerequisite form. This helps us understand your previous work experience, academic history, and what position you’re starting in.
Alternative Entry through Foundation Units
If you don’t meet the entry requirements, there may be other options available to you. We offer three foundation units in programming, databases, and mathematics. To make you eligible for the graduate diploma, you can take a maximum of two foundation units prior to commencing your studies if you possess:
- A bachelor’s degree in business, engineering, or science with a mathematics major plus two Foundation Units (i.e. Programming and Databases), or;
- A non-cognate Graduate Diploma or Master’s degree (e.g. MBA) in any field at a recognised Australian institution and two years of relevant professional experience in programming or databases, plus up to two Foundation Units in an area not related to your work experience (i.e. Mathematics plus either Programming or Databases)
With either of the qualifications above, you can take two foundation units (e.g. programming and databases) and become eligible to continue your studies in the Graduate Diploma of Data Science.
Course fees (2019 pricing)
$4,062.50 per unit
1.4 year part-time
Jan, Mar, May, Jul, Aug, Oct
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