Commerce and research are being transformed by data-driven discovery and prediction. Skills required for data analytics at massive levels - scalable data management on and off the cloud, parallel algorithms, statistical modeling, and proficiency with a complex ecosystem of tools and platforms - span a variety of disciplines and are not easy to obtain through conventional curricula. Tour the basic techniques of data science, including both SQL and NoSQL solutions for massive data management, basic statistical modeling (e.g., descriptive statistics, linear and non-linear regression), algorithms for machine learning and optimization, and fundamentals of knowledge representation and search. Learn key concepts in security and the use of cryptographic techniques in securing data.
Learning Outcomes
Upon successful completion, students will have the knowledge and skills to:
- Demonstrate a conceptual understanding of database systems and architecture, data models and declarative query languages
- Define, query and manipulate a relational database
- Demonstrate knowledge and understanding of descriptive and predictive data analysis methods, optimization and search, and knowledge representation.
- Formulate and extract descriptive and predictive statistics from data
- Analyse and interpret results from descriptive and predictive data analysis
- Apply their knowledge to a given problem domain and postulate potential data analysis problems
- Identify potential pitfalls, and social and ethical implications of data science
- Explain key security concepts and the use of cryptographic techniques, digital signatures and PKI in security
Indicative Assessment
- Assignments (30) [LO 1,2,3,4,5,6,7,8]
- Labs & Quizzes (20) [LO 2,3,4]
- Final exam (50) [LO 1,2,3,4,5,6,7,8]
The ANU uses Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. While the use of Turnitin is not mandatory, the ANU highly recommends Turnitin is used by both teaching staff and students. For additional information regarding Turnitin please visit the ANU Online website.
Workload
Up to 36 one-hour lectures and eight two-hour labs plus self study for a total of 130 hours.
Inherent Requirements
None.
Requisite and Incompatibility
Prescribed Texts
None
Fees
Tuition fees are for the academic year indicated at the top of the page.
Commonwealth Support (CSP) Students
If you have been offered a Commonwealth supported place, your fees are set by the Australian Government for each course. At ANU 1 EFTSL is 48 units (normally 8 x 6-unit courses). More information about your student contribution amount for each course at Fees.
- Student Contribution Band:
- 2
- Unit value:
- 6 units
If you are a domestic graduate coursework student with a Domestic Tuition Fee (DTF) place or international student you will be required to pay course tuition fees (see below). Course tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.
Where there is a unit range displayed for this course, not all unit options below may be available.
| Units | EFTSL |
|---|---|
| 6.00 | 0.12500 |
Course fees
- Domestic fee paying students
| Year | Fee |
|---|---|
| 2024 | $4980 |
- International fee paying students
| Year | Fee |
|---|---|
| 2024 | $6360 |
Offerings, Dates and Class Summary Links
ANU utilises MyTimetable to enable students to view the timetable for their enrolled courses, browse, then self-allocate to small teaching activities / tutorials so they can better plan their time. Find out more on the Timetable webpage.
