• Offered by Research School of Computer Science
  • ANU College ANU College of Engineering and Computer Science
  • Course subject Computer Science
  • Academic career Postgraduate
  • Course convener
    • Dr Ramesh Sankaranarayana
  • Mode of delivery In Person
  • Co-taught Course COMP2420
  • Offered in First Semester 2018
    See Future Offerings

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

1. Demonstrate a conceptual understanding of database systems and architecture, data models and declarative query languages
2. Define, query and manipulate a relational database
3. Demonstrate basic knowledge and understanding of descriptive and predictive data analysis methods, optimization and search, and knowledge representation.
4. Formulate and extract descriptive and predictive statistics from data
5. Analyse and interpret results from descriptive and predictive data analysis
6. Apply their knowledge to a given problem domain and articulate potential data analysis problems
7. Identify potential pitfalls, and social and ethical implications of data science
8. Explain key security concepts and the use of cryptographic techniques, digital signatures and PKI in security

Indicative Assessment

Assignments (50%); Final Exam (50%)

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.

Requisite and Incompatibility

To enrol in this course you must have successfully completed COMP6710. You are not able to enrol in this course if you have previously completed COMP2420. Students enrolled in the Master of Cyber Security, Strategy & Risk Management must contact CECS Student Services to request a permission code for the course.

Fees

Tuition fees are for the academic year indicated at the top of the page.  

If you are a domestic graduate coursework or international student you will be required to pay tuition fees. Tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.

Student Contribution Band:
Band 2
Unit value:
6 units

If you are an undergraduate student and 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). You can find your student contribution amount for each course 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
Domestic fee paying students
Year Fee
2018 $4080
International fee paying students
Year Fee
2018 $5400
Note: Please note that fee information is for current year only.

Offerings and Dates

The list of offerings for future years is indicative only

First Semester

Class number Class start date Last day to enrol Census date Class end date Mode Of Delivery
4743 19 Feb 2018 27 Feb 2018 31 Mar 2018 25 May 2018 In Person

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