• Offered by Research School of Computer Science
  • ANU College ANU College of Engineering and Computer Science
  • Classification Advanced
  • Course subject Computer Science
  • Academic career PGRD
  • Course convener
    • Dr Peter Christen
  • Mode of delivery In Person
  • Co-taught Course
  • Offered in Second Semester 2018
    See Future Offerings

Real-world data are commonly messy, distributed, and heterogeneous. This course introduces core concepts of data cleaning and standardisation, and data integration, that are aimed at converting and mapping raw data into other formats that allow more efficient and convenient use and analysis of data. The courses also discusses data quality, management, and storage issues as relevant to data analytics.

Learning Outcomes

Upon successful completion, students will have the knowledge and skills to:

  1. Critically reflect upon different data sources, types, formats, and structures,
  2. Research, justify and apply data cleaning, preprocessing, and standardisation for data analytics,
  3. Apply data integration concepts and techniques to heterogeneous and distributed data,

  4. Interpret, assess and discuss data quality measurements,

  5. Research and justify advanced data wrangling, data integration, and database techniques as relevant to data analytics

Other Information

If you believe you meet the pre-requisites for this course through alternative means, please contact dataanalytics.cecs@anu.edu.au to apply for an exemption.

Indicative Assessment

  1. Written and practical assignments (40% each)  - LO 1 to 4
  2. Oral presentation and report (20%) - LO 5
  3. Final examination (40%) - LO 1 to 5
    1. 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.

      Requisite and Incompatibility

      To enrol in this course you must have either: Completed COMP7230 or COMP1730 or COMP6730; and COMP7240 or COMP6240 or COMP2400 or COMP3430. OR be enrolled in MSc Quantitative Biology and Bioinformatics or Adv version. Contact School for permission code

      You will need to contact the Research School of Computer Science to request a permission code to enrol in this course.

      Preliminary Reading

      Data Matching - Concepts and Techniques fro Record Linkage, Entity Resolution and Duplicate Detection,
      Peter Christen, Springer, 2012.
      For more information see: http://users.cecs.anu.edu.au/~christen/data-matching-book-2012.html


      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:
      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, 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.

      The list of offerings for future years is indicative only.
      Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.

      Second Semester

      Class number Class start date Last day to enrol Census date Class end date Mode Of Delivery Class Summary
      9922 23 Jul 2018 30 Jul 2018 31 Aug 2018 26 Oct 2018 In Person N/A

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