COMP SCI 4403 - Mining Big Data
North Terrace Campus - Semester 2 - 2016
General Course Information
Course Code COMP SCI 4403 Course Mining Big Data Coordinating Unit School of Computer Science Term Semester 2 Level Undergraduate Location/s North Terrace Campus Units 3 Contact Up to 2 hours per week Available for Study Abroad and Exchange Y Prerequisites COMP SCI 2201 Course Description The Web and Internet Commerce provide extremely large datasets from which important information can be extracted by data mining. This course will cover practical algorithms for solving key problems in mining of massive datasets. It focuses on parallel algorithmic techniques that are used for large datasets in the area of cloud computing. Furthermore, stream processing algorithms for data streams that arrive constantly, page ranking algorithms for web search, and online advertisement systems are studied in detail.
Course Coordinator: Associate Professor Markus Wagner
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning Outcomes1. To develop knowledge of algorithms for massive data sets and methodologies in the context of data mining.
2. To gain experience in matching various algorithms for particular classes of problems.
3. To gain experience in applying and developing algorithms as a part of software development for mining big data.
4. Read and understand scientific research papers in the area of big data, critically evaluate research papers, and present them in a seminar talk.
University Graduate Attributes
This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:
University Graduate Attribute Course Learning Outcome(s) Deep discipline knowledge
- informed and infused by cutting edge research, scaffolded throughout their program of studies
- acquired from personal interaction with research active educators, from year 1
- accredited or validated against national or international standards (for relevant programs)
1,2,3,4 Critical thinking and problem solving
- steeped in research methods and rigor
- based on empirical evidence and the scientific approach to knowledge development
- demonstrated through appropriate and relevant assessment
1,2,3,4 Teamwork and communication skills
- developed from, with, and via the SGDE
- honed through assessment and practice throughout the program of studies
- encouraged and valued in all aspects of learning
1,2,3 Career and leadership readiness
- technology savvy
- professional and, where relevant, fully accredited
- forward thinking and well informed
- tested and validated by work based experiences
4 Intercultural and ethical competency
- adept at operating in other cultures
- comfortable with different nationalities and social contexts
- able to determine and contribute to desirable social outcomes
- demonstrated by study abroad or with an understanding of indigenous knowledges
1,2,3 Self-awareness and emotional intelligence
- a capacity for self-reflection and a willingness to engage in self-appraisal
- open to objective and constructive feedback from supervisors and peers
- able to negotiate difficult social situations, defuse conflict and engage positively in purposeful debate
Required ResourcesThe textbook for this course is: Anand Rajaraman, Jeffrey Ullman: Mining Massive Datasets, Cambridge University Press, 2012
Recommended ResourcesDuring the course, additional literature (available from Internet) will be recommended as additional reading.
Online LearningThe course will use the University's learning management Canvas. System. Students are expected to check the forum on a regular basis for announcements relating to the course and projects.
Learning & Teaching Activities
Learning & Teaching Modes
No information currently available.
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Learning Activities Summary
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The University's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- Assessment must maintain academic standards.
Assessment Summary3 assignments based on the material presented in the lecture (weight 10% each).
1 open-ended individual project (weight 70%).
The due dates will be announced through the course website.
Assessement Type Weight Learning objectives Abstraction Design Communication Data Programming Assignment 1 Formative 10% 1,2,3 4 4 4 5 5 Assignment 2 Formative 10% 1,2,3 5 5 4 5 5 Assignment 3 Formative 10% 1,2,3 5 5 4 5 5 Open-ended project
70% 2,4 5 5 5 5 5
CBOK categories are explained in section 4 of the ICT core body of knowlege. Numbers assigned correspond to the Bloom taxonomy (see page 26 of the same document).
SubmissionAll work will be submitted through the course forum.
Grades for your performance in this course will be awarded in accordance with the following scheme:
M10 (Coursework Mark Scheme) Grade Mark Description FNS Fail No Submission F 1-49 Fail P 50-64 Pass C 65-74 Credit D 75-84 Distinction HD 85-100 High Distinction CN Continuing NFE No Formal Examination RP Result Pending
Further details of the grades/results can be obtained from Examinations.
Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.
Final results for this course will be made available through Access Adelaide.
The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.
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