Download Course Outline 2016 INFOSYS 722: Data Mining

Course Outline 2016
INFOSYS 722: Data Mining and Big Data (15 POINTS)
Semester 2 (1165)
Course Prescription
Data mining and big data involves storing, processing, analysing and making sense of huge
volumes of data extracted in many formats and from many sources. Using information
systems frameworks and knowledge discovery concepts, this project-based and research
oriented course uses latest published research and cutting-edge business intelligence tools
for data analytics.
Programme and Course Advice
None
Goals of the Course
The primary objectives of the course are to:
1. Learn how to critically review literature in the area of business intelligence, big data,
business data analytics, visualisation and relevant information systems architectures;
2. investigate links between big data, business data analytics and emerging
technologies;
3. develop an understanding of the process of decision making; and
4. apply knowledge gained to design, implement and evaluate an information
system – with reference to big data, data analytics and visualisation for an
identified business case study;
Learning Outcomes
By the end of this course it is expected that the student will be able to:
1. critically read, discuss and write about published research;
2. develop a holistic research-based roadmap of the big data and business analytics
fields; and
3. analyse, design and build an information system using emerging tools and technologies
(including big data and business data analytics) for an identified business problem.
Content Outline
Week 1
Week 2
Week 3
Week 4
Week 5
Week 6
6 &6
Week 7
Week 8
Week 9
Week 10
Week 11
Week 12
Introduction to big data and business intelligence
Big data and its impacts
Exploration of tools used for harnessing big data
Capabilities of Business Intelligence and business data analytics
Technologies enabling insights and decisions regarding BI
Practical uses of Big Data and BI
Exploring various data mining techniques and BI tools
Exploring various BI Tools using case studies
Exploring various Big Data tools in use
Visualisation
Emerging technologies: links with data analytics
Presentations of Final Projects; handing in Research Essays
Learning and Teaching
Participants will be required to learn to use the library and other resources for
their literature searches, carefully read research papers, write summaries, locate
additional research papers, and discuss key research issues as part of the course.
The learning in this course is student-centred.
After the first two weeks during which time some of the important introductory papers
in the field will be discussed, class meetings will have a seminar and workshop format,
the success of which is heavily dependent on the active participation of the students.
Teaching Staff:
Dr. Ami Peiris
Room 468,
Owen G Glenn Building
Email:[email protected]
Phone: +64 (0)9 9235988
Learning Resources
There are no text-books for this course. Photocopied journal articles and book
chapters form an integral (and examinable) part of this course, and will be made
available on Canvas. You are strongly advised to prepare brief summaries as you
"digest" each reading. Especially useful in writing the research essay. Students
are also advised to take advantage of a n y software and library resources available.
Assessment
Assessment No
Course Component
Weight
1
Class presentations (paper Summaries)
20%
2
Project
25%
3
Research essay
40%
4
Labs
15%
Learning outcome/
Assessment
Assessment Assessment Assessment Assessment
1
2
3
4
1
X
2
X
3
X
X
X
X
X
X
X
Inclusive Learning
Students are urged to discuss privately any impairment-related requirements face-toface and/or in written form with the course lecturer and/or tutor.