COMP SCI 7319OL - Big Data Analysis & Industry Project

Online - Online Teaching 4 - 2022

In this course, students will complete a medium-scale, industry-inspired, data science project. This project will involve evaluating, selecting and applying relevant data science techniques, principles and theory to a data science problem. Working with a real-world, industry-motivated dataset, students will develop their data science skills and knowledge and demonstrate autonomy, initiative and accountability. Students will need to reflect on the nature of their data and identify any social and ethical concerns and identify appropriate ethical frameworks for data management. As part of the course students will deliver a written and oral presentation of their project design, plan, methodologies, and outcomes.

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Course Details

Course Code	COMP SCI 7319OL
Course	Big Data Analysis & Industry Project
Coordinating Unit	Computer Science
Term	Online Teaching 4
Level	Postgraduate Coursework
Location/s	Online
Units	3
Contact	Up to 4 hours per week
Available for Study Abroad and Exchange	N
Prerequisites	Carousel 2 Courses: COMP SCI 7211OL, DATA 7301OL, DATA 7302OL & MATHS 7027OL
Restrictions	Master of Data Science (Applied) OL Only
Assessment	Assignments

Course Staff

Course Coordinator: Dr Wei Zhang

Course Timetable

The full timetable of all activities for this course can be accessed from .

Learning Outcomes

Course Learning Outcomes

Deep discipline knowledge an intellectual breadth
Graduates have comprehensive knowledge and understanding of their subject area, the ability to engage with different traditions of thought, and the ability to apply their knowledge in practice including in multi-disciplinary or multi-professional contexts.

Creative and critical thinking and problem solving
Graduates are effective problems-solvers, able to apply critical, creative and evidence-based thinking to conceive innovative responses to future challenges.

Teamwork and communication skills
Graduates convey ideas and information effectively to a range of audiences for a variety of purposes and contribute in a positive and collaborative manner to achieving common goals.

Professionalism and leadership readiness
Graduates engage in professional behaviour and have the potential to be entrepreneurial and take leadership roles in their chosen occupations or careers and communities.

Intercultural and ethical competency
Graduates are responsible and effective global citizens whose personal values and practices are consistent with their roles as responsible members of society.

Aboriginal and cultural competency
Graduates have an understanding of, and respect for, Australian Aboriginal values, culture and knowledge.

Digital capabilities
Graduates are well prepared for living, learning and working in a digital society.

Self-awareness and emotional intelligence
Graduates are self-aware and reflective; they are flexible and resilient and have the capacity to accept and give constructive feedback; they act with integrity and take responsibility for their actions.

University Graduate Attributes

No information currently available.
Learning & Teaching Activities

Learning & Teaching Modes

This is a project-based course. The course has six modules as following:
1. Fundamentals of Big Data
2. Big Data techniques
3. Generic Data Modelling
4. Image Data Modelling
5. Time series Data Modelling
6. Advanced machine learning tools

There are two project-based assessments:
Assessment 1 is about big data analysis
Assessment 1 is about image and text data analysis

Workload

No information currently available.

Learning Activities Summary

No information currently available.

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 Summary

There are two project-based assessments:
Assessment 1 is a project about big data analysis.
Assessment 2 is a project on image and text data analysis.

Assessment Detail

No information currently available.

Submission

No information currently available.

Course Grading

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 .

Student Feedback

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.

SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.
Student Support
Counselling for Fully Online Postgraduate Students

Fully online students can access counselling services here:

Phone: 1800 512 155 (24/7)

SMS service: 0439 449 876 (24/7)

Email: info@assureprograms.com.au

Go to the to learn more, or speak to your Student Success Advisor (SSA) on 1300 296 648 (Monday to Thursday, 8.30am–5pm ACST/ACDT, Friday, 8.30am–4.30pm ACST/ACDT)
Policies & Guidelines
This section contains links to relevant assessment-related policies and guidelines - all university policies.
Fraud Awareness

Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student鈥檚 disciplinary procedures.

The 成人大片 is committed to regular reviews of the courses and programs it offers to students. The 成人大片 therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.

Authorised by:	Deputy Vice-Chancellor and Vice-President (Academic)
Maintained by:	Course Outlines Administrator

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