COMP SCI 7209 - Big Data Analysis and Project
North Terrace Campus - Semester 2 - 2021
General Course Information
Course Code COMP SCI 7209 Course Big Data Analysis and Project Coordinating Unit School of Computer Science Term Semester 2 Level Postgraduate Coursework Location/s North Terrace Campus Units 3 Contact Up to 2 hours per week Available for Study Abroad and Exchange N Restrictions Master of Data Science only Course Description In this course, you will complete a medium-scale 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 dataset, you will further develop your data science skills and knowledge and demonstrate autonomy, initiative and accountability. You will need to reflect on the nature of your data and identify any social and ethical concerns and identify appropriate ethical frameworks for data management. As part of the course you will deliver a written and oral presentation of your project design, plan, methodologies, and outcomes.
Course Coordinator: Dr Wei Zhang
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning OutcomesOn successful completion of this course students will be able to:
1. Evaluate, select and apply data analysis techniques, principles and theory;
2. Develop algorithms for the statistical analysis of big data;
3. Plan and execute a project;
4. Work autonomously using your own initiative;
University Graduate Attributes
University Graduate Attribute Course Learning Outcome(s)
- 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)
- steeped in research methods and rigor
- based on empirical evidence and the scientific approach to knowledge development
- demonstrated through appropriate and relevant assessment
- 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
- technology savvy
- professional and, where relevant, fully accredited
- forward thinking and well informed
- tested and validated by work based experiences
- 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
- a capacity for self-reflection and a willingness to engage in self-appraisal
- open to objective and constructive feedback from supervisors and peers
Required Resources1. No textbook required.
2. Knowing some basic statistics, probability, linear algebra and optimisation.
3. Ability to program in Matlab, or Python is required. C/C++ is a plus.
1. Pattern Recognition and Machine Learning by Bishop, Christopher M.
2. The elements of statistical learning: https://web.stanford.edu/~hastie/Papers/ESLII.pdf
3. Deep learning book: https://www.deeplearningbook.org/
The first two books are for statistical learning and data analysis in general.
The last one is about Deep Learning/Neural Networks techniques.
Online LearningOur course forum is accessible via the Canvas.
Excellent external courses available online:
3) Stanford Machine Learning Course: https://see.stanford.edu/course/cs229
Learning & Teaching Activities
Learning & Teaching ModesThe course will be primarily delivered through two activities:
Lectures will introduce and motivate very basic concepts of a few widely used data analytics methods. The two projects will reinforce concepts by their application to problem solving. This will be done via programming work, presentations and reports.
WorkloadThe information below is provided as a guide to assist students in engaging appropriately with the course requirements.
This is a 3-unit course. Students are expected to spend about 8 hours per week on the course. This includes a 1-hour lecture (on average), 2-hour self study and up to 5 hours per week on completing projects.
Project work will be subjected to deadlines. Students are expected to manage their time effectively to allow timely submission, especially with consideration to workload of other courses.
Learning Activities SummaryStudents are encouraged to attend lectures as material presented in lectures often includes more than is on the slides. Students are also encouraged to ask questions during the lectures. Slides will be available via the subject web page.
Specific Course Requirements1. Knowing some basic statistics, probability, linear algebra and optimisation;
2. Ability to program in Matlab or Python. C/C++ is a plus.
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment must maintain academic standards.
Assessment SummaryAssessment Summary
The course includes the following assessment components:
Project 1: 40%
- Project Presentation: 10%
- Project report and code implementation: 30%
Project 1: 60%
- Project Presentation: 15%
- Project report and code implementation: 45%
Component Weighting Learning Outcomes CBOK Areas
Presentation: 10% and 15%, 2 in total 1,2,3,4 1,2,4
Report and code: 30% and 45% 2 in total 1,2,3,4 1,2,3,4,7,8,9,10,11
4 Interpersonal Communication
5 Societal Issues
6 History & Status of the Discipline
7 Hardware & Software
8 Data & Information
10 Human Computer Interfaces
11 Systems Development
Details of the Australian Computer Society's Core Bode of Knowledge (CBOK) can be found in this document: https://www.acs.org.au/accreditedcourses-and-jobs.
Assessment DetailEach student is expected to complete assignments in the form of report and programming work. The assignments must be completed individually (excluding group work) and all submissions are to be made under the declaration of adherring to the academic honesty principles. Submissions will be subjected to plagiarism checks. This course has a zero-tolerance policy towards academic honesty violations. Offenders will be duly subjected to university procedures for dealing with academic honesty cases.
SubmissionAssignment solutions are to be submitted through canvas.
No physical submissions of work will be accepted unless specifically requested by the lecturer.
Marks will be capped for late submissions, based on the following schedule:
1 day late – mark capped at 75%
2 days late – mark capped at 50%
3 days late – mark capped at 25%
more than 3 days late – no marks available.
Extensions to due dates will only be considered under exceptional medical or personal conditions and will not be granted on the last day due, or retrospectively. Applications for extensions must be made to the course coordinator by e-mail or hard copy and must include supporting documentation – medical certificate or letter from the student counselling service.
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.
Final results for this course will be made available through Access Adelaide.
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