COMP SCI 7209 - Big Data Analysis and Project
North Terrace Campus - Trimester 2 - 2022
General Course Information
Course Code COMP SCI 7209 Course Big Data Analysis and Project Coordinating Unit School of Computer Science Term Trimester 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 students 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 Alfred Krzywicki
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning OutcomesOn successful completion of this course, you will be able to:
1 Apply data analysis principles and theory to process and analyse real-world big data. 2 Evaluate data analysis techniques by them applying real-world datasets using benchmark metrics. 3 Develop algorithms based on learnt techniques for the statistical analysis of big data. 4 Identify project goals, develop project milestones and execute them accordingly. 5 Analyse known industry studies and simulate assessment by following industry standards.
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)
Attribute 1: Deep discipline knowledge and 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.
1, 2, 3
Attribute 2: 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.
Attribute 3: 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.
Attribute 4: 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.
Attribute 5: 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.
Attribute 7: Digital capabilities
Graduates are well prepared for living, learning and working in a digital society.
Attribute 8: 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.
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 MyUni.
Excellent external courses available online:
3) Stanford Machine Learning Course: https://see.stanford.edu/course/cs229
Learning & Teaching Activities
Learning & Teaching ModesThis course is delivered in a semester, trimester and intensive format, although enrolment options may be limited by availability.
This course offers opportunities for you to learn through blended learning approaches, meaning some of the learning is done autonomously online and some of the learning is done through face-to-face engagement. This blended approach is used to create a rich scaffolded and supportive learning experience.
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.This is a 3-unit course. In the semester or trimester format, you are expected to allocate the following study time to fully meet the Course Learning Outcomes (CLOs) for this course. Please note that students work at different paces, so this indicates the approximate time required to complete this course.
Learning Activity Hours/Week Duration Total Online learning activities 1 hour 12 weeks 12 hours Face-to-face learning activities 3 hours 12 weeks 36 hours Independent study 4 hours 12 weeks 48 hours Assessment tasks 5 hours 12 weeks 60 hours Expected total student workload: 156 hours
Learning Activities SummaryYou will be required to complete the online learning activities available on MyUni prior to regular face-to-face learning sessions. Throughout these autonomous tasks, you will have time to process new concepts and build foundational knowledge around them. In the face-to-face sessions, you will get a chance to apply that learning to build new skills and address real-world problems.
Learning activities, both online and face-to-face, are scaffolding to the learning builds throughout the course. Through this learning experience, you will be asked to draw on a range of lower-order and higher-order thinking skills.
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 Task Individual / Group Weighting Learning Outcome Report Individual 50% 1, 2, 5 Develop a tool for a specific purpose based data analysis Individual 50% 1, 2, 3, 4, 5
Assessment DetailFull descriptions of the assessment tasks and associated grading rubrics are in the Assignments space on the MyUni course site. You will have opportunities to get further clarification on assessment tasks as needed.
SubmissionUnless otherwise specified, submit all of your assessments to the Assignments space in the MyUni course site for this course. For written assessments, your submissions will go through Turnitin to check for originality. Make sure your submissions adhere to the University of Adelaide Academic Integrity policies.
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.
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.
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This section contains links to relevant assessment-related policies and guidelines - all university policies.
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