APP DATA 3010 - Advanced Data Analysis III
North Terrace Campus - Semester 1 - 2022
General Course Information
Course Code APP DATA 3010 Course Advanced Data Analysis III Coordinating Unit School of Physical Sciences Term Semester 1 Level Undergraduate Location/s North Terrace Campus Units 3 Contact Up to 7 hours per week Available for Study Abroad and Exchange Y Prerequisites APP DATA 2015 or STATS 2107 Incompatible STATS 3022 Course Description This course will introduce the fundamental concepts of modern data science. It will provide students with tools to deal with real, messy data, an understanding of the appropriate methods to use, and the ability to use these tools safely. Topics will include data structures; regression models including lasso regression, ridge regression and non-linearity with splines; classification models including logistic regression, linear discriminant analysis, support vector machines and random forests; and unsupervised learning methods such as principal component analysis, k-means and hierarchical clustering. The practical skills will be focused on data science in R.
Course Coordinator: Dr Graham Heinson
The full timetable of all activities for this course can be accessed from Course Planner.Wednersday 4 -5
As this is a small class, we will have further tutorial sessions with you in times that suit you.
Course Learning OutcomesOn successful completion of this course, students will be able to:
1. Demonstrate an understanding of the foundational principles of machine learning
2. Recognise which method to use for a given data analysis problem
3. Demonstrate an understanding the statistical underpinning of the chosen method
4. Implement safely any chosen method and interpret the results.
5. Apply the theory in the course to solve a range of problems at an appropriate level of difficulty.
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.
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 6: Australian Aboriginal and Torres Strait Islander cultural competency
Graduates have an understanding of, and respect for, Australian Aboriginal and Torres Strait Islander values, culture and knowledge.
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.
Learning & Teaching Activities
Learning & Teaching ModesThe structure of the course consists of :
Weekly topic videos watched in own time.One interpretation workshop a week held in the lecture time.One implementation workshop a week held in practical time.
The topics covered will include:
Overview of modelling frameworkPre-processingModel theory
LDA / SVM
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 Task Type of Assessment Percentage of total assessment for grading Hurdle Yes or No Course larning outcomes Approximate timing Online quizzes Summative 5 No 1,2 Weekly Written assignments Summative 15 No 4,5 Weeks 4,8,12 Test Summative 30 No 1,2,3 Weeks 2,6,10 Written exam Summative 30 No 1,2,3 Exam period Practical exam Summative 20 No 4,5 Exam period
Assessment DetailOnline quizzes:
continued reinforcement of course materials on a weekly basis.
four-week projects using R to apply the theory in the course to solve a
range of problems at an appropriate level of difficulty
in-class assessment every 4 weeks for continued
reinforcement of course materials and to provide mechanism for feedback on
demonstrate proficiency in understanding key data science concepts.
demonstrate proficiency in implementation of key data science concepts.
No information currently available.
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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