STATS 3022 - Data Science III
North Terrace Campus - Semester 2 - 2022
General Course Information
Course Code STATS 3022 Course Data Science III Coordinating Unit School of Mathematical Sciences Term Semester 2 Level Undergraduate Location/s North Terrace Campus Units 3 Contact up to 3 hours per week Available for Study Abroad and Exchange Y Prerequisites STATS 2107 or (MATHS 2201 and MATHS 2202) or (MATHS 2106 and 2107) Assumed Knowledge Experience with the statistical package R such as would be obtained from STATS 1005 or STATS 2107. 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 Jono Tuke
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning OutcomesSyllabus:
The topics covered will include:
Overview of modelling framework
LDA / SVM
On successful completion of this course, students will:
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. Be confident to apply the methods to large datasets.
6. 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.
1, 2, 3, 4, 5, 6
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.
2, 3, 5, 6
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 7: Digital capabilities
Graduates are well prepared for living, learning and working in a digital society.
1, 2, 3, 4, 5, 6
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 consists of
- Weekly topic videos watched in own time.
- One workshop on Advanced R methods in the workshop time.
- One implementation workshop a week held in practical time.
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
Activity Quantity Workload hours Topic videos 12 12 Practicals 12 24 Advanced R workshop 12 24 Assignments 3 33 Online test 3 33 Online quizzes 12 12 Project 1 18 Total 156
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 Percent of final mark Online quizzes 5 Practical quizzes 5 Written assignments (3) 15 Test (3) 30 Project 20 Practical exam 25
Assessment Distributed Due Weighting A1 Week 2 Friday Week 4 5% A2 Week 6 Friday Week 8 5% A3 Week 10 Friday Week 12 5% Test 1 Week 2 10% Test 2 Week 6 10% Test 3 Week 10 10% Online quizzes Weekly Weekly 5% Practical quizzes Weekly Weekly 5% Practical exam Week 13 Week 13 25% Project Week 6 Week 12 20%
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.
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