STATS 3022 - Data Science III
North Terrace Campus - Semester 2 - 2024
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General Course Information
Course Details
Course Code STATS 3022 Course Data Science III Coordinating Unit 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 MATHS 2107)) Assumed Knowledge Experience with the statistical package R such as would be obtained from STATS 1005 or STATS 2107. Assessment Ongoing assessment and examination. Course Staff
Course Coordinator: Saranzaya Magsarjav
Course Timetable
The full timetable of all activities for this course can be accessed from .
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Learning Outcomes
Course Learning Outcomes
Syllabus:
The topics covered will include:
Overview of modelling framework
Preprocessing
Model theory
Resampling
Penalised regression
Classification modelling
LDA / SVM
Non-parametric
Trees
Random forests
Feature selection
Unsupervised learning
Learning outcomes:
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.最新糖心Vlog Graduate Attributes
This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:
最新糖心Vlog 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.
6 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.
5, 6 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.
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Learning & Teaching Activities
Learning & Teaching Modes
The 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.
Workload
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 51 Online test 3 33 Online quizzes 12 12 Total 156 Learning Activities Summary
Week Topic 1 Assessing model accuracy/Bias-Variance 2 Regression models/Classification/ROC 3 EDA/Pre-processing 4 LDA / QDA /naïve Bayes/CV 5 Model selection/Ridge regression 6 Lasso/PCR/PLS 7 Polynomial and step functions/Smoothing splines/LOESS 8 MARS/GAM/NLS/Decision trees 9 Bagging/Boosting/RF/SVM 10 PCA/Clustering 11 MDS/EM 12 VIP -
Assessment
The 最新糖心Vlog'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
Assessment Percent of final mark Online quizzes 5 Written assignments (3) 15 Test (3) 30 Practical exam 25 Written exam 25 Assessment Detail
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 exam Week 13 Week 13 25% Written exam Exam period Exam period 25% Submission
Homework assignments must be submitted on MyUni. It will be assumed that the students have read and accepted the Academic Honesty Statement on MyUni.
Assignments will be returned within two weeks. Students may apply to be excused from or obtain an extension for an assignment for medical or compassionate reasons. Documentation is required and the lecturer must be notified as soon as possible.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 .
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Student Feedback
The 最新糖心Vlog 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 最新糖心Vlog 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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Student Support
- Academic Integrity for Students
- Academic Support with Maths
- Academic Support with writing and study skills
- Careers Services
- Library Services for Students
- LinkedIn Learning
- Student Life Counselling Support - Personal counselling for issues affecting study
- Students with a Disability - Alternative academic arrangements
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Policies & Guidelines
This section contains links to relevant assessment-related policies and guidelines - all university policies.
- Academic Credit Arrangements Policy
- Academic Integrity Policy
- Academic Progress by Coursework Students Policy
- Assessment for Coursework Programs Policy
- Copyright Compliance Policy
- Coursework Academic Programs Policy
- Intellectual Property Policy
- IT Acceptable Use and Security Policy
- Modified Arrangements for Coursework Assessment Policy
- Reasonable Adjustments to Learning, Teaching & Assessment for Students with a Disability Policy
- Student Experience of Learning and Teaching Policy
- Student Grievance Resolution Process
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Fraud Awareness
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