STATS 7022 - Data Science PG
North Terrace Campus - Semester 2 - 2021
General Course Information
Course Code STATS 7022 Course Data Science PG Coordinating Unit School of Mathematical Sciences Term Semester 2 Level Postgraduate Coursework Location/s North Terrace Campus Units 3 Contact Up to 3 hours per week Available for Study Abroad and Exchange Y Assumed Knowledge STATS 2107 or (MATHS 2201 and MATHS 2202) or (MATHS 2106 and 2107). 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
No information currently available.
Learning & Teaching Activities
Learning & Teaching ModesThe structure 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 information below is provided as a guide to assist students in engaging appropriately with the course requirements.
Activity Quantity Workload hours Topic videos 12 24 Interpretation workshop 12 24 Implementation workshop 12 24 Assignments 3 33 Online test 3 33 Online quizzes 12 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.
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- 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 Written assignments (3) 15 Test (3) 30 Written exam 30 Practical exam 20
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 TBD (week 12 or exam period) 20% Final exam Examination period 30%
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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