APP DATA 2015 - Statistical Inference and Machine Learning II
North Terrace Campus - Semester 2 - 2021
General Course Information
Course Code APP DATA 2015 Course Statistical Inference and Machine Learning II Coordinating Unit School of Physical Sciences Term Semester 2 Level Undergraduate Location/s North Terrace Campus Units 3 Contact Up to 7 hours per week Available for Study Abroad and Exchange Y Incompatible STATS 7107, DATA 7201OL, STATS 2107 Assumed Knowledge SCIENCE 1500 or MATHS 1004 or ECON 1008 Course Description The foundation of data analytics is based on statistics. This course will cover the concepts of random sampling and bias, experimental design, regression statistics and outliers, classification techniques, and unsupervised 'machine' learning. The course will use R as its core programming environments using real example data sets.
Course Coordinator: Dr Graham Heinson
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning OutcomesOn successful completion of this course, students will be able to:
1 Understand fundamentals of data statistics and inference 2 Understand the difference between supervised and unsupervised machine learning 3 Interpret data sets from different disciplines 4 Program using R
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) Deep discipline knowledge
- informed and infused by cutting edge research, scaffolded throughout their program of studies
- acquired from personal interaction with research active educators, from year 1
- accredited or validated against national or international standards (for relevant programs)
1,2,3 Critical thinking and problem solving
- steeped in research methods and rigor
- based on empirical evidence and the scientific approach to knowledge development
- demonstrated through appropriate and relevant assessment
3,4 Teamwork and communication skills
- developed from, with, and via the SGDE
- honed through assessment and practice throughout the program of studies
- encouraged and valued in all aspects of learning
3,4 Career and leadership readiness
- technology savvy
- professional and, where relevant, fully accredited
- forward thinking and well informed
- tested and validated by work based experiences
1,2,3,4 Intercultural and ethical competency
- adept at operating in other cultures
- comfortable with different nationalities and social contexts
- able to determine and contribute to desirable social outcomes
- demonstrated by study abroad or with an understanding of indigenous knowledges
1,2,3 Self-awareness and emotional intelligence
- a capacity for self-reflection and a willingness to engage in self-appraisal
- open to objective and constructive feedback from supervisors and peers
- able to negotiate difficult social situations, defuse conflict and engage positively in purposeful debate
Learning & Teaching Activities
Learning & Teaching ModesThis course consists of:
- Lectures: 12 x 1hr per week
- Computer Practicals: 12 x 4 hrs per week
- Workshops: 12 x 2hrs per week
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.A student enrolled in a 3 unit course, such as this, should expect to spend, on average 12 hours per week on the studies required. This includes both the formal contact time required to the course (e.g., lectures and practicals), as well as non-contact time (e.g., reading and revision)
Learning Activities Summary
No information currently available.
Specific Course RequirementsCompulsory attendance of the Computer practicals and Workshops is required as they address the key course learning objectives 1-4
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 Task Type
Due Learning Outcome Programming practical (Five biweekly) Summative
Weeks 2,4,6,8,10 4 Concept quizzes (Five biweekly) Summative 20% No Weeks 1,3,5,7,9 1,2,3 Discipline-specific research project Summative 50% No Weeks 11 or 12 1,2,4
Assessment Related RequirementsAttendance at practicals and workshops is compulsory. The learning outcomes for this course are substantially dependent on
this hands-on experience and practice. Therefore, missing any practicals or workshops without an allowed absence will result in a grade of FAIL being recorded for the course. Students are able to apply for an allowed absence to the Course Coordinator.
Assessment Item Requirement for Hurdle Is additional assessment available if student does not reach hurdle requirement? Details of additional assessment if known Practicals and workhops are
completion of all practicals, including attendance of ALL practical and
workshop sessions and reasonable attempt at ALL practical assessments
Missing any practical/workshop
class or failing to submit a reasonable attempt at any practical report in a
semester will result in a grade of FAIL being recorded for the course.
Students with medical or compassionate reasons for non-attendance will be given
an opportunity to compensate for missed practical/workshop sessions.
Assessment DetailProgramming practical (Five biweekly): Total of 30% of course grades
In this assessment, students will be required to demonstrate their ability to create R scripts to solve a specific statistical problem.
Online-upload on R scripts.
Concept quizzes (Five biweekly): Total of 20% of course grades
In this assessment, students will be required to demonstrate their understanding of concepts of data management and visualisation. Online upload of short written answers though MyUni, and embed figures and R scripts. We will not use multiple choice.
Discipline specific research project: Total of 50% of course grades
In this assessment, students will be required to identify a data set to work with (typically from their area of discipline) and build an R script to understand the statistical nature of their data. The purpose of this assessment is for the student to demonstrate their ability to apply what they have learned throughout the course in the creation of a document (about 2000 words and figures) including
programs (as an online appendix) to answer questions about discipline-specific data, along with a short video (10 minutes) explaining their work
SubmissionSubmission of Assigned Work
Instructions on submission of work will be available on MyUn
Extensions for Assessment Tasks
Extensions of deadlines for assessment tasks may be allowed for reasonable causes. Such situations would include compassionate and medical grounds of the severity that would justify the awarding of a supplementary examination. Evidence for the grounds must be provided when an extension is requested. Students are required to apply for an extension to the Course
Co-ordinator before the assessment task is due. Extensions will not be provided on the grounds of poor prioritising of time. The assessment extension application form can be obtained from: http://www.sciences.adelaide.edu.au/current/
Late submission of assessments
If an extension is not applied for, or not granted then a penalty for late submission will apply. A penalty of 10% of the value of the assignment for each calendar day that the assignment is late (i.e. weekends count as 2 days), up to a maximum of 50% of the available marks will be applied. This means that an assignment that is 5 days late or more without an approved extension can only receive a maximum of 50% of the marks available for that assignment.
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.
- Academic Credit Arrangement Policy
- Academic Honesty Policy
- Academic Progress by Coursework Students Policy
- Assessment for Coursework Programs
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- Modified Arrangements for Coursework Assessment
- Student Experience of Learning and Teaching Policy
- Student Grievance Resolution Process
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