MATHS 1006 - Data Taming & Prediction
North Terrace Campus - Semester 2 - 2024
General Course Information
Course Code MATHS 1006 Course Data Taming & Prediction Coordinating Unit Mathematical Sciences Term Semester 2 Level Undergraduate Location/s North Terrace Campus Units 3 Available for Study Abroad and Exchange Y Incompatible APP DATA 2015 Course Description This course is a practical introduction to the practice of wrangling, finding relationships in, and making predictions from, messy datasets using statistical methods. The course introduces the principle of tidy data, types of data and data formats, exploratory data analysis, data transformation, as well as model fitting and prediction using statistical machine learning tools. A focus will be to introduce R programming for data science applications, particularly through real-world case studies.
Course Coordinator: Dr Lauren KennedyLecturer: Louise Campbell
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. Describe the principles of data taming and approaches used to tidy data.
2. Identify the different types of data and data variables.
3. Select from data analysis and visualisation techniques to create a linear model and make predictions from it.
4. Execute techniques to transform, reduce and summarise data in order to visualise it.
5. Articulate the ideas that data scientists consider when looking at data.
6. Communicate professionally on the application of linear models through the use of real-world case studies.
University Graduate Attributes
No information currently available.
Required ResourcesAll required resources will be provided through MyUni.
Learning & Teaching Activities
Learning & Teaching ModesThis course uses a flipped classroom approach -- there will be prescribed material to consume in preparation for a weekly active learning session, as well as tutorials and computer labs.
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
TOTAL 156 Activity Quantity Workload hours Weekly preparation 12 weeks 36 Workshops 12 12 Practicals 12 24 Quizzes 12 12 Assignments 5 50 Final report 1 22
Learning Activities Summary
Schedule Week 1 Intro to R and data frames Week 2 Cleaning data and text manipulation Week 3 Reproducible research and Rmarkdown Week 4 Summarising data and interpreting plots Week 5 Transforming data Week 6 Linear regression Week 7 Multiple regression Week 8 Classification and cross-validation Week 9 Predicting with curved lines/classification Week 10 Predicting with fancy lines Week 11 Case Study Week 12 Revision and final report
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.
Task Weighting Learning Outcomes Assignments 25% (5% each) 1-6 Quizzes 10% 1-5 Final Report 40% 1-6 Mid-semester test 20% 1-6 Participation 5% 1-6
Assessment Task Due Weighting Learning Outcome Assignment 1
Fri, week 2
5% 1, 2 Assignment 2 Fri, week 4 5% All Assignment 3 Fri, week 6 5% All Assignment 4 Fri, week 8 5% All Assignment 5 Fri, week 10 5% All Weekly quizzes Monday of each week (excl wk1) 10% total All Mid-semester test Week 10 20% All Final report End of SWOT 40% All
SubmissionAll submissions will be via electronic submission on MyUni. Any written assignments will be tested for plagiarism through Turnitin.
Assignments will have a two week turn-around time for feedback to students.
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.
- Academic Integrity for Students
- Academic Support with Maths
- Academic Support with writing and study skills
- Careers Services
- International Student Support
- Library Services for Students
- LinkedIn Learning
- Student Life Counselling Support - Personal counselling for issues affecting study
- Students with a Disability - Alternative academic arrangements
- YouX Student Care - Advocacy, confidential counselling, welfare support and advice
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
- Elder Conservatorium of Music Noise Management Plan
- 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
Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student’s disciplinary procedures.
The University of Adelaide is committed to regular reviews of the courses and programs it offers to students. The University of Adelaide therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.