MATHS 7027 - Mathematical Foundations of Data Science
North Terrace Campus - Trimester 1 - 2024
General Course Information
Course Code MATHS 7027 Course Mathematical Foundations of Data Science Coordinating Unit Mathematical Sciences Term Trimester 1 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 SACE Stage 2 Mathematical Methods Restrictions Not available to MMaSc students. Course Description This course introduces fundamental mathematical concepts relevant to computer science and provides a basis for further postgraduate study in data science, statistical machine learning, and cybersecurity. Topics covered are include: probability: sets, counting, probability axioms, Bayes theorem; optimisation and calculus: differentiation, integration, functions of several variables, series approximations; linear algebra: vector and matrices, matrix algebra, vector spaces; discrete mathematics and statistics: linear regression, linear least squares, regularisation. Applications of the theory to data science and machine learning will be developed.
Course Coordinator: Mr Max Glonek
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:
- Demonstrate understanding of basic mathematical concepts in data science, relating to linear algebra, probability, and calculus.
- Employ methods related to these concepts in a variety of data science applications.
- Apply logical thinking to problem-solving in context.
- Demonstrate skills in writing mathematics.
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.
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.
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.
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.
Required ResourcesAll required resources are provided in MyUni. There is no requirement to buy a textbook.
- Lay: Linear Algebra and its Applications 4th ed. (Addison Wesley Longman)
- Stewart: Calculus 7th ed. (international ed.) (Brooks/Cole)
- Graham, Knuth, Patashnik: Concrete Mathematics (Addison-Wesley)
- Deisenroth, Faisal, Ong: Mathematics for Machine Learning (Cambridge University Press)
Learning & Teaching Activities
Learning & Teaching ModesThis course relies on seminars and practicals to guide students through the course material, workshops to provide students with class/small group/indvidual assistance, and a sequence of assignments to provide formative assessment opportunities for students to practise techniques and develop their understanding of the course.
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.This is a 3-unit course. In the semester or trimester format, you are expected to allocate the following study time to fully meet the Course Learning Outcomes (CLOs) for this course. Please note that students work at different paces, so this indicates the approximate time required to complete this course.
Learning Activity Hours / Week Duration Total Online Learning Activities 2 hours 12 weeks 24 hours Face to Face Learning Activities 2 hours 12 weeks 24 hours Independent Study 4 hours 12 weeks 48 hours Assessment Tasks 4.5 hours 12 weeks 54 hours Total 150 hours
Learning Activities Summary
Topic OutlineFundamentals (weeks 1-2)
- Sets and functions
- Sums and series
- Discrete random variables
- Conditional probability
- Bayes' theorem
- Matrix operations
- Matrix equations
- Row reduction
- Linear independence
- Eigenvalues and eigenvectors
- Dimension reduction
- Integration and continuous probability distributions
- Series approximations
- Gradient descent
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.
Component Weighting Assignments 25% Quizzes 10% Mid-Trimester Test 15% Exam 50%
Assessment Related Requirements
An aggregate score of 50% is required to pass the course.
This course also contains an exam hurdle. Students must achieve a grade of at least 40% in the final exam in order to pass the course.
No information currently available.
SubmissionAll written assignments are to be e-submitted following the instructions on MyUni.
See MyUni for more comprehensive details regarding assignment submission, our late policy etc.
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
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- Academic Support with writing and study skills
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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
- 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.
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