MATHS 1004 - Mathematics for Data Science I
North Terrace Campus - Semester 2 - 2019
General Course Information
Course Code MATHS 1004 Course Mathematics for Data Science I Coordinating Unit School of Mathematical Sciences Term Semester 2 Level Undergraduate Location/s North Terrace Campus Units 3 Contact Up to 5 hours per week Available for Study Abroad and Exchange Y Prerequisites SACE Stage 2 Mathematical Methods Incompatible MATHS 1008, MATHS 1010 Course Description This course introduces fundamental mathematical concepts relevant to computer science and provides a basis for further study in data science, statistics and cybersecurity. Topics covered are 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 number theory: induction, difference equations, prime numbers, division algorithm, Euclidean algorithm, modular arithmetic. Applications of the theory to data science and cryptology will be developed.
Course Coordinator: Dr Lewis Mitchell
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. Demonstrate understanding of basic mathematical concepts in data science, relating to linear algebra, probability, and calculus.
2. Employ methods related to these concepts in a variety of data science applications.
3. Apply logical thinking to problem-solving in context.
4. Use appropriate technology to aid problem-solving and data analysis.
5. Demonstrate skills in writing mathematics.
University Graduate Attributes
University Graduate Attribute Course Learning Outcome(s)
- 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)
- steeped in research methods and rigor
- based on empirical evidence and the scientific approach to knowledge development
- demonstrated through appropriate and relevant assessment
- 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 lectures and computer laboratories to guide students through the material, tutorial classes to provide students with class/small group/individual assistance,and a sequence of assignments to provide formative assessment opportunities for students to practise techniques and develop their understanding of the course.
No information currently available.
Learning Activities SummaryLecture Outline
Fundamentals (12 Lectures)
- Series Approximation
Linear Algebra (16 Lectures)
- Vectors and matrices
- Systems of linear equations
- Eigenvalues and eigenvectors
- Dimension reduction
Probability (8 Lectures)
- Discrete random variables
- Conditional probability
- Bayes theorem
Calculus (12 Lectures)
- Differential calculus for optimisation
- Integration and continuous probability distributions
- Gradient descent
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment must maintain academic standards.
Written Assignments: 5 x 5% each = 25% total
Lab and Tutorial Participation: 5% total
Final Exam: 70% total
Assessment Related RequirementsAn aggregate score of 50% is required to pass the course. Furthermore students must achieve at least 45% on the final examination to pass the course.
Assessment DetailWritten assignments are due every fortnight, the first is due in Week 3.
Labs are fortnightly beginning in Week 1. Tutorials are fortnightly beginning in Week 2.
Precise details of all of these will be provided on the MyUni site for this course.
- All written assignments are to be e-submitted following the instructions on MyUni.
- Late assignments will not be accepted without a medical certificate.
- Written assignments will have a one 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.
Final results for this course will be made available through Access Adelaide.
- Academic Support with Maths
- Academic Support with writing and speaking skills
- Student Life Counselling Support - Personal counselling for issues affecting study
- International Student Support
- AUU Student Care - Advocacy, confidential counselling, welfare support and advice
- Students with a Disability - Alternative academic arrangements
- Reasonable Adjustments to Teaching & Assessment for Students with a Disability Policy
Policies & Guidelines
- Academic Credit Arrangement Policy
- Academic Honesty Policy
- Academic Progress by Coursework Students Policy
- Assessment for Coursework Programs
- 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
- Student Experience of Learning and Teaching Policy
- Student Grievance Resolution Process