MATHS 1004 - Mathematics for Data Science I
North Terrace Campus - Semester 2 - 2021
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 At least a C- in SACE Stage 2 Mathematical Methods (formerly Mathematical Studies) or 4 in International Baccalaureate Mathematics SL Incompatible MATHS 1008, MATHS 1010, MATHS 1012 Restrictions Not available for BMaSc or BMaSc(Adv) students Course Description Data science is one of the highest-paying graduate jobs, for those with the relevant mathematical training. This course introduces fundamental mathematical concepts relevant to data and 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, gradient descent; linear algebra: vectors and matrices, matrix algebra, vector spaces; discrete mathematics: induction, difference equations. The course draws connections between each of these fundamental mathematical concepts and modern data science applications, and introduces Python programming for data wrangling, algorithms, and visualisation.
Course Coordinator: Dr Stuart Johnson
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
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)
all 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
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 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 (weeks 1-2)
- Sets and Functions
- Sums and Series
Probability (weeks 3-4)
- Discrete random variables
- Bayes theorem
Representing Data with Matrices (weeks 5-6)
- Matrix operations
- Matrix equations
Solving Linear Equations (weeks 7-8)
- Row reduction
- linear independence
Dimensional Reduction (weeks 9-10
- Eigenvalues and eigenvectors
- Dimension reduction
Applications of Calculus (weeks 11-12)
- Integration and continuous probability distributions
- Series Approximations
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.
Weighting Assignments 20% Quizzes 10% Test 1 10% Test 2 10% Exam 50%
Assessment Related RequirementsAn aggregate score of 50% is required to pass the course.
Assessment details 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.
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 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
- LinkedIn Learning
Policies & Guidelines
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
- 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
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