MATHS 1004  Mathematics for Data Science I
North Terrace Campus  Semester 2  2020

General Course Information
Course Details
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 highestpaying 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 Staff
Course Coordinator: Dr Raymond Vozzo
Course Timetable
The full timetable of all activities for this course can be accessed from Course Planner.

Learning Outcomes
Course Learning Outcomes
On 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 problemsolving in context.
4. Use appropriate technology to aid problemsolving 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
3,4,5 
Learning Resources
Recommended Resources
 Lay: Linear Algebra and its Applications 4th ed. (Addison Wesley Longman)
 Stewart: Calculus 7th ed. (international ed.) (Brooks/Cole)
 Graham, Knuth, Patashnik: Concrete Mathematics (AddisonWesley)
 Deisenroth, Faisal, Ong: Mathematics for Machine Learning (Cambridge University Press)

Learning & Teaching Activities
Learning & Teaching Modes
This course relies on (online) 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.Workload
No information currently available.
Learning Activities Summary
Lecture Outline
Fundamentals (weeks 13)
 Approximation
 Functions
 Summation
 Series Approximation
 Induction
Linear Algebra (weeks 47)
 Vectors and matrices
 Systems of linear equations
 Eigenvalues and eigenvectors
 Dimension reduction
Probability (weeks 89)
 Counting
 Discrete random variables
 Conditional probability
 Bayes theorem
Calculus (weeks 1012)
 Differential calculus for optimisation
 Integration and continuous probability distributions
 Gradient descent

Assessment
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 Summary
Component
Weighting Assignments 25% Quizzes 10% Lab & tutorial participation 5% Test 1 15% Test 2 15% Exam 30% Assessment Related Requirements
An 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 Detail
Written 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.Submission
 All written assignments are to be esubmitted following the instructions on MyUni.
 Late assignments will not be accepted without a medical certificate.
 Written assignments will have a one week turnaround time for feedback to students.
Course Grading
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 149 Fail P 5064 Pass C 6574 Credit D 7584 Distinction HD 85100 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.

Student Feedback
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 ongoing 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.

Student Support
 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 assessmentrelated 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

Fraud Awareness
Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zerotolerance 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 studentâ€™s disciplinary procedures.
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