MATHS 1004 - Mathematics for Data Science I
North Terrace Campus - Semester 2 - 2019
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General Course Information
Course Details
Course Code MATHS 1004 Course Mathematics for Data Science I Coordinating Unit 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 Assessment on-going assessment, exam Course Staff
Course Coordinator: Professor Lewis Mitchell
Course Timetable
The full timetable of all activities for this course can be accessed from .
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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 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
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 (Addison-Wesley)
- Deisenroth, Faisal, Ong: Mathematics for Machine Learning (Cambridge University Press)
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Learning & Teaching Activities
Learning & Teaching Modes
This 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.Workload
No information currently available.
Learning Activities Summary
Lecture Outline
Fundamentals (12 Lectures)
- Approximation
- Functions
- Summation
- Series Approximation
- Induction
Linear Algebra (16 Lectures)
- Vectors and matrices
- Systems of linear equations
- Eigenvalues and eigenvectors
- Dimension reduction
Probability (8 Lectures)
- Counting
- Discrete random variables
- Conditional probability
- Bayes theorem
Calculus (12 Lectures)
- Differential calculus for optimisation
- Integration and continuous probability distributions
- Gradient descent
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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
Written Assignments: 5 x 5% each = 25% total
Lab and Tutorial Participation: 5% total
Final Exam: 70% totalAssessment 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 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.
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 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 .
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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 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.
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Student Support
- Academic Integrity for Students
- Academic Support with Maths
- Academic Support with writing and study skills
- Careers Services
- Library Services for Students
- LinkedIn Learning
- Student Life Counselling Support - Personal counselling for issues affecting study
- Students with a Disability - Alternative academic arrangements
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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
- 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
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Fraud Awareness
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鈥檚 disciplinary procedures.
The 成人大片 is committed to regular reviews of the courses and programs it offers to students. The 成人大片 therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.
