UO Mathematics Essentials in Data Analytics

Course aim

To provide students with working knowledge of mathematical & statistical concepts relevant to applications in data analytics.

Course content

Topics covered in this course include: linear algebra concepts, vectors and matrices operations, eigenvalues & eigenvectors, dynamical systems, optimisation techniques (e.g. Gradient descent), linear regression, probability concepts (probability laws, Bayes' rule and independence), selected probability distributions, statistical inference and applications to data analytics.

Textbooks

Nil

Prerequisites

Common to all relevant programs
Subject Area & Catalogue Number	Course Name
MATH 2032	UO R for Data Analytics
MATH 1080	UO Fundamentals of Mathematics for Data Analytics

Corequisite(s)

Nil

Teaching Method

Component	Duration
EXTERNAL, ONLINE ACTIVITY
Online	10 weeks x N/A

Note: These components may or may not be scheduled in every study period. Please refer to the timetable for further details.

Assessment

Problem solving exercise, Project

Fees

EFTSL*: 0.125
Commonwealth Supported program (Band 1)
To determine the fee for this course as part of a Commonwealth Supported program, go to:
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Fee-paying program for domestic and international students
International students and students undertaking this course as part of a postgraduate fee paying program must refer to the relevant program home page to determine the cost for undertaking this course.

Non-award enrolment
Non-award tuition fees are set by the university. To determine the cost of this course, go to:
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Not all courses are available on all of the above bases, and students must check to ensure that they are permitted to enrol in a particular course.

* Equivalent Full Time Study Load. Please note all EFTSL values are published and calculated at ten decimal places. Values are displayed to three decimal places for ease of interpretation