MATH 2009 Introduction to Data Science
Credit Points 10
Legacy Code 301033
Coordinator Liwan Liyanage Opens in new window
Description Analysis of data is essential for scientific investigation, modelling processes and predicting future events. Data Science is the investigation of the tools required that allow us to perform this modelling and prediction. The increase in accessible data over the past few decades has promoted the use of Data Science, making it a desired skill in many professions. In this unit we further investigate the methods of regression, clustering and classification that form the basis of a data scientist's toolbox.
School Computer, Data & Math Sciences
Discipline Computer Science, Not Elsewhere Classified.
Student Contribution Band HECS Band 2 10cp
Check your HECS Band contribution amount via the Fees page.
Level Undergraduate Level 2 subject
Pre-requisite(s) For students NOT enrolled in 3769 Bachelor of Data Science or 3770 Bachelor of Applied Data Science - MATH 1028 Statistical Decision Making or MATH 1003 Biometry or MATH 1030 Statistics for Business
Co-requisite(s) For students enrolled in 3769 Bachelor of Data Science or 3770 Bachelor of Applied Data Science - MATH 1033 Thinking About Data
Assumed Knowledge
Computer Programming.
Assessment
The following table summarises the standard assessment tasks for this subject. Please note this is a guide only. Assessment tasks are regularly updated, where there is a difference your Learning Guide takes precedence.
| Item | Length | Percent | Threshold | Individual/Group Task |
|---|---|---|---|---|
| Online Quizzes | 10 min per quiz | 20 | N | Individual |
| Assignment 1 | to consist of 10 or so pages of text, not including code and output | 30 | N | Individual |
| Oral exam | 15 min per student | 10 | Y | Individual |
| Assignment 2 | to consist of 10 or so pages of text, not including code and output | 40 | N | Individual |
Teaching Periods
Sydney City Campus - Term 1
Sydney City
Day
Subject Contact Antoinette Cevenini Opens in new window