STATISTICAL SCIENCE
STA4SS
2017
Credit points: 15
Subject outline
This subject provides an introduction to applied and theoretical statistics. (The applied component of this subject is identical to the content covered in STA1LS.) It introduces students to the basic applied statistical methods used in the biological sciences, medical sciences, agricultural sciences, nutrition, and health sciences and also provides an introduction to the mathematical theory used in the area of statistics. The three main areas of study are descriptive statistics, probability, and statistical inference and the use of a statistical computing package is an integral part of this subject. Students will also gain an understanding of some of the statistical techniques used in the area of Data Science.
School: School Engineering&Mathematical Sciences
Credit points: 15
Subject Co-ordinator: David Farchione
Available to Study Abroad Students: Yes
Subject year level: Year Level 4 - UG/Hons/1st Yr PG
Exchange Students: Yes
Subject particulars
Subject rules
Prerequisites: Must be enrolled in the Master of Data Science (SMDS)
Co-requisites: N/A
Incompatible subjects: STA1SS; STA1LS; STA1PSY; STA1IDA; STA1STM; STA1CTS; BUS1BAN; ECO1ISB
Equivalent subjects: N/A
Special conditions: N/A
Learning resources
Readings
| Resource Type | Title | Resource Requirement | Author and Year | Publisher |
|---|---|---|---|---|
| Readings | Introductory Statistics: a problem-solving approach | Recommended | Kokoska, 2011 | Freeman |
| Readings | Manual for SPSS and R with Examples from the Life Sciences | Prescribed | Farchione, D. (2017) | La Trobe |
Graduate capabilities & intended learning outcomes
01. Convert data into information by using appropriate numerical and graphical summaries.
- Activities:
- Weeks 1 and 2 Lectures: The first two hours of lectures in week 1 and 2 introduce the common numerical and graphical summaries encountered in statistics and the third lecture in weeks 1 and 2 reinforce these summaries via practical examples. Weeks 2 and 3 Computer Labs: Students in this class work through practical examples using the SPSS and R package. Weeks 2 and 3 Practicals: Students in this class work through practical examples.
- Related graduate capabilities and elements:
- Literacies and Communication Skills(Writing,Quantitative Literacy)
- Literacies and Communication Skills(Writing,Quantitative Literacy)
- Inquiry and Analytical Skills(Critical Thinking,Creative Problem-solving,Inquiry/Research)
- Inquiry and Analytical Skills(Critical Thinking,Creative Problem-solving,Inquiry/Research)
- Inquiry and Analytical Skills(Critical Thinking,Creative Problem-solving,Inquiry/Research)
- Discipline -Specific Knowledge and Skills(Discipline-Specific Knowledge and Skills)
02. Calculate probabilities and other quantities from discrete and continuous probability distributions and by applying the basic rules of probability.
- Activities:
- Weeks 3, 4 and 5 Lectures: The first two hours of lectures in weeks 3, 4 and 5 provide an introduction to the topics of probability and probability distributions and the third lecture in weeks 3, 4 and 5 reinforce these topics via practical examples. Weeks 5 and 6 Computer Labs: Students in this class work through practical examples using the SPSS and R package. Weeks 4, 5 and 6 Practicals: Students in this class work through practical examples.
- Related graduate capabilities and elements:
- Literacies and Communication Skills(Writing,Quantitative Literacy)
- Literacies and Communication Skills(Writing,Quantitative Literacy)
- Inquiry and Analytical Skills(Critical Thinking,Creative Problem-solving,Inquiry/Research)
- Inquiry and Analytical Skills(Critical Thinking,Creative Problem-solving,Inquiry/Research)
- Inquiry and Analytical Skills(Critical Thinking,Creative Problem-solving,Inquiry/Research)
- Discipline -Specific Knowledge and Skills(Discipline-Specific Knowledge and Skills)
03. Identify and apply appropriate statistical inference methods for decision making.
- Activities:
- Weeks 6 to 11 Lectures: The first two hours of lectures in weeks 6 to 11 introduces the common statistical inference procedures for decision making and the third lecture in weeks 6 to 11 reinforce these procedures via practical examples. Weeks 7 to 12 Computer Labs: Students in this class work through practical examples using the SPSS and R package. Weeks 7 to 12 Practicals: Students in this class work through practical examples.
- Related graduate capabilities and elements:
- Literacies and Communication Skills(Writing,Quantitative Literacy)
- Literacies and Communication Skills(Writing,Quantitative Literacy)
- Inquiry and Analytical Skills(Critical Thinking,Creative Problem-solving,Inquiry/Research)
- Inquiry and Analytical Skills(Critical Thinking,Creative Problem-solving,Inquiry/Research)
- Inquiry and Analytical Skills(Critical Thinking,Creative Problem-solving,Inquiry/Research)
- Discipline -Specific Knowledge and Skills(Discipline-Specific Knowledge and Skills)
04. Compute, display and interpret numerical and graphical summaries, probabilities and various statistical inference procedures using the statistical software packages SPSS and R.
- Activities:
- Computer Labs: Students in this class work through practical examples using the SPSS and R package.
- Related graduate capabilities and elements:
- Literacies and Communication Skills(Writing,Quantitative Literacy)
- Literacies and Communication Skills(Writing,Quantitative Literacy)
- Inquiry and Analytical Skills(Critical Thinking,Creative Problem-solving,Inquiry/Research)
- Inquiry and Analytical Skills(Critical Thinking,Creative Problem-solving,Inquiry/Research)
- Inquiry and Analytical Skills(Critical Thinking,Creative Problem-solving,Inquiry/Research)
- Discipline -Specific Knowledge and Skills(Discipline-Specific Knowledge and Skills)
05. Apply basic mathematical theoretical techniques in the area of statistics.
- Activities:
- Weeks 5 to 10 Lectures: The final hour of lecture in weeks 5 to 10 introduces basic statistical theoretical techniques. Weeks 6 to 11 Practicals: Students in this class work through statistical theoretical problems.
- Related graduate capabilities and elements:
- Literacies and Communication Skills(Writing,Quantitative Literacy)
- Inquiry and Analytical Skills(Critical Thinking,Creative Problem-solving,Inquiry/Research)
- Inquiry and Analytical Skills(Critical Thinking,Creative Problem-solving,Inquiry/Research)
- Inquiry and Analytical Skills(Critical Thinking,Creative Problem-solving,Inquiry/Research)
- Discipline -Specific Knowledge and Skills(Discipline-Specific Knowledge and Skills)
06. Identify and apply appropriate statistical techniques which are used in the area of Data Science.
- Activities:
- Unscheduled Online Class: The material in this class provides an introduction to the use of statistics in Data Science.
- Related graduate capabilities and elements:
- Literacies and Communication Skills(Writing,Quantitative Literacy)
- Literacies and Communication Skills(Writing,Quantitative Literacy)
- Inquiry and Analytical Skills(Critical Thinking,Creative Problem-solving,Inquiry/Research)
- Inquiry and Analytical Skills(Critical Thinking,Creative Problem-solving,Inquiry/Research)
- Inquiry and Analytical Skills(Critical Thinking,Creative Problem-solving,Inquiry/Research)
- Discipline -Specific Knowledge and Skills(Discipline-Specific Knowledge and Skills)
Melbourne, 2017, Semester 2, Day
Overview
Online enrolment: Yes
Maximum enrolment size: N/A
Enrolment information:
Subject Instance Co-ordinator: David Farchione
Class requirements
LectureWeek: 31 - 43
One 1.0 hours lecture per week on weekdays during the day from week 31 to week 43 and delivered via face-to-face.
Computer LaboratoryWeek: 31 - 43
One 1.0 hours computer laboratory per week on weekdays during the day from week 31 to week 43 and delivered via face-to-face.
PracticalWeek: 31 - 43
One 1.0 hours practical per week on weekdays during the day from week 31 to week 43 and delivered via face-to-face.
Unscheduled Online ClassWeek: 31 - 43
One 1.0 hours unscheduled online class every two weeks on any day including weekend during the day from week 31 to week 43 and delivered via online.
"The material in this class provides an introduction to the use of statistics in Data Science."
LectureWeek: 31 - 43
One 2.0 hours lecture per week on weekdays during the day from week 31 to week 43 and delivered via face-to-face.
Assessments
| Assessment element | Comments | % | ILO* |
|---|---|---|---|
| Five Assignments (Equivalent to 1800 words) | 30 | 01, 02, 03, 04, 05 | |
| Written Project (Equivalent to 1000 words) | 10 | 01, 02, 03, 04, 06 | |
| One 3-hour examination | 60 | 01, 02, 03, 04, 05, 06 |