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.

SchoolSchool Engineering&Mathematical Sciences

Credit points15

Subject Co-ordinatorDavid Farchione

Available to Study Abroad StudentsYes

Subject year levelYear Level 4 - UG/Hons/1st Yr PG

Exchange StudentsYes

Subject particulars

Subject rules

Prerequisites Must be enrolled in the Master of Data Science (SMDS)

Co-requisitesN/A

Incompatible subjects STA1SS; STA1LS; STA1PSY; STA1IDA; STA1STM; STA1CTS; BUS1BAN; ECO1ISB

Equivalent subjectsN/A

Special conditionsN/A

Readings

Resource TypeTitleResource RequirementAuthor and YearPublisher
ReadingsIntroductory Statistics: a problem-solving approachRecommendedKokoska, 2011Freeman
ReadingsManual for SPSS and R with Examples from the Life SciencesPrescribedFarchione, 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)

Subject options

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Start date between: and    Key dates

Melbourne, 2017, Semester 2, Day

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Enrolment information

Subject Instance Co-ordinatorDavid Farchione

Class requirements

Lecture Week: 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 Laboratory Week: 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.

Practical Week: 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 Class Week: 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."

Lecture Week: 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 elementComments% 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 examination60 01, 02, 03, 04, 05, 06