Financial Statistics

FIN10002 12.5 Credit Points Hawthorn Available to incoming Study Abroad and Exchange students

Duration

  • One Semester or equivalent

Contact hours

  • 36 contact hours

On-campus unit delivery combines face-to-face and digital learning.

2021 teaching periods

Hawthorn

Higher Ed. Semester 1 Higher Ed. Semester 2

Dates:
1 Mar 21 - 30 May 21

Results:
6 Jul 21

Last self enrolment:
14 Mar 21

Census:
31 Mar 21

Last withdraw without fail:
16 Apr 21

Dates:
2 Aug 21 - 31 Oct 21

Results:
7 Dec 21

Last self enrolment:
15 Aug 21

Census:
31 Aug 21

Last withdraw without fail:
17 Sep 21

More teaching periods

Swinburne Online

Teaching Period 1 Teaching Period 2

Dates:
8 Mar 21 - 6 Jun 21

Results:
29 Jun 21

Last self enrolment:
21 Mar 21

Census:
2 Apr 21

Last withdraw without fail:
23 Apr 21

Dates:
5 Jul 21 - 3 Oct 21

Results:
26 Oct 21

Last self enrolment:
18 Jul 21

Census:
30 Jul 21

Last withdraw without fail:
20 Aug 21


Prerequisites

Anti-requisite
Students that have successfully completed the unit below should not study FIN10002 as it is similar in content.
 
Alternative Tertiary Entry Program: Students who have passed FIN00001 are exempted from taking this unit and must select another unit in its place with advice from a Course Advice Specialist.
 

Aims and objectives

This unit provides students with an introduction to statistics within a financial context. Students will gain an appreciation of what statistical methods can achieve, as well as skills in preparing, analysing and interpreting business data and statistical analysis. Students will also learn how to apply analytical tools to visualise and analyse data. The focus of the unit is on data science as an analytical and decision-making tool, in a variety of business contexts, with a major emphasis on interpretation and application.

Students who successfully complete this unit will be able to:
 
1. Identify and apply commonly used techniques for data collection and analysis.
 
2. Apply fundamental concepts of probability and probability distributions to problems in business decision-making.
 
3. Apply statistical inference methods to conduct and explain the results of hypothesis testing.
 
4. Apply simple regression analysis to explain the relationship between variables to draw inferences about relationships.
 
5. Apply technological tools to analyse data for decision-making purposes