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EC5216 - Econometrics and Big Data Analysis
Credit points: |
3 |
Year: |
2023 |
Student Contribution Band: |
Band
4 |
Administered by: |
College of Business, Law & Governance |
In a day and age in which the availability of big data increases exponentially, the
value of the skills to correctly analyse such data increases accordingly. This subject
demonstrates a range of econometric models that can be used to interrogate or mine
large datasets to test theories and ideas. The subject focuses on the application
of these models, understanding their limitations and correctly interpreting their
results. The subject provides valuable skills to students in economics, finance or
any other discipline in which the analysis of big data is or will be important.
Learning Outcomes
- formulate testable scientific hypotheses, demonstrating creativity and initiative
as a result of a coherent understanding of economic theories;
- show advanced and integrated knowledge of a range of models to test hypotheses to
select an appropriate econometric model;
- conduct an econometric analysis (including specification tests) using real life big
data and critically evaluate the results of econometric models.
Subject Assessment
- Written > Examination (centrally administered) - (50%) - Individual
- Written > Test/Quiz 1 - (20%) - Individual
- Written > Research report - (30%) - Individual.
Inadmissible Subject Combinations:
|
BX3122 BX3022 |
Availabilities
|
Townsville,
Trimester 3,
Mixed attendance
|
Census Date 05-Oct-2023 |
Face to face teaching
(To be advised)
|
Coord/Lect: |
Dr Rabiul Beg. |
Workload expectations: |
The student workload for this
3
credit point subject is approximately
130 hours.
- 20 hours workshops
- 10 hours online activity - Recordings, online activities and self-directed learning
- 10 hours online Tutorials - Online Collaborate Sessions
- assessment and self-directed study
|
|
|
Trimester 3,
External
|
Census Date 05-Oct-2023 |
Coord/Lect: |
Dr Rabiul Beg. |
Workload expectations: |
The student workload for this
3
credit point subject is approximately
130 hours.
- 30 hours online activity - Recordings, online activities & self-directed learning
- 10 hours online Tutorials - Online collaborate sessions
- assessment and self-directed study
|
Method of Delivery: |
WWW - LearnJCU |
|
|
Note:
Minor variations might occur due to the continuous Subject quality improvement process,
and in case
of minor variation(s) in assessment details, the Subject Outline represents the latest
official information.