Course Title: Data Modelling Techniques for Business

Part A: Course Overview

Course Title: Data Modelling Techniques for Business

Credit Points: 12.00

Important Information:

Course Title Amendment

2024 Data Modelling Techniques for Business

2023 Econometric Techniques


Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

ECON1223

City Campus

Undergraduate

625H Economics, Finance and Marketing

Face-to-Face

 Sem 1 2006,
Sem 1 2007,
Sem 1 2008,
Sem 1 2009,
Sem 1 2010,
Sem 1 2011,
Sem 1 2012,
Sem 1 2013,
Sem 1 2014,
Sem 1 2015,
Sem 1 2016,
Sem 1 2017,
Sem 1 2018,
Sem 1 2019,
Sem 1 2020,
Sem 2 2024

ECON1238

City Campus

Postgraduate

625H Economics, Finance and Marketing

Face-to-Face

 Sem 1 2008,
Sem 1 2010,
Sem 1 2011,
Sem 1 2012,
Sem 1 2013,
Sem 1 2014,
Sem 1 2015,
Sem 1 2016,
Sem 1 2018,
Sem 1 2019,
Sem 1 2020

Course Coordinator: Dr Pratima Srivastava

Course Coordinator Phone: +61 3 9925 5905

Course Coordinator Email: pratima.srivastava@rmit.edu.au

Course Coordinator Location: Melbourne Campus Building 80

Course Coordinator Availability: By appointment via email 


Pre-requisite Courses and Assumed Knowledge and Capabilities

None.


Course Description

Data science skills are highly in demand and open the door to career opportunities in a range of industries and occupations. In this course students will learn a range of sophisticated quantitative skills used to analyse microlevel social, financial, business and economic data. The special features of some of these datasets require the use of statistical/econometric techniques that make the application of the standard linear regression model inappropriate. This course will teach students a variety of techniques that can be used to deal with such situations. The skills are applicable in a range of areas including banking and finance, marketing research, health, education, labour market, international trade and policy evaluation, allowing practitioners and decision makers to study the behaviour of individuals, firms and other micro-units, and uncover patterns. They are highly valued in the research environment for those who would like to purse a research career. Students will also gain hands-on experience and computation skills for data mining and analysing large scale micro datasets.


Objectives/Learning Outcomes/Capability Development

-


On successful completion of this course you will be able to:

CLO1: Critically analyse and synthesise data to formulate effective economic and business decisions.

CLO2:Use appropriate analytical techniques to provide specialist advice to people from business disciplines, in diverse business contexts.

CLO3: Effectively communicate business data and econometric concepts to professionals and non-professionals in diverse business contexts.

CLO4: Apply reasoned judgements to solve a variety of statistical and econometric problems in business disciplines such as Accounting, Economics, Finance and Marketing, with reference to business, government policy and global perspectives.

CLO5: Analyse data in an ethical manner, avoiding selective use of data, concealing of results and fabrication of outcomes.


Overview of Learning Activities

To achieve the desired learning outcomes the course encourages you to participate in the following learning experiences:

  • Attendance and participation in class activities.
  • Using the various resources provided in canvas for content knowledge.

In this course you will be encouraged to be an active learner. Your learning will be supported through various in-class and online activities comprising individual and group work. These may include quizzes; assignments; readings; sourcing, researching and analysing specific information; solving problems; conducting presentations; producing written work and collaborating with peers on set tasks or projects.


Overview of Learning Resources

Various learning resources are available online through MyRMIT Studies\Canvas. The lecture notes and workshop notes are posted on Canvas.

Resources are also available online through RMIT Library databases and other facilities. Visit the RMIT library website for further details. Assistance is available online via our chat and email services, face to face at our campus libraries or via the telephone on (03) 9925 2020.

Additional resources and/or sources to assist your learning will be identified by your course coordinator and will be made available to you as required during the teaching period. 


Overview of Assessment

The assessment alignment list below shows the assessment tasks against the learning outcomes they develop.

Assessment Task 1: 20%
Linked CLOs:1, 4

Assessment Task 2: 30%
Linked CLOs: 1, 2, 3, 4, 5

Assessment Task 3: 50%
Linked CLOs: 1, 2, 3, 4, 5

Feedback will be provided throughout the semester in class and/or in online forums through individual and group feedback on practical exercises and by individual consultation.