Course Title: Big Data, Machine Learning and Society

Part A: Course Overview

Course Title: Big Data, Machine Learning and Society

Credit Points: 12.00


Course Coordinator: Dr Anna Zhu

Course Coordinator Phone: +61 3 9925

Course Coordinator Email: anna.zhu@rmit.edu.au

Course Coordinator Location: Melbourne City

Course Coordinator Availability: via email


Pre-requisite Courses and Assumed Knowledge and Capabilities

None


Course Description

Big Data and Machine Learning have never been more omnipresent for discovery and research in economics. They increasingly play a role in enabling policymakers to address important social and economic problems. Therefore, it is essential that data users and consumers can identify high quality data and understand the implications of poor data analysis.

In this course, you will explore how big data and machine learning are being used in economics and social science research. This course provides a practical, skills-based approach. You will be guided through the process and pitfalls of using and interpreting results based on big data from project inception to final data analysis. Practical skills will be developed through the completion of weekly data analysis tasks and coding exercises.

Topics covered include: poverty and disadvantage, education, gender disparities, Covid-19 impacts, and social-policy evaluations. In the context of these topics, the course will also provide an introduction to basic methods in data science, including machine learning for prediction, causal inference (econometric approaches and machine learning embellishments), and heterogeneous treatment effects. This course will discuss the benefits and drawbacks of each of Machine Learning methods in a non-technical manner and through real-world case-studies and applications.


Objectives/Learning Outcomes/Capability Development

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

CLO1: Apply modern statistical tools to big data sets to answer real-world business and economic questions.

CLO2: Critically analyse the validity of statistical models to answer policy questions and identify when causality can be inferred.

CLO3: Assess the strengths and weaknesses of Machine Learning methods the conditions under which the methods should be used.

CLO4: Perform data analysis with Python. Understand the implications of poor data analysis on results and policy/business decisions. Communicate results in an effective and non-technical manner.

CLO5: Identify high quality data and recommend solutions for common data issues.


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 possibly comprising individual and group work. These may include quizzes; assignments; prescribed 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

Recommended readings including newspaper articles, academic research papers. Interactive materials including online data explorers and videos.

The asynchronous materials and notes are posted on Canvas.

RMIT Library provides extensive resources, services and study spaces. All RMIT students have access to scholarly resources including course related material, books, e-books, journals and databases.

Computers and printers are available at every Library. You can access the Internet and Library e-resources. You can also access the RMIT University wireless network in the Library.

Contact: Ask the Library for assistance and information on Library resources and services http://www.rmit.edu.au/library. Study support is available for assistance with assignment preparation, academic writing, information literacy, referencing, maths and study skills. 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

Assessment Task 1: 20%
Linked CLOs: 1, 2, 3

Assessment Task 2: 35%
Linked CLOs: 4, 5

Assessment Task 3: 35%
Linked CLOs: 2, 3

Assessment Task 4: 10%
Linked CLOs: 1, 2, 3, 4, 5