ECON314-18S2 (C) Semester Two 2018

Special Topic: Economic Analysis of "Big Data"

15 points

Details:
Start Date: Monday, 16 July 2018
End Date: Sunday, 18 November 2018
Withdrawal Dates
Last Day to withdraw from this course:
  • Without financial penalty (full fee refund): Friday, 27 July 2018
  • Without academic penalty (including no fee refund): Friday, 12 October 2018

Description

This course will build on skills learned in ECON213/214 by extending to the practice of finding, creating and analysing "Big Data" using the concepts of economics and the tools of econometrics. Students will also need to present their findings in a way that is appropriate to their audience which may be non-technical. Students will be required to bring a laptop or similar device to class. The software package Python will be used although no previous experience is assumed.

Course Content
With the decrease of the cost of storing data, both the size and the variety of available data has increased dramatically. In this course, we will discuss how such ‘big data’ is affecting both the economy itself and how the economy can be analysed. We will analyse the impact of ‘big data’ on the economy through various examples of how businesses and governments can benefit from big data, but also of how big data can be abused by them. We also discuss how ‘big data’ has created new ways to analyse the economy, by providing new ways to collect data (for example, web scraping) and by creating new types of data (for example, Google Trend/Correlate data, Text data and Image data) that can be used to analyse the economy.

This course will combine economics and econometrics, with elements from computer programming and machine learning.  We will scrape data from priceme.co.nz to estimate the determinants of prices of fridges, use the API’s of Twitter and the New York Times to analyse text, and ‘predict the present’ with Google Correlate and Google Trends. We also will use econometric case studies to compare traditional econometrics techniques to machine learning techniques, and discuss their advantages and disadvantages.

Learning Outcomes

  • At the end of this course students will be able to
  • think creatively about finding original data sources to help individuals, businesses and governments in their decision making;
  • analyse various kinds of data (numerical data, text, images) that can help businesses and governments to obtain competitive advantages and help researchers to create new knowledge about the economy;
  • use Python to collect and analyse datasets from online sources (like websites, twitter etc.) and apply selected data science methods (like cross-validation, bagging/boosting, etc.) which are becoming more and more popular among economists.
  • communicate findings in written form and in presentations in a language understandable to people without much analytical background.
  • participate with confidence in professional discussions about big data and artificial intelligence

      • University Graduate Attributes

      This course will provide students with an opportunity to develop the Graduate Attributes specified below:

      Critically competent in a core academic discipline of their award

      Students know and can critically evaluate and, where applicable, apply this knowledge to topics/issues within their majoring subject.

Prerequisites

(1) ECON105
(2) ECON213 or ECON214

Course Coordinator

For further information see Department of Economics and Finance Head of Department

Assessment

Assessment Due Date Percentage 
Assignments 40%
Final 30%
Midterm test 30%

Indicative Fees

Domestic fee $790.00

International fee $3,350.00

* All fees are inclusive of NZ GST or any equivalent overseas tax, and do not include any programme level discount or additional course-related expenses.

For further information see Department of Economics and Finance .

All ECON314 Occurrences

  • ECON314-18S2 (C) Semester Two 2018
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