• University of Exeter Business School
• Module

Introduction to Data Science in Economics

Module description

We are living in a data age. Businesses and governments are leveraging data to make better decisions. However, converting data into an actionable item is a challenge, particularly when there are multiple complex data sources and innumerable statistical methods and machine learning algorithms to choose from. To get insights from data in the current age requires a combination of skills such as computer programming, statistical understanding and knowledge of the predictive algorithms. In this module, students will learn to apply some of the popularly used data science techniques.

The assessment structure on this module is subject to review and may change before the start of the new academic year. Any changes will be clearly communicated to you before at the start of the new term and if you wish to change module as a result of this you can do so in the module change window.

Module aims - intentions of the module

This module will enable students to understand, apply and interpret findings from the commonly used data science techniques.

A student undertaking this module should have a good grasp of statistics.

Intended Learning Outcomes (ILOs)

ILO: Module-specific skills

On successfully completing the module you will be able to...

• 1. recognise the differences and similarities among various data science techniques using a variety of software.
• 2. critically evaluate alternative approaches for collecting, managing and analysing data and how this data is used to support decision-making.

ILO: Discipline-specific skills

On successfully completing the module you will be able to...

• 3. recognise the most commonly used data analysis and research methods used in data science.
• 4. demonstrate an understanding of the role of numerical evidence in business and economics.

ILO: Personal and key skills

On successfully completing the module you will be able to...

• 5. demonstrate logical problem solving skills.
• 6. exemplify analytical thinking and independent study skills.

Syllabus plan

1. Introduction to data science
2. Data preparation
3. Data cleaning and integration
4. Data manipulation
5. Working with big data
6. Basic programming skills

Note: The syllabus plan is tentative.

Learning activities and teaching methods (given in hours of study time)

Details of learning activities and teaching methods

Formative assessment

Summative assessment (% of credit)

Details of summative assessment

Details of re-assessment (where required by referral or deferral)

Indicative learning resources - Basic reading

Basic reading:

Grus, J. (2015) Data Science from Scratch, O’Reilly

Williams, G. (2017) One Page R – A Survival Guide to Data Science, Taylor & Francis Group (available online at http://togaware.com/onepager/)

Williams, G. (2017) The Essentials of Data Science – Knowledge discovery Using R and Python, Taylor & Francis Group (available online at https://essentials.togaware.com/)

Key words search

Data science, Data manipulation, Data analysis.