ECONOMIC DATA ANALYSIS - 2021/2

Module Overview

Economics is a data-driven subject and figures on growth, unemployment, spending and inflation provide the raw material for many of the theories that dominate the subject. This module explores the data on which economics relies. Several lectures will be focused on understanding the data, but, also, discuss the measurement of key macroeconomic data series. The laboratory sessions will be focused on using the data, developing students understanding on examining and presenting data using Excel.

Module provider

Economics

Module Leader

SAGIEV Erkin (Economics)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 4

Module cap (Maximum number of students): N/A

Module Availability

Semester 1

Prerequisites / Co-requisites

None

Module content

Indicatively, content includes:

• Graphical and numerical examination of distributions;


• Correlation, least-squares regression;


• Understanding causation;


• Measuring economic activity.

Assessment pattern

Alternative Assessment

An individual project can serve as an alternative assessment for the group project for resitting students or those with extenuating circumstances.

Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to show both their research and technical skills as well as their understanding of the taught theory.

Specifically, the summative assessment for this module consists of:

• 
  A group research proposal that is submitted in week 8.
  


• A group report that is submitted after the Christmas break in January. Each group should use secondary data sources and taught techniques to analyse an economic issue. The focus of this assessment is on students' ability to work effectively as part of a team in order to collect, analyse and interpret data. The analysis of data is expected to be carried out using Microsoft Excel.

Formative assessment and feedback

Students are given the opportunity to apply the taught theory and practise the use of Excel on a weekly basis. Specifically, a set of exercises is provided every week until term week 10. Students have the opportunity to attempt the exercises under the guidance of a tutor during the scheduled weekly computer lab sessions. Solutions to the exercises are provided too. Students can receive feedback from the tutor and/or use the solutions to check their progress on their own.

Module aims

• Understand the application of basic statistical techniques using Microsoft Excel.
• Learn how to analyse data and make judgements based on empirical findings.
• Develop research and teamwork skills.

Learning outcomes

Methods of Teaching / Learning

The learning and teaching strategy is designed to help students:

• understand basic statistical techniques and use them to carry out empirical analysis;


• familiarise with Microsoft Excel and appreciate it as a tool to conduct data analysis;


• develop critical thinking.

The learning and teaching methods include:

• 2-hour lecture per week x 11 weeks;


• 1-hour drop-in computer lab session.

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list

https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: ECO1017

Programmes this module appears in

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2021/2 academic year.