ECONOMIC DATA ANALYSIS - 2022/3
Module code: ECO1017
The recent digital revolution has led to data gathering and usage in Economics at an unprecedented scale. Public policy and business become more data-driven than ever before. This module explores the data on which economics relies and forms fundamental analytical skills. The lectures are focused on understanding the data, while the laboratory sessions are focused on examination and visualization of the data using R and MS Excel.
WANG Zhe (Economics)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 4
JACs code: G300
Module cap (Maximum number of students): N/A
Overall student workload
Independent Learning Hours: 96
Lecture Hours: 11
Laboratory Hours: 10
Guided Learning: 22
Captured Content: 11
Prerequisites / Co-requisites
Indicatively, content includes:
Data and data structures.
Numerical examination of data.
Visual analysis of data.
Interaction between economic factors.
Measuring economic activity.
|Assessment type||Unit of assessment||Weighting|
|Online Scheduled Summative Class Test||MIDTERM TEST||25|
|Coursework||COURSEWORK - GROUP REPORT||75|
An individual project can serve as an alternative assessment for the group project for resitting students or those with extenuating circumstances.
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. The module includes formative and summative assessments.
The students benefit from formative feedback about their work on problem sets in the weekly seminars and laboratories where they trial learning the how to use R and MS Excel. In these seminars and laboratories students can ask questions about their work in the course and receive feedback from the instructor.
The first summative assessment is a quiz designed to test the understanding of fundamental concepts in data analysis. Administered in the middle of term, if informs students about misconceptions and knowledge gaps they have Feedback solutions to the quiz are released within the University’s normal three-week marking period.
The second summative assessment is a group report that is submitted after the term ends. 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 to collect, analyse and interpret data. Feedback on the group report is made available separately through special forms which are posted to each student’s individual space on SurreyLearn, within the University’s designated deadlines. Feedback forms include comments on student performance and advice on how to improve, where appropriate.
- Learn how to analyse data.
- Learn the application of basic statistical techniques using R and MS Excel.
- Make judgements based on empirical findings.
- Develop research and teamwork skills.
|003||Understand how to find data, analyse, and interpret it individually and in a group.||CPT|
|004||Understand how economic activity is measured.||KC|
|001||Understanding of key characteristics of data.||KCP|
|002||Familiarize with the use of R or MS Excel for data analysis.||CPT|
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Methods of Teaching / Learning
Students have access to a pre-recorded theory captured content lectures (11 hours in total) at the start of each week. The videos present important concepts ahead of the weekly live sessions. Students are expected to familiarise themselves with the videos before attending lectures (11 hours in total) and laboratory sessions (10 hours in total). Pre-recorded captured content lectures emphasize the understanding of theory, whereas in person lectures link theory to empirical examples and students can find answers to their questions. During laboratory sessions students learn and practice R and MS Excel.
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.
Upon accessing the reading list, please search for the module using the module code: ECO1017
The School of Economics is committed to developing graduates with strengths in Employability, Digital Capabilities, Global and Cultural Capabilities, Sustainability, and Resourcefulness and Resilience. This module is designed to allow students to develop knowledge, skills, and capabilities particularly in the following areas:
The module is designed to give fundamental knowledge and skills in data analysis which is in high demand in business, government, and academia.
Resourcefulness and resilience:
Student will learn and practice analytical software packages R and MS Excel which are widely used for data analysis in business and public bodies.
Global and Cultural Intelligence:
Students will be able to collect and analyse data from various countries to observe differences and similarities between countries and regions.
Doing the group coursework student will learn how to plan, coordinate, and complete projects. They will learn how to work in teams and evaluate teammate’s contributions. Learned transferable skills can be applied to future employment or graduate study.
Student will learn how to use open repositories for installing R and its packages. They will obtain general understanding of how programming languages work.
Programmes this module appears in
|Economics and Finance BSc (Hons)||1||Compulsory||A weighted aggregate mark of 40% is required to pass the module|
|Economics BSc (Hons)||1||Compulsory||A weighted aggregate mark of 40% is required to pass the module|
|Business Economics BSc (Hons)||1||Compulsory||A weighted aggregate mark of 40% is required to pass the module|
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 2022/3 academic year.