EC125-6 Computing and Data Analysis
Introductory description
This module is a research-led module, which exposes students to appropriate statistical packages to which will enable students to apply techniques learnt in first year statistics modules to real world data. You will gain an understanding of how programme within the statistical package, thereby enabling the student to present statistical data in a simple and meaningful way (tables, graphs), how to develop hypothesis tests from the data. Students will gain skills in presenting statistical data and report writing.
Module aims
To develop undergraduate students' research skills: students will have to work independently and find things out for themselves; To develop undergraduate students' computing skills: students will be taught how to generate pictures/diagrams and equations in word, to present data in tables and graphs in excel and be given an introduction to an advance statistical software package; To learn about data handling and data description; To learn relevant economic statistics and hypothesis testing: this module will include numerical work on microeconomic datasets, which is part of the basic training of every economist. The module forms part of the first year core cluster EC120 Quantitative Techniques, which is made up of one module in Mathematical Techniques (A (EC121) or B (EC123)), one module in Statistical Techniques (A (EC122) or B (EC124)) as well as Computing and Data Analysis (EC125).
Outline syllabus
This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.
The module will typically cover the following topics:Computing skills; Economic statistics; Descriptive statistics; Data awareness; Data analysis; Report-writing and report-presentation
Learning outcomes
By the end of the module, students should be able to:
- Use and undertake basic programming in the selected statistical software package.The teaching and learning methods that enable students to achieve this learning outcome are: Both lectures and computer lab sessions will be used to introduce the software.The summative assessment methods that measure the achievement of this learning outcome are: Group project.
- Undertake basic cleaning of micro datasets and preliminary data description of those datasets. The teaching and learning methods that enable students to achieve this learning outcome are: Lectures will be used to provide examples of data description, students will then practice in computer lab sessions and have exercise sheets to practice themselves.The summative assessment methods that measure the achievement of this learning outcome are: Group project.
- Undertake basic statistical analysis and hypothesis testing of datasets.The teaching and learning methods that enable students to achieve this learning outcome are: Lectures will be used to provide examples of data description, students will then practice in computer lab sessions and have exercise sheets to practice themselves.The summative assessment methods that measure the achievement of this learning outcome are: Group project.
- Write reports of their data description and data analysis, distilling key insights and conclusions.The teaching and learning methods that enable students to achieve this learning outcome are: Exercise sheets will give students practice at report writing. The summative assessment methods that measure the achievement of this learning outcome are: Group project and individual exercise sheets
Indicative reading list
The Stata Survival Manual - Pevalin, David J. 2009
View reading list on Talis Aspire
Subject specific skills
Students will have the opportunity to develop skills in:
Analytical thinking and communication
Critical thinking
Creative thinking
Problem-solving
Abstraction
Policy evaluation
Concepts of Simultaneity and Endogeneity
Transferable skills
Students will have the opportunity to develop:
Research skills
Numeracy and quantitative skills
Data-based skills
IT skills
Written communication skills
Oral communication skills
Team work skills
Mathematical, statistical and data-based research skills
Study time
| Type | Required |
|---|---|
| Lectures | 2 sessions of 1 hour (3%) |
| Online learning (independent) | 10 sessions of 1 hour (17%) |
| Other activity | 1 hour (2%) |
| Private study | 47 hours (78%) |
| Total | 60 hours |
Private study description
Private study will be required in order to prepare for seminars/classes, to review lecture notes, to prepare for forthcoming assessments, tests, and exams, and to undertake wider reading around the subject
Other activity description
Presentation
Costs
No further costs have been identified for this module.
You do not need to pass all assessment components to pass the module.
Students can register for this module without taking any assessment.
Assessment group A1
| Weighting | Study time | Eligible for self-certification | |
|---|---|---|---|
| Coursework | 10% | No | |
|
Reproducing some equations (typical of those seen in economics) and some diagram (typical of |
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| Group Project | 90% | No | |
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This work requires students to formulate a research questions and develop some hypotheses and |
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Feedback on assessment
The Department of Economics is committed to providing high quality and timely feedback to students on their assessed work, to enable them to review and continuously improve their work. We are dedicated to ensuring feedback is returned to students within 20 University working days of their assessment deadline. Feedback for assignments is returned either on a standardised assessment feedback cover sheet which gives information both by tick boxes and by free comments or via free text comments on tabula, together with the annotated assignment. For tests and problem sets, students receive solutions as an important form of feedback and their marked assignment, with a breakdown of marks and comments by question and sub-question. Students are informed how to access their feedback, either by collecting from the Undergraduate Office or via tabula. Module leaders often provide generic feedback for the cohort outlining what was done well, less well, and what was expected on the assignment and any other common themes. This feedback also includes a cumulative distribution function with summary statistics so students can review their performance in relation to the cohort. This feedback is in addition to the individual-specific feedback on assessment performance.
Post-requisite modules
If you pass this module, you can take:
- EC208-15 Industrial Economics 1: Market Structure
- EC208-15 Industrial Economics 1: Market Structure
- EC203-30 Applied Econometrics
- EC203-30 Applied Econometrics
- EC220-15 Mathematical Economics 1A
- EC220-15 Mathematical Economics 1A
- EC220-12 Mathematical Economics 1A
- EC220-12 Mathematical Economics 1A
Courses
This module is Core for:
-
UECA-3 Undergraduate Economics 3 Year Variants
- Year 1 of L100 Economics
- Year 1 of L116 Economics and Industrial Organization
- Year 1 of UECA-LM1D Undergraduate Economics, Politics and International Studies
- Year 1 of UPHA-L1CA Undergraduate Economics, Psychology and Philosophy
- Year 1 of ULNA-R1L4 Undergraduate French and Economics (4-year)
- Year 1 of ULNA-R2L4 Undergraduate German and Economics (4-year)
- Year 1 of ULNA-R4L1 Undergraduate Hispanic Studies and Economics (4-year)
- Year 1 of ULNA-R3L4 Undergraduate Italian and Economics (4-year)
- Year 1 of ULNA-R9L1 Undergraduate Modern Languages and Economics (4-year)
- Year 1 of UPHA-V7ML Undergraduate Philosophy, Politics and Economics
This module is Core optional for:
- Year 1 of UIPA-L1L8 Undergraduate Economic Studies and Global Sustainable Development