Mathematics and Programming Skills for Policy Analytics
| Module title | Mathematics and Programming Skills for Policy Analytics |
|---|---|
| Module code | POLM030 |
| Academic year | 2019/0 |
| Credits | 15 |
| Module staff | Lorien Jasny (Lecturer) |
| Duration: Term | 1 | 2 | 3 |
|---|---|---|---|
| Duration: Weeks | 11 |
| Number students taking module (anticipated) | 6 |
|---|
Module description
You will cover the maths and programming skills needed to progress through the MSc Policy Analytics. You will be a social science student with an introductory understanding of quantitative methods. This mathematics and programming course will aim to provide you with the essential mathematical skills to undertake data analysis, the programming skills to acquire and analyse data. You will need a sufficient level of quantitative methods training and a willingness to engage with new material.
Module aims - intentions of the module
The module will cover basic maths and programming skills that you will require to progress on the MSc in Policy Analytics. The main aims of the module are:
- Develop basic maths skills for data analysis
- Develop proficiency in the use of relevant computer packages/languages (R, Python);
- Introduce you to Application Programming Interfaces (APIs) of various web sources (such as Twitter) to obtain large amounts of data allowing understanding of the scope of possibilities that are open to a researcher without special “big data” resources.
- Develop skills in managing large scale structured and unstructured data and constructing new databases from different sources;
- Develop skills in using the R package for data analysis.
Intended Learning Outcomes (ILOs)
ILO: Module-specific skills
On successfully completing the module you will be able to...
- 1. Demonstrate proficiency in the use of specific programming languages/packages used for statistical analysis: e.g. R and Python.
- 2. Understand code in R and implement appropriate commands to perform relevant statistical analyses (topics covered will include types of variables, functions and parameters, conditional commands and constructs such as when and for cycles).
- 3. Use Application Programming Interfaces to obtain data for potential use in future research projects (Python).
ILO: Discipline-specific skills
On successfully completing the module you will be able to...
- 4. Developed computer programming skills in a way that results in high level of synergies with quantitative research skills.
- 5. Manipulate data in each program and use the appropriate in-built analytic tools.
- 6. Interpret output from each program and draw appropriate inference regarding the hypotheses being tested.
ILO: Personal and key skills
On successfully completing the module you will be able to...
- 7. Demonstrate understanding of and use a full range of computing skills effectively and independently
- 8. Demonstrate understanding of and use a full range of data management skills effectively and independently
Syllabus plan
Whilst the module’s precise content and order of syllabus coverage may vary, it is envisaged that it will include the following topics:
Introduction to R
Data management in R
Data Analysis in R
Linear Algebra
Sets and Functions
Probability
Optimisation
Web scraping and data retrieval with Python
Learning activities and teaching methods (given in hours of study time)
| Scheduled Learning and Teaching Activities | Guided independent study | Placement / study abroad |
|---|---|---|
| 20 | 130 | 0 |
Details of learning activities and teaching methods
| Category | Hours of study time | Description |
|---|---|---|
| Lectures with lab | 20 | 10 x 2 hours of lectures and labs. These lectures cover the main concepts of the course. Sessions will sometimes include group and lab work. |
| Independent study | 130 | A variety of independent study tasks directed by module leader. These tasks may include (with an indicative number of hours): Assigned readings (40 hours) Preparation for and completion of practical assessments (60 hours) Practicing techniques used in computer tutorials (30 hours) |
Formative assessment
| Form of assessment | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|
| 2 short practical exercises | Between 2-4 tables, graphs, etc. with short descriptions | 1-8 | Oral feedback to group. Some written feedback. |
Summative assessment (% of credit)
| Coursework | Written exams | Practical exams |
|---|---|---|
| 100 | 0 | 0 |
Details of summative assessment
| Form of assessment | % of credit | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|---|
| Assessment 1. 750 word practical exercise using the skills/techniques developed in one of the programming languages/applications to investigate a research problem relevant to the students chosen discipline | 25 | 750 words with tables, figures, charts from analysis | 1-8 | Written Feedback |
| Assessment 2. 750 word practical exercise using the skills/techniques developed in one of the programming languages/applications to investigate a research problem relevant to the students chosen discipline | 25 | 750 words with tables, figures, charts from analysis | 1-8 | Written Feedback |
| Assessment 3. 750 word practical exercise using the skills/techniques developed in one of the programming languages/applications to investigate a research problem relevant to the students chosen discipline. | 25 | 750 words with tables, figures, charts from analysis | 1-8 | Written Feedback |
| Assessment 4. 750 word practical exercise using the skills/techniques developed in one of the programming languages/applications to investigate a research problem relevant to the students chosen discipline. | 25 | 750 words with tables, figures, charts from analysis | 1-8 | Written Feedback |
Details of re-assessment (where required by referral or deferral)
| Original form of assessment | Form of re-assessment | ILOs re-assessed | Timescale for re-assessment |
|---|---|---|---|
| Assessment 1 | Assessment 1 | 1-8 | August/September reassessment period |
| Assessment 2 | Assessment 2 | 1-8 | August/September reassessment period |
| Assessment 3 | Assessment 3 | 1-8 | August/September reassessment period |
| Assessment 4 | Assessment 4 | 1-8 | August/September reassessment period |
Indicative learning resources - Basic reading
Basic reading:
Cioffi-Revilla, Claudio. Introduction to computational social science: principles and applications. Springer Science & Business Media, 2013.
Gill, Jeff. 2006. Essential Mathematics for Political and Social Research. Cambridge, England: Cambridge University Press.
Bush, ROBERT R., et al. "Mathematics for social scientists." The American Mathematical Monthly 61.8 (1954): 550-561.
Kropko, Jonathan. Mathematics for Social Scientists. SAGE Publications, 2015.
Indicative learning resources - Web based and electronic resources
Maths Refresher Course (Gary King) http://projects.iq.harvard.edu/prefresher
UK Data Services - https://www.ukdataservice.ac.uk
NCRM - http://www.ncrm.ac.uk
| Credit value | 15 |
|---|---|
| Module ECTS | 7.5 |
| Module pre-requisites | None |
| Module co-requisites | None |
| NQF level (module) | 7 |
| Available as distance learning? | No |
| Origin date | 28/11/2016 |
