Mathematics and Programming Skills for Policy Analytics

Module title	Mathematics and Programming Skills for Policy Analytics
Module code	POLM030
Academic year	2022/3
Credits	15
Module staff	Dr 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 R for data analysis.

Develop skills in using R 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 while 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
Introduction to Python
Data Analysis in R
Linear Algebra
Big data
Probability and frequentist inference
Multilevel modelling
Bayesian inference
Advanced data visualisation
Web scraping

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
Scheduled Learning and Teaching Activities	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.
Guided Independent Study	40	A variety of independent study tasks directed by module leader. These tasks may include (with an indicative number of hours): Assigned readings
Guided Independent Study	60	Preparation for and completion of practical assessments
Guided Independent Study	30	Practicing techniques used in computer tutorials

Formative assessment

Form of assessment	Size of the assessment (eg length / duration)	ILOs assessed	Feedback method
1 set of practical exercises	Between 2-4 tables, graphs, etc. with short descriptions	1-8	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 essay/report using practical skills in R to demonstrate one of the concepts/skills learned in the module.	25	750 words with tables, figures, charts from analysis	1-8	Written Feedback
Assessment 2. 750-word essay/report using practical skills in Python to demonstrate one of the concepts/skills learned in the module.	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
	0
	0

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 (750 words with tables, figures, charts from analysis)	1-8	August/September reassessment period
Assessment 2	Assessment 2 (750 words with tables, figures, charts from analysis)	1-8	August/September reassessment period
Assessment 3	Assessment 3 (750 words with tables, figures, charts from analysis)	1-8	August/September reassessment period
Assessment 4	Assessment 4 (750 words with tables, figures, charts from analysis)	1-8	August/September reassessment period

Indicative learning resources - Basic reading

Basic reading:

Cioffi-Revilla, C. (2013). Introduction to computational social science: principles and applications, London: Springer Science & Business Media.

Gill, J. (2006). Essential Mathematics for Political and Social Research, Cambridge: Cambridge University Press.

Gelman, A & Hill, J. (2006). Data Analysis Using Regression and Multilevel/Hierarchical Models, Cambridge: Cambridge University Press.

Kropko, J. (2015) Mathematics for Social Scientists. London: SAGE Publications.

McElreath, R. (2020). Statistical rethinking: A Bayesian course with examples in R and Stan. New York: CRC Press.

Indicative learning resources - Web based and electronic resources

Big Data and Social Science (Ian Foster, Rayid Ghani, Ron S. Jarmin, Frauke Kreuter, and Julia Lane): https://textbook.coleridgeinitiative.org

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

Key words search

Quantitative methods, maths, social sciences, computing

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
Last revision date	28/04/2022