Programming for Social Data Science
Module title | Programming for Social Data Science |
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Module code | SSIM917 |
Academic year | 2023/4 |
Credits | 15 |
Module staff | Dr Lorien Jasny (Lecturer) |
Duration: Term | 1 | 2 | 3 |
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Duration: Weeks | 11 |
Number students taking module (anticipated) | 25 |
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Module description
You will cover the programming skills needed to progress through the MSc Social Data Science. You will be a social science student with an introductory understanding of quantitative methods. This course will aim to provide you with the essential 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. The course will start from a level of no prior knowledege of computer programming, but will accelerate quickly.
Module aims - intentions of the module
The main aims of the module are:
- 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 data allowing understanding of the scope of possibilities that are open to a researcher without special “big data” resources.
- Develop skills in managing structured and unstructured data and constructing new databases from different sources
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 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)
ILO: Discipline-specific skills
On successfully completing the module you will be able to...
- 3. developed computer programming skills in a way that results in high level of synergies with quantitative research skills
- 4. manipulate data in each program and use the appropriate in-built analytic tools
ILO: Personal and key skills
On successfully completing the module you will be able to...
- 5. demonstrate understanding of and use a full range of computing skills effectively and independently
- 6. demonstrate understanding of and use a full range of data management skills effectively and independently
Syllabus plan
Whilst the module’s precise content may vary from year to year, it is envisaged that the syllabus will cover some or all of the following topics:
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
- Loops
- Advanced R programming
- Big data
- Simulation
Learning activities and teaching methods (given in hours of study time)
Scheduled Learning and Teaching Activities | Guided independent study | Placement / study abroad |
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22 | 128 |
Details of learning activities and teaching methods
Category | Hours of study time | Description |
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Scheduled Learning & Teaching activities | 22 | Weekly 2-hour lectures / seminars or 1 hour lecture + 1 hour seminar |
Guided Independent Study | 38 | 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 |
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2 short practical exercises | Between 2-4 tables, graphs, etc. with short descriptions | 1-6 | Written |
Summative assessment (% of credit)
Coursework | Written exams | Practical exams |
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100 | 0 | 0 |
Details of summative assessment
Form of assessment | % of credit | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
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Assessment 1. Problem set using one of the discussed programming languages. | 25 | 750 words with tables, figures, charts from analysis | 1-6 | Written |
Assessment 2. Problem set using one of the discussed programming languages. | 25 | 750 words with tables, figures, charts from analysis | 1-6 | Written |
Assessment 3. Problem set using one of the discussed programming languages. | 50 | 1500 words with tables, figures, charts from analysis | 1-6 | Written |
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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 |
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Assessment 1 | Assessment 1. Problem set using one of the discussed programming languages. 750 words with tables, figures, charts from analysis. | 1-6 | August/September re-assessment period |
Assessment 2 | Assessment 2. Problem set using one of the discussed programming languages. 750 words with tables, figures, charts from analysis. | 1-6 | August/September re-assessment period |
Assessment 3 | Assessment 3. Problem set using one of the discussed programming languages. 750 words with tables, figures, charts from analysis. | 1-6 | August/September re-assessment period |
Indicative learning resources - Basic reading
- Cioffi-Revilla, C. (2013). Introduction to computational social science: principles and applications, London: Springer Science & Business Media.
- Braun, W. Johh. (2016). A First Course in Statistical Programming with R. Cambridge University Press.
- Unpingco, José. (2021). Python programming for data analysis. Springer.
Indicative learning resources - Web based and electronic resources
- ELE – College to provide hyperlink to appropriate pages
- 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
Credit value | 15 |
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Module ECTS | 7.5 |
Module pre-requisites | none |
Module co-requisites | none |
NQF level (module) | 7 |
Available as distance learning? | No |
Origin date | 24/02/2022 |