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Study information

Programming for Social Data Science

Module titleProgramming for Social Data Science
Module codeSSIM917
Academic year2023/4
Credits15
Module staff

Dr Lorien Jasny (Lecturer)

Duration: Term123
Duration: Weeks

11

Number students taking module (anticipated)

25

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 ActivitiesGuided independent studyPlacement / study abroad
22128

Details of learning activities and teaching methods

CategoryHours of study timeDescription
Scheduled Learning & Teaching activities22Weekly 2-hour lectures / seminars or 1 hour lecture + 1 hour seminar
Guided Independent Study38Assigned readings
Guided Independent Study60Preparation for and completion of practical assessments
Guided Independent Study30Practicing techniques used in computer tutorials

Formative assessment

Form of assessmentSize of the assessment (eg length / duration)ILOs assessedFeedback method
2 short practical exercisesBetween 2-4 tables, graphs, etc. with short descriptions1-6Written

Summative assessment (% of credit)

CourseworkWritten examsPractical exams
10000

Details of summative assessment

Form of assessment% of creditSize of the assessment (eg length / duration)ILOs assessedFeedback method
Assessment 1. Problem set using one of the discussed programming languages.25750 words with tables, figures, charts from analysis1-6Written
Assessment 2. Problem set using one of the discussed programming languages.25750 words with tables, figures, charts from analysis1-6Written
Assessment 3. Problem set using one of the discussed programming languages.501500 words with tables, figures, charts from analysis1-6Written
0
0
0

Details of re-assessment (where required by referral or deferral)

Original form of assessmentForm of re-assessmentILOs re-assessedTimescale for re-assessment
Assessment 1Assessment 1. Problem set using one of the discussed programming languages. 750 words with tables, figures, charts from analysis.1-6August/September re-assessment period
Assessment 2Assessment 2. Problem set using one of the discussed programming languages. 750 words with tables, figures, charts from analysis.1-6August/September re-assessment period
Assessment 3Assessment 3. Problem set using one of the discussed programming languages. 750 words with tables, figures, charts from analysis.1-6August/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

Key words search

Programming, quantitative methods, social sciences, computing

Credit value15
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