Linguistics with Data Science (Including Placement Year)

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Academic Year of Entry: 2023/24 - 2024/25
Course overview
(BA) Bachelor of Arts
Linguistics with Data Science (Including Placement Year)
Current
University of Essex
University of Essex
Language and Linguistics
Colchester Campus
Honours Degree
Full-time
BA Q122
08/05/2024

Details

Professional accreditation

None

Admission criteria

GCSE: Mathematics C/4

A-levels: ABB

BTEC: DDD, depending on subject studied - advice on acceptability can be provided.

IB: 32 points or three Higher Level certificates with 655. Either must include Standard Level Mathematics grade 4, or a minimum of 3 in Higher Level Mathematics. We will accept grade 4 in either Standard Level Mathematics: Analysis and Approaches or Standard Level Mathematics: Applications and Interpretation.
Maths in the IB is not required if you have already achieved GCSE Maths at grade C/4 or above or 4 in IB Middle Years Maths.
We are also happy to consider a combination of separate IB Diploma Programme Courses (formerly certificates) at both Higher and Standard Level. Exact offer levels will vary depending on the range of subjects being taken at higher and standard level, and the course applied for.
We can also consider combinations with BTECs or other qualifications in the Career-related programme – the acceptability of BTECs and other qualifications depends on the subject studied, advice on acceptability can be provided. Please contact the Undergraduate Admissions Office for more information.

Access to HE Diploma:15 Level 3 credits at Distinction and 30 level 3 credits at Merit, depending on subject studied - advice on acceptability can be provided.

T-levels: Distinction, depending on subject studied - advice on acceptability can be provided.

What if I don’t achieve the grades I hoped?

If your final grades are not as high as you had hoped, the good news is you may still be able to secure a place with us on a course which includes a foundation year. Visit our undergraduate application information page for more details.

What if I have a non-traditional academic background?
Don’t worry. To gain a deeper knowledge of your course suitability, we will look at your educational and employment history, together with your personal statement and reference.

You may be considered for entry into Year 1 of your chosen course. Alternatively, some UK and EU applicants may be considered for Essex Pathways, an additional year of study (known as a foundation year/year 0) helping students gain the necessary skills and knowledge in order to succeed on their chosen course. You can find a list of Essex Pathways courses and entry requirements here

If you are a mature student, further information is here

IELTS (International English Language Testing System) code

English language requirements for applicants whose first language is not English: IELTS 6.0 overall. Different requirements apply for second year entry, and specified component grades are also required for applicants who require a visa to study in the UK.

Other English language qualifications may be acceptable so please contact us for further details. If we accept the English component of an international qualification then it will be included in the information given about the academic levels listed above. Please note that date restrictions may apply to some English language qualifications

If you are an international student requiring a visa to study in the UK please see our immigration webpages for the latest Home Office guidance on English language qualifications.

If you do not meet our IELTS requirements then you may be able to complete a pre-sessional English pathway that enables you to start your course without retaking IELTS.

Additional Notes

If you’re an international student, but do not meet the English language or academic requirements for direct admission to this degree, you could prepare and gain entry through a pathway course. Find out more about opportunities available to you at the University of Essex International College here.

Course qualifiers

A course qualifier is a bracketed addition to your course title to denote a specialisation or pathway that you have achieved via the completion of specific modules during your course. The specific module requirements for each qualifier title are noted below. Eligibility for any selected qualifier will be determined by the department and confirmed by the final year Board of Examiners. If the required modules are not successfully completed, your course title will remain as described above without any bracketed addition. Selection of a course qualifier is optional and student can register preferences or opt-out via Online Module Enrolment (eNROL).

None

Rules of assessment

Rules of assessment are the rules, principles and frameworks which the University uses to calculate your course progression and final results.

Additional notes

None

External examiners

Staff photo
Dr Sam Christian D'Elia

External Examiners provide an independent overview of our courses, offering their expertise and help towards our continual improvement of course content, teaching, learning, and assessment. External Examiners are normally academics from other higher education institutions, but may be from the industry, business or the profession as appropriate for the course. They comment on how well courses align with national standards, and on how well the teaching, learning and assessment methods allow students to develop and demonstrate the relevant knowledge and skills needed to achieve their awards. External Examiners who are responsible for awards are key members of Boards of Examiners. These boards make decisions about student progression within their course and about whether students can receive their final award.

eNROL, the module enrolment system, is now open until Monday 27 January 2025 8:59AM, for students wishing to make changes to their module options.

Key

Core You must take this module.
You must pass this module. No failure can be permitted.
Core with Options You can choose which module to study.
You must pass this module. No failure can be permitted.
Compulsory You must take this module.
There may be limited opportunities to continue on the course/be eligible for the degree if you fail.
Compulsory with Options You can choose which module to study.
There may be limited opportunities to continue on the course/be eligible for the degree if you fail.
Optional You can choose which module to study.
There may be limited opportunities to continue on the course/be eligible for the degree if you fail.

Year 1 - 2023/24

Component Number Module Code Module Title Status Min Credits Max Credits
01  LG110-4-AU-CO  Sounds  Compulsory  15  15 
02  LG111-4-AU-CO  Words and Sentences  Compulsory  15  15 
03  LG104-4-AU-CO  Introduction to the Study of Language  Compulsory  15  15 
04  LG114-4-SP-CO  Foundations of Sociolinguistics  Compulsory  15  15 
05    (LG115-4-AU and LG119-4-SP) or (LG665-4-AU and LG667-4-SP)  Compulsory with Options  30  30 
06  MA331-4-SP-CO  Programming and Text Analytics with R  Compulsory  15  15 
07  MA334-4-SP-CO  Data analysis and statistics with R  Compulsory  15  15 
08  LA099-4-FY-CO  Careers and Employability Skills for Languages and Linguistics  Compulsory 

Year 2 - 2024/25

Component Number Module Code Module Title Status Min Credits Max Credits
01  LG219-5-SP-CO  Multilingualism  Compulsory  15  15 
02    Linguistics option(s)  Optional  45  45 
03    LG666-5-FY or Linguistics option(s) or Language options(s) from list  Optional  30  30 
04  MA332-5-SP-CO  Databases and data processing with SQL  Compulsory  15  15 
05  MA335-5-SP-CO  Modelling experimental and observational data  Compulsory  15  15 
06  LA099-5-FY-CO  Careers and Employability Skills for Languages and Linguistics  Compulsory 

Year Abroad/Placement - 2025/26

Component Number Module Code Module Title Status Min Credits Max Credits
01  LG088-6-FY-CO  Linguistics Work Placement Year  Core  120  120 

Year 3 - 2026/27

Component Number Module Code Module Title Status Min Credits Max Credits
01    LG831-6-FY or HG832-6-FY  Compulsory with Options  30  30 
02    Linguistics option(s) from list  Optional  60  60 
03  MA304-6-SP-CO  Data Visualisation  Compulsory  15  15 
04  MA336-6-SP-CO  Artificial intelligence and machine learning with applications  Compulsory  15  15 
05  LA099-6-FY-CO  Careers and Employability Skills for Languages and Linguistics  Compulsory 

Exit awards

A module is given one of the following statuses: 'core' – meaning it must be taken and passed; 'compulsory' – meaning it must be taken; or 'optional' – meaning that students can choose the module from a designated list. The rules of assessment may allow for limited condonement of fails in 'compulsory' or 'optional' modules, but 'core' modules cannot be failed. The status of the module may be different in any exit awards which are available for the course. Exam Boards will consider students' eligibility for an exit award if they fail the main award or do not complete their studies.

Programme aims

  • Introduce students to contemporary linguistic approaches to the study of language
  • Offer a varied and flexible curriculum which allows students to choose their own areas of specialisation within Linguistics
  • Develop students' knowledge and understanding of key concepts, issues, ideas, theories, styles of argumentation, evaluation criteria and research methods used in contemporary work in the chosen areas of specialisation, and of associated theoretical, descriptive and methodological issues
  • Offer students the opportunity to acquire knowledge, understanding and skills in another field (if an appropriate outside option is chosen).
  • Equip students with a wide range of transferable cognitive, practical and key skills, and a foundation for further study, employment and lifelong learning.
  • Give students knowledge of the foundation and application of data analytic methods, such as programming, using databases and corpora and statistical modelling.
  • Introduce students to artifical intelligence and deep and statistical learning.
  • The outcomes listed below represent the minimum expected of a graduate on this course; it is anticipated that the vast majority of graduates will achieve significantly more.


Learning outcomes and learning, teaching and assessment methods

On successful completion of the programme a graduate should demonstrate knowledge and skills as follows:

A: Knowledge and understanding

A1: Contemporary linguistic approaches to the study of language, language learning, and language teaching and how researchers in another country approach the study of language.

A2: A selection of work within the chosen areas of specialisation in Linguistics

A3: Key concepts, issues, ideas, theories, styles of argumentation, evaluation criteria and research methods used in contemporary work in the chosen areas of Linguistics, and associated theoretical, descriptive and methodological issues.

A4: A systematic understanding of key aspects of programming, relational databases and text analytics

A5: A systematic, extensive and comparative knowledge and understanding of data visualisations, statistical modelling and decision making

A6: A systematic understanding of key aspects of linear and generalised linear models for experimental and observational data and of artificial intelligence, deep and statistical learning

Learning methods

A range of teaching and learning methods are employed which typically include: lectures, seminars and classes; tutorials for project work; library and internet materials; printed/web course materials; independent learning or research; office/email consultation with staff; written or oral feedback from staff on work.

Assessment methods

Knowledge and understanding are assessed by a range of methods which typically include some combination of the following: written unseen exams; coursework assignments; exercises; a literature review; an individual or team research project; and an oral presentation.

B: Intellectual and cognitive skills

B1: Abstract and synthesise information from a range of sources (lectures/seminars/classes, journals, books, internet etc.) identifying those ideas or findings which are most significant

B2: Make observations and generalisations about data or behaviour or other materials, using appropriate analytic techniques

B3: Critically evaluate contrasting theories, accounts, explanations, approaches, demonstrating an understanding of the relationship between theory and data and be aware of possible cross-cultural differences in the way that theories, accounts and explanations are evaluated.

B4: Identify an appropriate analytical, computational, mathematical and/or statistical model for a specific data-analytical question.

B5: Analyse a given data-analytical problem and select the most appropriate computational and statistical tools for its solution.

B6: Data pre-processing as part of analytical and statistical data analysis.

B7: Use data science skills and methods for research strategies.

Learning methods

A range of teaching and learning methods are employed which typically include: lectures, seminars and classes; tutorials for project work; library and internet materials; printed/web course materials; independent learning or research; office/email consultation with staff; written or oral feedback from staff on work

Assessment methods

Cognitive skills are assessed by a range of methods which typically include some combination of the following: written unseen exams; coursework assignments; exercises; a literature review; an individual or team research project; and an oral presentation.

C: Practical skills

C1: Gather and process information from a range of different sources

C2: Plan, undertake and present an independent piece of work which involves reviewing existing work on a given topic, making use of standard referencing conventions

C3: Utilise specialised techniques for the collection, analysis, presentation or evaluation of materials, data or behaviour

C4: Use their knowledge, understanding and skills in the systematic and critical assessment of programming languages and computational tools and packages.

C5: Use their knowledge, understanding and skills to apply a rigorous, analytic, highly quantitative approach to a problem.

Learning methods

A range of teaching and learning methods are employed which typically include: lectures, seminars and classes; tutorials for project work; library and internet materials; advice in the Departmental Handbook; printed/web course materials; independent learning or research; office/email consultation with staff; written or oral feedback from staff on work.

Assessment methods

Practical skills are assessed by a range of methods which typically include some combination of the following: written unseen exams; coursework assignments; exercises; a literature review; an individual or team research project; and an oral presentation.

D: Key skills

D1: Communicate ideas, information and arguments in a manner which is relevant, focused, effective, and clear, using an appropriate register, style and format, and with an international audience in mind

D2: Use appropriate computational tools and software to obtain, store or process information electronically and (where required) produce materials in electronic form. Use appropriate IT facilities as a tool for developing computer programmes and data analytics and text processing.

D3: Use and interpret data science techniques correctly.

D4: Analyse complex data, materials or behaviour, using appropriate specialised techniques, formulating and testing research hypotheses, identifying problems and evaluating solutions

D5: Analyse complex data, materials or behaviour, using appropriate specialised techniques, formulating and testing research hypotheses, identifying problems and evaluating solutions. Improve own learning and performance from feedback.

D6: Work autonomously showing organisation, self-discipline and time management, responding constructively to feedback and learning new material and techniques.

Learning methods

Communication skills are taught through lectures, seminars, classes, advice in the Departmental Handbook, and feedback from teachers on assessed work.

Generic IT skills are taught on induction courses run by the University and the Department, with more specialised IT skills (where appropriate) being taught on some LG courses.

Analytic and study skills (D4, D6) are taught through lectures, seminars, and classes; tutorials for project work; library and internet materials; printed/web course materials; advice in the Departmental Handbook; independent learning or research; office/email consultation with staff; written or oral feedback from staff

Assessment methods

Key skills are assessed by a range of methods which typically include some combination of the following: written unseen exams; coursework assignments; exercises; a literature review; an individual or team research project; and an oral presentation.


Note

The University makes every effort to ensure that this information on its programme specification is accurate and up-to-date. Exceptionally it can be necessary to make changes, for example to courses, facilities or fees. Examples of such reasons might include a change of law or regulatory requirements, industrial action, lack of demand, departure of key personnel, change in government policy, or withdrawal/reduction of funding. Changes to courses may for example consist of variations to the content and method of delivery of programmes, courses and other services, to discontinue programmes, courses and other services and to merge or combine programmes or courses. The University will endeavour to keep such changes to a minimum, and will also keep students informed appropriately by updating our programme specifications.

The full Procedures, Rules and Regulations of the University governing how it operates are set out in the Charter, Statutes and Ordinances and in the University Regulations, Policy and Procedures.

Contact

If you are thinking of studying at Essex and have questions about the course, please contact Undergraduate Admissions by emailing admit@essex.ac.uk, or Postgraduate Admissions by emailing pgadmit@essex.ac.uk.

If you're a current student and have questions about your course or specific modules, please contact your department.

If you think there might be an error on this page, please contact the Course Records Team by emailing crt@essex.ac.uk.