Modules

Core Module Information
Module title: Scripting for Natural Language Processing
SCQF level: 09:
SCQF credit value: 20.00
ECTS credit value: 10
Module code: SET09125
Module leader: Md Zia Ullah
School School of Computing, Engineering and the Built Environment
Subject area group: Computer Science
Prerequisites

There are no pre-requisites for this module to be added
Description of module content:

Conversational AI has attracted increasing attention in academia and industry in the past five years. There have been over 100 companies working on voice-AI or Conversational AI-based products, including the world’s most valuable brands (e.g. Apple, Google, Amazon, Huawei, etc.) and start-ups (e.g. Emotech, Alana). On the other hand, there are global competitions organised by Amazon, which focus on state-of-the-art technologies in diverse conversational AI tasks, e.g. social conversation and task-oriented conversation. This module can help students learn about conversational AI, related NLP and ML/DL methodologies, and state-of-the-art research topics. It also can provide students with the experience of team-level project work on realistic human daily problems, which offers a good insight into academic and industrial perspectives. The module covers the following topics: • Data types and formats: numerical and time series, graph, textual, unstructured, • Data sources and interfaces: open data, APIs, social media, web-based • NoSQL databases such as document (MongoDB), graph and key-value pair • Techniques for dealing with large data sets, including Map Reduce • Developing Data-Driven Applications in Python The Benchmark Statement for Computing specifies the range of skills and knowledge that should be incorporated in computing courses. This module encompasses cognitive skills in Computational Thinking, Modelling and Methods and Tools, Requirements Analysis and practical skills in specification, development and testing and the deployment and use of tools and critical evaluation, and providing useful generic skills for employment.
Learning Outcomes for module:

Upon completion of this module you will be able to

LO1: Apply the fundamental concepts of NLP

LO2: Critically select, describe, and apply appropriate feature engineering and machine learning models for NLP tasks, including text processing, language understanding, and generation

LO3: Design and implement NLP applications using appropriate tools, ensuring efficient and reproducible workflows

LO4: Evaluate the effectiveness of implemented NLP solutions, assessing model performance, scalability, and ethical considerations in language processing applications
Full Details of Teaching and Assessment
2026/7, Trimester 2, In Person,
VIEW FULL DETAILS
Occurrence: 001
Primary mode of delivery: In Person
Location of delivery: MERCHISTON
Partner:
Member of staff responsible for delivering module: Md Zia Ullah
Module Organiser:


Student Activity (Notional Equivalent Study Hours (NESH))
Mode of activityLearning & Teaching ActivityNESH (Study Hours)NESH Description
Face To Face Lecture 20 2 hours lecture content with focusing on technique, concepts and other related tools.
Face To Face Practical classes and workshops 20 Students will spend 2 hours per week practising their techniques.
Face To Face Guided independent study 160 Students will work by themselves through all provided materials and their research topic till the end of the teaching period.
Total Study Hours200
Expected Total Study Hours for Module200


Assessment
Type of Assessment Weighting % LOs covered Week due Length in Hours/Words Description
Project - Practical 30 1 Week 7 HOURS= 1000 words Students will complete a series of small NLP tasks, including text preprocessing, feature extraction, and basic language modeling (N-grams) using Python in Jupyter notebook. The student will document their approach and findings for each task within the same notebook.
Project - Practical 70 2~3~4 Exam Period HOURS= 2000 words Building on Coursework 1, students will design and develop a complete NLP pipeline addressing a real-world problem (e.g., sentiment analysis). The project will integrate techniques from traditional and modern NLP for feature representation and machine learning. The submission should include organised and well-documented code in a Jupyter notebook. Furthermore, the notebook must detail the implementation, evaluation, visualisation, and ethical considerations, as part of the report.
Component 1 subtotal: 100
Component 2 subtotal: 0
Module subtotal: 100

Indicative References and Reading List - URL:
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