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Text Mining and Natural Language Processing

In this module, students will look at analysing unstructured data such as that found on social media, newspaper articles, videos, and more.



Specifically, students will look at text techniques for text mining and natural language processing using R and Python code to produce graphical representations of unstructured data and carry out sentiment analysis.



This module focuses on learning key concepts, tools, and methodologies for natural language processing and emphasises hands-on learning through guided tutorials and real-world examples.



Core Reading List:

Text Mining with R

Julia Silge and David Robinson.

O’Reilly



Natural Language Processing with Python

Steven Bird, Ewan Klein and Edward Loper.

O’Reilly

    Application requirements

    Candidates who apply for this course must have a recognised undergraduate degree or equivalent. Candidates without a degree but with other relevant qualifications and/or work experience can also be considered.

    

    English language competency at an IELTS 6.5 (or equivalent) is required of all applicants whose first language is not English. Where students can demonstrate previous substantial studies or work experience in English, this requirement can be waived.

    

  • Accreditation: Unaccredited
  • Total workload: 150 hours
  • Requires extra purchases (outside texts, etc.): Yes, purchases required
  • ID verification: Required
  • Admission requirements: Application required
  • Minimum education requirement for students: Undergraduate