Developed by the National Centre for Population Health research team, this course offers comprehensive insights into text mining methods for analysing unstructured administrative data.
- 2 days
- Paid course
- Live online
- Statement of participation
- 2 & 3 Dec 2024
- 10:00 to 15:00 each day
- Online via Teams
Taught by Amrita Bandyopadhyay, Dr Jonathan Kennedy, Dr Tasha Kenney, Dr Mohamed Mhereeg
Course description:
This course offers a comprehensive introduction to text mining techniques for analysing unstructured administrative data. It is designed for individuals new to handling unstructured data, with a focus on transforming raw, free-text data into valuable insights that inform decision-making processes.
Learners will explore the full-text mining process, including data preprocessing, sentiment analysis, and the application of various text mining methods to extract and visualise meaningful insights from administrative datasets.
The course focuses on practical and hands-on learning. Learners will gain the skills and tools needed to interpret and report on administrative data effectively. This will be guided by real-world case studies from researchers at Administrative Data Research Centre Wales, Health Data Research UK Wales, and the National Centre for Population Health and Wellbeing.
This module aims to enable students to:
- Gain a comprehensive understanding of real-world unstructured free-text administrative data.
- Learn how text mining methods can be effectively applied to analyse qualitative administrative data.
- Develop skills to interpret findings accurately and derive meaningful insights from unstructured free-text administrative data.
Explore techniques for uncovering patterns, trends, and correlations within unstructured administrative data through text mining.
Learning mode
This course will be delivered live online over two days.
Who should attend?
This course is suitable for individuals who are new to handling unstructured administrative data. Prior introductory knowledge of R is assumed for participants.
Pre-requisite
Basic understanding of R software language (here are some suggested links: Udemy, Great Learning, R Tutorial with Quick-R)
Skills area and competencies
Skill area:
- Problem Solving, Analysis, Statistical Modelling, Visualisation
Skill competencies:
- Identifying and Applying Technical Solutions and Project Management Approaches
- Data Preparation and Feature Modelling
- Data Analysis and Model Building
Learning outcomes
- Demonstrate a thorough understanding of real-world unstructured free-text administrative data, including its characteristics, sources, and challenges.
- Apply text mining methods effectively to analyse qualitative administrative data, utilizing appropriate techniques for preprocessing, feature extraction, and modelling.
- Interpret findings derived from text mining analyses of unstructured free-text administrative data accurately, synthesizing insights and drawing meaningful conclusions to inform decision-making processes.
Transferrable skills
- Data Literacy: Students will enhance their ability to comprehend and work with real-world unstructured free-text administrative data, improving their overall data literacy.
- Analytical Skills: By applying text mining methods to analyse qualitative administrative data, students will develop strong analytical skills, enabling them to extract meaningful insights from complex datasets.
- Problem-Solving: Students will learn to address challenges associated with unstructured data, developing effective problem-solving skills to preprocess, extract features, and model text data.
- Critical Thinking: Interpreting findings from text mining analyses requires critical thinking skills to evaluate and synthesize insights, fostering a deeper understanding of complex datasets.
- Communication: Students will refine their communication skills by articulating their understanding of real-world unstructured free-text administrative data and conveying insights derived from text mining analyses effectively.
- Technical Proficiency: Through practical application, students will gain proficiency in using text mining techniques and tools, enhancing their technical skill set.
- Decision-Making: By drawing meaningful conclusions from text-mining analyses, students will develop decision-making skills that are informed by insights derived from complex data.
For more information and to enrol, visit the Datacise website