Text Analytics

Course Content

• Text Preprocessing and Preparation: Learn to clean and prepare text data based on business requirements.

• Text Vectorization: Convert text into numerical data using techniques such as TF-IDF, word embeddings, and contextualised embeddings; unleashing the power of feature engineering.
• Predictive Modelling with Machine Learning: Explore various techniques and algorithms to predict categories, classify documents, and uncover patterns.
• Clustering and Topic Modelling: Group similar texts together and reveal underlying themes. Discover the art of topic extraction.
• Information Extraction and Classification: Extract entities, relationships, and structured information from unstructured text. Explore techniques ranging from rule-based to Language Model-based. Learn how to utilise Large Language Models (LLMs) such as ChatGPT.

• Solid Grasp of Text Analytics: Understand diverse applications, essential processing techniques, and LLM-based approaches. 
• Theme Identification: Identify main themes or topics in the collection of documents or textual data (e.g. the prominent issues customers are complaining about). 
• Document Categorization: Categorise documents based on discovered topics and user-definable criteria, such as grouping complaints about similar issues for further investigation.
• Machine Learning Applications: Discover how machine learning techniques (both supervised and unsupervised) can be applied to categorise and structure textual information, enabling integration into the traditional data mining process.
• LLM Techniques: Use LLMs such as ChatGPT for extracting entities, relations, and events from text data.

Who Should Attend

• Data Analysts, Business Users, IT Professionals: Enhance your data analysis skills by incorporating text analytics into your toolkit.
• Professionals Handling Large Textual Data: Business and IT professionals seeking to elevate analytical skills to handle large amounts of textual data (e.g. customer feedback, product reviews on social media, etc.) for insights to improve business processes and decision-making. 
• Anyone Interested in Drawing Insights from Text Data: Anyone seeking to extract meaningful information from text data and uncover hidden insights for better decision-making through LLMs such as ChatGPT.

Prerequisites

• Possess foundational knowledge in data analytics and predictive modelling (at the level of Predictive Analytics - Insights of Trends and Irregularities or Problem Solving using Pattern Recognition course offered by NUS-ISS).
• Competent in Python programming.
• Experienced in using Jupyter Notebooks, Google Colab, and well-versed in package installation.

Course Logistics

• No Printed Materials: Course materials are accessed digitally. Do kindly note that no printed copies of course materials will be issued.
• Device Requirements: Bring an internet-enabled device (laptop, tablet, etc) with power chargers to access and download course materials.

Fees & Subsidies

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WIFI Access

WIFI access will be made available to participants.

Venue

NUS-ISS
25 Heng Mui Keng Terrace
Singapore 119615

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In the event of a change of venue, participants are advised to refer to the acceptance email sent one week prior to the commencement date.

Course Confirmation

All classes are subject to confirmation and NUS-ISS will send an acceptance email to participants one week prior to the commencement date. Confirmed registrants are to attend and complete all lectures, class exercises, workshops and assessments (where applicable). Additionally, all responses to feedbacks and surveys conducted by NUS-ISS and its partners must be submitted. All training and assessments will be delivered as described in the course webpage.

General Enquiry

Please feel free to write to ask-iss@nus.edu.sg if you have any enquiry or feedback.