You have no items in your shopping cart.
Course Details
Course Details
What You'll Learn
Topic 1: Overview of Text Mining and Text Analytics
Introduction to Natural Language Processing (NLP)
Applications of Text Analytics and Text Mining for Business Intelligence
Cross-Industry Standard Process for Data Mining (CRISP-DM)
Topic 2: Text Cleaning and Pre-processing
Install Python NLTK Package
Read In Text Corpus
Remove Punctuation and Stop Words
Pre-process Text using Tokenization, Stemming, Lemmatization
Vectorize Text using Term Frequency (TF) Vectorization, N-gram and Inverse-Document Frequency (TF-IDF)
Topic 3: Text Analytics
Part of Speech (POS) Tagging
Named Entity Recognition (NER)
Text Link Analysis and Feature Engineering
Topic 4: Sentimental Analysis
Overview of Machine Learning
Install Python Scikit Learn Package
Build a Machine Learning Model for Sentimental Analysis
Model Evaluation
Topic 5: Text Summarization
Summarize Sentiment Analysis
Visualize Text Summarization
Assessment
- Written Exam
- Practical Exam
Course Info
Minimum Entry Requirement
Knowledge and Skills
- Able to operate using computer functions
- Minimum 3 GCE ‘O’ Levels Passes including English or WPL Level 5 (Average of Reading, Listening, Speaking & Writing Scores)
Attitude
- Positive Learning Attitude
- Enthusiastic Learner
Experience
- Minimum of 1 year of working experience.
Minimum Software/Hardware Requirement
Software:
You will need the following for this course:
- Python 3 – free
- Visual Studio Code – free
- Google Colab – free, runs in the browser with a Google account
Hardware: Windows PC or Mac laptop
Funding Eligibility Criteria
Self-sponsored learners (individuals)
- Singapore Citizens and Singapore Permanent Residents aged 21 and above.
- Up to 50% course fee funding. Singapore Citizens aged 40 and above receive up to 70% under the Mid-career Enhanced Subsidy (MCES).
- Singapore Citizens aged 25 and above can pay the remaining fee with SkillsFuture Credit.
Company-sponsored learners
- Singapore Citizens and Singapore Permanent Residents who are direct employees of the sponsoring company.
- Up to 50% course fee funding, or up to 70% for employees of SMEs (Enhanced Training Support for SMEs) and for Singapore Citizens aged 40 and above. An SME has no more than 200 employees or an annual turnover of no more than $100 million.
- Employers can also claim Absentee Payroll funding for employees who attend during working hours.
- Eligible companies can offset their out-of-pocket fees with SkillsFuture Enterprise Credit (SFEC).
Conditions for all funded learners
- Attendance is taken digitally with Singpass for every session, including the assessment.
- You must complete the course and pass all assessments (assessed as Competent).
- If these conditions are not met, the funding is clawed back and the learner or employer pays the full course fee.
- 9% GST is charged on the full course fee before funding.
- Promo and discount codes cannot be used on funded courses.
- Any balance after funding can be paid in cash, with SkillsFuture Credit, with PSEA (Singapore Citizens with a PSEA).
How to Apply SkillsFuture Credit Claim for Individuals
Singapore Citizens aged 25 and above can use their SkillsFuture Credit (base tier) to pay the course fee after funding. The $4,000 mid-career top-up cannot be used for this course.
- Register for this course as self-sponsored and tell us you will use SkillsFuture Credit. Do not pay the amount you plan to claim.
- We email you an invoice showing the course fee after funding.
- From 60 days before the course starts, and no later than the course start date, log in to MySkillsFuture with Singpass and click Claim credits.
- Click Submit a Claim and search for this course by its course code, TGS-2026064715.
- Select your class start date, enter the course fee payable after funding and the amount of credit to claim, then upload the invoice (PDF, JPG or PNG, under 4MB).
- Review, accept the terms and click Agree and Submit. The credit is paid directly to us; processing can take up to 14 working days.
Changing or withdrawing: cancel the claim on MySkillsFuture at least one day before the course starts. If your class date changes, cancel the claim and submit a new one with the new date.
How to Apply SkillsFuture Enterprise Credit (SFEC)
SFEC helps companies pay for employee training. The current SFEC ends on 30 November 2026, and a redesigned SFEC starts on 1 December 2026.
Current SFEC: classes ending on or before 30 November 2026
- Eligible companies received a one-off $10,000 credit, notified by letter to their Corppass administrator. Eligibility generally required at least 3 Singapore Citizen or PR employees and Skills Development Levy contributions.
- SFEC covers up to 90% of the company's out-of-pocket course fee after other subsidies.
- Check your company's SFEC eligibility and balance on EnterpriseSG.
- Register your Singapore Citizen or PR employee for a class that ends by 30 November 2026, as company-sponsored, and pay the course fee after funding.
- Your employee attends and completes the course. We submit the attendance and assessment results to SWDA.
- No separate claim is needed. SFEC is calculated automatically once the course fee grant is approved and is paid quarterly to your company's IRAS GIRO or PayNow Corporate account.
Any unused balance of the current SFEC expires on 30 November 2026.
Redesigned SFEC from 1 December 2026
Companies with at least 3 resident employees receive a fresh $10,000 in an online SFEC wallet that offsets out-of-pocket fees upfront at enrolment, instead of reimbursing them afterwards. SWDA has not yet published the support rates and claim steps; see OurWorkforce or our SFEC guide.
How to Apply PSEA
Singapore Citizens with a Post-Secondary Education Account (PSEA), which stays open until around the year you turn 31, can use it to pay the course fee after funding for PSEA-approved courses. You can also use a sibling's PSEA after your own has been used up.
- Check that this course shows the PSEA tag on its MySkillsFuture page.
- Check your PSEA balance on MOE's Student Finance System with Singpass.
- Download and complete the PSEA withdrawal form, then submit it to us.
- We submit the withdrawal request to MOE. You (and your sibling, if you are using their account) authorise it in the Student Finance System. Nothing is withdrawn without this authorisation.
- MOE pays us directly. If you have already paid, we refund the amount covered by PSEA.
More details: MOE PSEA overview.
Appeal Process
If you disagree with an assessment decision, you have the right to appeal.
- When the assessor gives you feedback, say whether you agree with the outcome. If you agree, you and the assessor sign the Assessment Summary Record; if you disagree, do not sign it.
- Discuss the decision with the assessor or the assessment manager first.
- If you are still not satisfied, tell the assessor you intend to appeal. The assessor records this in the feedback section of the Assessment Summary Record and informs the assessment manager.
- Lodge your appeal within 7 days, giving your reasons, using the Candidate Appeal Form. The assessor can help you write and lodge it.
- An Assessment Appeal Panel reviews the information from you and the assessor and makes a decision. A record of the appeal and its outcome is kept.
- You will be told the outcome within 2 weeks of lodging the appeal. The panel's decision is final.
Job Roles
Job Roles
- Data Scientist
- Natural Language Processing Engineer
- Text Mining Specialist
- Machine Learning Engineer (focused on text data)
- Content Analyst
- Big Data Analyst
- Sentiment Analysis Specialist
- Information Retrieval Engineer
- Computational Linguist
- Data Journalist (with coding skills)
- SEO Specialist (using text analytics for insights)
- Digital Marketing Analyst
- Knowledge Engineer
- Chatbot Developer
- Content Recommendation System Developer.
Trainers
Trainers
Dr. Alfred Ang approaches text analytics with the rigour of a researcher and the instincts of a builder. He holds a PhD in Electrical Engineering from NUS, an MSc in Electrical and Electronic Engineering from NTU and a B.Sc (Hons) in Physics from NUS, has published more than 20 peer-reviewed papers and co-invented more than 20 US patents, and is a Microsoft Certified Data Scientist (DP-100), AI Engineer Associate (AI-102) and Power BI Data Analyst (PL-300), as well as a Neo4j Certified Professional and an IBM certified instructor for the AI Practitioners course. As a full-stack AI developer and Agentic AI solution architect, and Managing Director, CIO, CTO and Chief Instructional Designer of Tertiary Infotech Academy Pte Ltd, he works with language at scale: LLMs, RAG pipelines that retrieve and ground answers in documents, and conversational agents for customer service and analytics. His GitHub portfolio of more than 300 public repositories includes data-mining-ml-labs, interactive Python and Streamlit labs for data mining and machine learning; n8n-ragdemo and n8n-ragdemo2, which turn PDFs and course brochures into searchable vector stores for AI chatbots; scannerapp, an offline document scanner with OCR that turns paper into text; and AIInterviewing, which scores interview answers against role-specific rubrics. He is an ACTA-certified trainer and DACE-certified curriculum developer. He also leads the design of Skills Framework-aligned WSQ and IBF programmes and customised corporate training in AI and data engineering, and his career spans the defence, electronics and semiconductor industries.
In CASL - Python Text Mining and Analytics: Transforming Text into Insights, Dr. Ang shows learners how classic text-mining techniques and modern AI fit together. The course opens with natural language processing, the business uses of text analytics and the CRISP-DM framework that structures every project. Learners then install NLTK, read in a text corpus and clean it, before pre-processing with tokenisation, stemming and lemmatisation and vectorising text with term frequency, n-grams and TF-IDF. Text analytics follows, with part-of-speech tagging, named entity recognition, text link analysis and feature engineering, after which learners build and evaluate a scikit-learn model for sentiment analysis and finish with summarising and visualising results. Because he builds retrieval and LLM systems himself, he can explain why these fundamentals still matter: clean, well-represented text drives better models, better search and better grounding for AI assistants, and a simple TF-IDF baseline is often the honest benchmark a large model must beat. His Neo4j background also helps him make text link analysis intuitive. Sessions are hands-on in Python, with learners interpreting results in business terms, so they leave able to turn unstructured text into insights. He also shows where large language models can speed up tasks such as labelling, summarising and theme extraction, and why their outputs still need validation against a held-out sample. To explore his data and AI projects, learners can view his GitHub portfolio at https://github.com/alfredang and connect with him on LinkedIn at https://www.linkedin.com/in/angchewhoe/
Yeo Hwee Theng is a data science and AI strategy leader with extensive experience in enterprise analytics, natural language processing (NLP) and machine learning. As Data & Analytics Product Lead at Amplify Health, she oversees large-scale data initiatives that drive business intelligence and predictive modelling across the Asia-Pacific region. Her earlier roles as AI & Data Architect at Huawei International and Senior Data Scientist at DataRobot gave her hands-on expertise in text analytics, sentiment analysis and AI-driven knowledge extraction. She holds a Master of Technology in Enterprise Business Analytics from the National University of Singapore and an Advanced Certificate in Learning and Performance (ACLP).
In Python Text Mining and Analytics: Transforming Text into Insights, she helps learners extract actionable intelligence from unstructured text using the CRISP-DM framework. Participants install Python's NLTK package, read in text corpora and clean them by removing punctuation and stop words, then apply tokenisation, stemming, lemmatisation and TF, n-gram and TF-IDF vectorisation. They move on to part-of-speech tagging, named entity recognition, text link analysis and feature engineering, before building sentiment analysis with machine learning. Her practical projects and case studies connect each technique to business intelligence, so participants leave able to automate text analysis and build insight-driven applications for their own organisations.
Bernard Peh is a senior data analytics and digital transformation trainer with deep expertise in business intelligence, data visualisation and automation. A certified Principal Trainer and ACLP-qualified educator, he has trained professionals across sectors in Python analytics, Microsoft Power Platform and AI-driven workflow optimisation, and his work focuses on helping organisations harness both structured and unstructured data. He is also a data scientist with more than two decades of experience, serving as Senior Partner and Lead Data Scientist at Big Data Venture Capital, where his hands-on skills span data acquisition, cleaning, feature engineering, ETL and visualisation.
CASL - Python Text Mining and Analytics: Transforming Text into Insights applies these skills to unstructured text. Bernard introduces the business applications of text analytics and natural language processing within the CRISP-DM process, then guides learners through cleaning and pre-processing a text corpus with NLTK, vectorising text with TF-IDF and n-grams, part-of-speech tagging, named entity recognition and feature engineering. Participants then build and evaluate a machine learning model for sentiment analysis with scikit-learn and summarise and visualise the results. By pairing practical coding with business interpretation, Bernard helps learners apply NLP techniques to customer feedback, sentiment tracking and content discovery.
Mohamed Afiq is a data analytics professional specialising in Python programming, natural language processing and financial data modelling. With a background in data engineering and quantitative analysis, he has worked on projects involving sentiment classification, chatbot development and text-based data mining. As an instructor, he is known for making complex technical concepts approachable, equipping learners with hands-on coding skills to implement real-world data solutions.
In CASL - Python Text Mining and Analytics: Transforming Text into Insights, Afiq guides learners through practical applications of text analytics in business and finance. Participants start with an overview of text mining, text analytics and natural language processing, their uses in business intelligence and the CRISP-DM framework. They then install the Python NLTK package, read in a text corpus and pre-process it by removing punctuation and stop words, tokenising and stemming. His sessions emphasise techniques such as text tokenisation, frequency analysis and vectorisation, enabling participants to turn qualitative data into measurable insights. His project work in sentiment classification gives learners realistic context for why each pre-processing step matters. By combining theoretical foundations with interactive exercises, he helps learners build Python-based solutions that improve data interpretation and decision-making.
Dr. Alvin Ang is an ACLP-certified trainer and data science educator with a PhD in Operations Research from Nanyang Technological University. He has taught extensively at universities and professional institutions, covering Python, R, statistics and text mining. With IBM certifications in data science and natural language processing, and published research in analytics and optimisation, he brings academic depth and applied expertise in machine learning and text analytics. His teaching style emphasises rigorous analysis combined with practical implementation, helping participants turn raw text into actionable insights.
In CASL - Python Text Mining and Analytics: Transforming Text into Insights, Dr. Ang blends theory with practice. Learners start with an overview of text mining, text analytics and natural language processing, their business intelligence applications and the CRISP-DM framework. They then install the Python NLTK package, read in a text corpus and pre-process it by removing punctuation and stop words, tokenising and stemming. He trains learners in techniques such as vectorisation with TF-IDF and word embeddings, and sentiment analysis, supported by case studies in business and social media analytics. Participants leave able to build effective text analytics solutions in Python that turn customer comments, reviews and documents into evidence for decisions.
Review
Customer Reviews (3)
- will recommend Review by Course Participant/Trainee
-
Maybe more real-life examples will be good1. Do you find the course meet your expectation? 2. Do you find the trainer knowledgeable in this subject? 3. How do you find the training environment
good session. Very good introduction on the capabilities of Python to do text analytics (Posted on 3/19/2023) - will recommend Review by Course Participant/Trainee
-
1. Do you find the course meet your expectation? 2. Do you find the trainer knowledgeable in this subject? 3. How do you find the training environment
On behalf of my team, we would like to thank the school, and especially Marcel for the informative course we had. Having gone for many data related courses over the past 8 years, this is one, if not the most enjoyable and informative course I attended. This is especially so because Marcel was very knowledgeable in this area and could address many of the technical queries we had. The team was most impressed by this.
(Posted on 11/8/2022) - will recommend Review by Course Participant/Trainee
-
Marcel is one of the most excellent and knowledgeable lecturers i have encountered. Your company would do well to hold on to him (Posted on 11/7/2022)1. Do you find the course meet your expectation? 2. Do you find the trainer knowledgeable in this subject? 3. How do you find the training environment
Write Your Own Review
- Recommended Courses
- Related Courses
