WSQ - Python Text Mining and Analytics: Transforming Text into Insights

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
• Name 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

Final Assessment

• Written Assessment - Short Answer Questions (WA-SAQ)
• Practical Performance (PP)

Promotion Code

Promo or discount cannot be applied to WSQ courses

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:

Download and Install the following software

• Python
• Visual Studio Code
• Anacoda

Sign up free Google Colab account

Hardware: Window or Mac Laptops

About Progressive Wage Model (PWM)

The Progressive Wage Model (PWM) helps to increase wages of workers through upgrading skills and improving productivity. 

Employers must ensure that their Singapore citizen and PR workers meet the PWM training requirements of attaining at least 1 Workforce Skills Qualification (WSQ) Statement of Attainment, out of the list of approved WSQ training modules.

For more information on PWM, please visit MOM site.

Funding Eligility Criteria

Steps to Apply Skills Future Claim

• The staff will send you an invoice with the fee breakdown.
• Login to the MySkillsFuture portal, select the course you’re enrolling on and enter the course date and schedule.
• Enter the course fee payable by you (including GST) and enter the amount of credit to claim.
• Upload your invoice and click ‘Submit’

SkillsFuture Level-Up Program

The  SkillsFuture Level-Up Programme provides greater structural support for mid-career Singaporeans aged 40 years and above to pursue a substantive skills reboot and stay relevant in a changing economy. For more information, visit SkillsFuture Level-Up Programme

Get Additional Course Fee Support Up to $500 under UTAP

The Union Training Assistance Programme (UTAP) is a training benefit provided to NTUC Union Members with an objective of encouraging them to upgrade with skills training. It is provided to minimize the training cost. If you are a NTUC Union Member then you can get 50% funding (capped at $500 per year) under Union Training Assistance Programme (UTAP).

For more information visit NTUC U Portal – Union Training Assistance Program (UTAP)

Steps to Apply UTAP

• Log in to your U Portal account to submit your UTAP application upon completion of the course.

Note

• SSG subsidy is available for Singapore Citizens, Permanent Residents, and Corporates.
• All Singaporeans aged 25 and above can use their SkillsFuture Credit to pay. For more details, visit www.skillsfuture.gov.sg/credit
• An unfunded course fee can be claimed via SkillsFuture Credit or paid in cash.
• UTAP funding for NTUC Union Members is capped at $250 for 39 years and below and at $500 for 40 years and above.
• UTAP support amount will be paid to training provider first and claimed after end of class by learner.

Appeal Process

1. The candidate has the right to disagree with the assessment decision made by the assessor.
2. When giving feedback to the candidate, the assessor must check with the candidate if he agrees with the assessment outcome.
3. If the candidate agrees with the assessment outcome, the assessor & the candidate must sign the Assessment Summary Record.
4. If the candidate disagrees with the assessment outcome, he/she should not sign in the Assessment Summary Record.
5. If the candidate intends to appeal the decision, he/she should first discuss the matter with the assessor/assessment manager.
6. If the candidate is still not satisfied with the decision, the candidate must notify the assessor of the decision to appeal. The assessor will reflect the candidate’s intention in the Feedback Section of the Assessment Summary Record.
7. The assessor will notify the assessor manager about the candidate’s intention to lodge an appeal.
8. The candidate must lodge the appeal within 7 days, giving reasons for appeal 
9. The assessor can help the candidate with writing and lodging the appeal.
10. he assessment manager will collect information from the candidate & assessor and give a final decision.
11. A record of the appeal and any subsequent actions and findings will be made.
12. An Assessment Appeal Panel will be formed to review and give a decision.
13. The outcome of the appeal will be made known to the candidate within 2 weeks from the date the appeal was lodged.
14. The decision of the Assessment Appeal Panel is final and no further appeal will be entertained.
15. Please click the link below to fill up the Candidates Appeal Form.

• 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.

Dr. Alvin Ang: Dr. Alvin Ang is an ACLP-certified trainer and data science educator with a Ph.D. 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 NLP, and published research in analytics and optimization, Alvin brings academic depth and applied expertise in machine learning and text analytics.

In his Python text mining courses, Alvin blends theory with practice. He trains learners in techniques such as tokenization, vectorization (TF-IDF, word embeddings), and sentiment analysis, supported by case studies in business and social media analytics. His teaching style emphasizes rigorous analysis combined with practical implementation, enabling participants to transform raw text into actionable insights.

Yeo Hwee Theng: 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 the Data & Analytics Product Lead at Amplify Health, she oversees large-scale data initiatives that drive business intelligence and predictive modeling across the Asia-Pacific region. Her previous roles as AI & Data Architect at Huawei International and Senior Data Scientist at DataRobot have equipped her with 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,” Hwee Theng focuses on enabling learners to extract actionable intelligence from unstructured text data using Python. Her sessions cover the complete NLP workflow, including text preprocessing, feature extraction, sentiment analysis, and topic modeling. Through practical projects and case studies, she teaches participants how to apply libraries such as NLTK, spaCy, and scikit-learn to automate text analysis and build insight-driven business applications.

Mohamed Afiq: Mohamed Afiq is a data analytics professional specializing in Python programming, natural language processing, and financial data modeling. 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, Afiq is known for making complex technical concepts approachable, equipping learners with hands-on coding skills to implement real-world data solutions.

In “Python Text Mining and Analytics: Transforming Text into Insights,” Afiq guides learners through practical applications of text analytics in business and finance. His sessions emphasize techniques such as text tokenization, frequency analysis, and vectorization, enabling participants to turn qualitative data into measurable insights. By combining theoretical foundations with interactive exercises, he helps learners build Python-based solutions that enhance data interpretation and decision-making.

Bernard Peh: Bernard Peh is a senior digital transformation and data analytics trainer with deep expertise in business intelligence, data visualization, and automation. As a certified Principal Trainer and ACLP-qualified educator, he has trained professionals across sectors in Microsoft Power Platform, Python analytics, and AI-driven workflow optimization. Bernard’s work focuses on helping organizations harness structured and unstructured data to drive efficiency, insight, and innovation.

In “Python Text Mining and Analytics: Transforming Text into Insights,” Bernard introduces participants to the business applications of text analytics and automation. His sessions cover data extraction, cleaning, and visualization using Python’s analytical libraries, enabling learners to connect text analytics to business reporting. By integrating practical coding with strategic interpretation, he empowers participants to apply NLP techniques effectively for customer sentiment analysis, feedback categorization, and intelligent content discovery.

Quah Chee Yong (CY Quah): CY Quah is an ACLP-certified trainer and data science professional with extensive experience in Python, NLP, and machine learning. He has led AI training programs for SAP, Temasek Polytechnic, and IMDA under the SGUnited Mid-Career Pathways initiative, and has delivered corporate workshops on text analytics, recommender systems, and chatbot development. His expertise includes applying NLP tools such as NLTK, spaCy, and Gensim for sentiment analysis, topic modeling, and text classification.

In his Python text mining training, CY Quah focuses on helping learners transform unstructured text into meaningful insights. He teaches data cleaning, text preprocessing, feature extraction, and model development for tasks such as sentiment detection and recommendation systems. With a project-based approach, he ensures learners acquire skills directly applicable to business and research contexts.