WSQ - Text Analytics with R

Topic 1: Overview of Text Mining and Text Analytics

• Introduction to Text Mining and Text Analytics
• Applications of Text Mining and Text Analytics for Business Intelligence
• Cross-Industry Standard Process for Data Mining (CRISP-DM)

Topic 2: Text Cleaning and Pre-processing

• Install R Text Mining Packages
• 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 R Machine Learning 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:

You can download and install the following software:

• R

Hardware: Windows and 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.

Dwight Nuwan Fonseka: Dwight Fonseka is an ACLP-certified trainer and Head of Data Science at Plano Pte. Ltd., specializing in R programming, data visualization, and predictive analytics. He has extensive training experience as an adjunct lecturer at LSBF and associate trainer at Tertiary Courses, where he has delivered courses in R, Tableau, machine learning, and text mining. His expertise covers R libraries, h2oAI, Keras, Spark, and cloud-based analytics, with strong experience applying these to real-world projects in healthcare and social media analytics.

In his Text Analytics with R training, Dwight emphasizes practical applications such as sentiment analysis, topic modeling, and social media text mining. He guides learners through R packages for data cleaning, natural language processing, and visualization, ensuring they gain hands-on experience. By integrating project-based learning and case studies, Dwight equips participants with the ability to transform raw text into actionable insights using R.

Dr. Alvin Ang: Dr. Alvin Ang is an ACLP-certified trainer with a Ph.D. in Operations Research from Nanyang Technological University and a strong track record in data science, machine learning, and text analytics. He has taught extensively at universities and training institutions, delivering courses in R, Python, data visualization, and advanced analytics. His IBM and Kaggle certifications, along with published research in analytics and optimization, underscore his academic and applied expertise.

In his Text Analytics with R courses, Alvin focuses on both the theoretical underpinnings and hands-on practice. He trains learners to use R for text preprocessing, topic modeling, and statistical analysis, applying methods such as sentiment analysis and NLP. His teaching approach balances statistical rigor with practical case studies, enabling learners to apply text analytics effectively in business, research, and digital transformation contexts.

Terence Ee: Terence Ee is a data analytics consultant and corporate trainer with over 20 years of experience in business process optimization, statistical modeling, and quality management. Having worked across technology, engineering, and corporate training sectors, he specializes in helping organizations leverage data analytics to improve operational efficiency and decision-making. As an experienced educator, Terence has trained professionals in R, Python, and Excel analytics, focusing on transforming raw data into meaningful insights through structured methodologies and visualization techniques.

In “Text Analytics with R,” Terence teaches participants how to analyze and interpret unstructured text data using R’s powerful text mining and visualization tools. His sessions cover data cleaning, tokenization, sentiment analysis, and topic modeling using packages such as tm, tidytext, and ggplot2. By blending statistical rigor with practical application, he enables learners to uncover patterns, trends, and insights from text data that support evidence-based business strategies.

Quah Chee Yong: Quah Chee Yong is a data scientist and machine learning specialist with deep expertise in programming, automation, and predictive analytics. With a strong background in data engineering and applied statistics, he has worked on analytics projects across industries involving customer segmentation, predictive modeling, and text-based data mining. His professional experience includes using R, Python, and SQL for data-driven decision support, process automation, and performance optimization.

In “Text Analytics with R,” Chee Yong focuses on developing learners’ hands-on proficiency in R for natural language processing and text analytics. His sessions emphasize techniques for text cleaning, word frequency analysis, sentiment scoring, and visual storytelling through word clouds and clustering. Through step-by-step instruction and real-world case studies, he empowers participants to convert textual data into structured insights that enhance business intelligence and decision accuracy.

Bernard Peh: Bernard Peh is an accomplished data analytics and digital transformation trainer with extensive experience in business intelligence, automation, and workflow optimization. As a certified Principal Trainer and ACLP-qualified educator, he has delivered numerous programs in data analytics, R programming, and AI-driven data visualization. Bernard’s training approach emphasizes the practical application of analytical tools to address real-world business challenges, bridging the gap between data science and operational decision-making.

In “Text Analytics with R,” Bernard guides learners through the analytical process of extracting, transforming, and interpreting text data. His sessions introduce participants to R’s text mining ecosystem, including sentiment analysis, keyword extraction, and clustering methods. With his structured and application-oriented teaching style, he helps participants translate raw text into actionable insights that drive marketing intelligence, customer understanding, and data-informed strategy development.