Trainings
Workshops and training programmes delivered on AI, Machine Learning, Data Analytics, and programming. Certified HRDF trainer.
Introduction to Power Query and Power Pivot
Hands-on introduction to Power Query for data transformation and Power Pivot for data modelling in Excel. Covers importing data from multiple sources, cleaning and shaping data with Power Query, and building powerful pivot tables and calculated measures with Power Pivot.
A Gentle Introduction to Tidy Statistics in R
An accessible introduction to statistical analysis using the tidyverse in R. Covers foundational statistical concepts — descriptive statistics, hypothesis testing, and correlation — using modern tidy data principles and ggplot2 visualizations. Ideal for beginners with no prior R experience.
Data Analytics with R
Comprehensive 2-day programme covering data wrangling with dplyr and tidyr, exploratory data analysis, visualization with ggplot2, and basic statistical modelling in R. Participants leave with hands-on experience analysing real-world datasets from import to insight.
Data Analytics and Machine Learning with Python
Intensive 5-day bootcamp covering the full data analytics and ML workflow in Python. Topics include Python fundamentals, pandas and NumPy for data manipulation, Matplotlib and Seaborn for visualization, scikit-learn for machine learning (regression, classification, clustering), and model evaluation and deployment best practices.
Introduction to Artificial Intelligence
A non-technical introduction to Artificial Intelligence for business professionals and decision-makers. Covers what AI is, key AI concepts (ML, deep learning, NLP, computer vision), current AI applications across industries, ethical considerations, and how organisations can start their AI journey.
Introduction to Industry Revolution 4.0 (IR 4.0) and Data Driven Organisation
An overview of the Fourth Industrial Revolution and its impact on businesses and the workforce. Covers IR 4.0 technologies (IoT, AI, Big Data, Cloud, Robotics), the concept of a data-driven organisation, and practical strategies for digital transformation and data adoption in the Malaysian context.
Introduction to R
A structured 3-day introduction to R programming for data science. Covers R syntax and data types, importing and cleaning data, data transformation with dplyr, exploratory data analysis, and producing publication-quality visualizations with ggplot2. No prior programming experience required.
Machine Learning with R
An intensive 5-day programme covering machine learning theory and practical implementation in R. Topics include supervised learning (linear regression, logistic regression, decision trees, random forest, SVM), unsupervised learning (k-means, hierarchical clustering), model selection, cross-validation, and feature engineering.
Introduction to Big Data and R Programming
An introductory overview of the Big Data landscape and how R can be used to work with large datasets. Covers Big Data concepts (Volume, Velocity, Variety), an introduction to Hadoop and Spark, and practical R tools for handling and analysing large-scale data.