The AI+ Data™ certification is designed to help you master the key skills needed in data science and artificial intelligence for modern business and analytics roles. The course covers foundational topics such as data science principles, statistics, data wrangling, and programming (Python and R), along with advanced subjects like generative AI, machine learning, and predictive analytics. You also work on a capstone project to solve real‑world problems, giving you practical experience. This certification prepares you for data‑focused careers such as data analyst, data scientist, AI consultant, and machine learning engineer, as it teaches you how to transform raw data into actionable insights that support business decisions. 

Our organization proposes the “AI for Data Professional Program” to equip data specialists with advanced AI skills for analytics, automation, and intelligent decision-making. Malaysia’s data-driven economy is growing rapidly, with AI playing a critical role in predictive modeling, big data processing, and business intelligence across sectors such as finance, manufacturing, and healthcare. However, many data professionals lack the expertise to integrate AI tools and frameworks effectively, limiting innovation and scalability. This program will empower Bumiputera talent to apply AI in data engineering, visualization, and machine learning workflows, enabling them to deliver high-value insights and solutions. By bridging this skills gap, participants will be positioned for high-demand roles in Malaysia’s digital economy, supporting national goals under the Malaysia Digital Economy Blueprint.

Full Time

Participantsshould understand data analytics concepts and be keen to apply AI for advanced data insights, predictive modeling, and decision-making.

5 Days

40 Hours

Module 1: Foundations of Data Science

1. Introduction to Data Science
2. Data Science Life Cycle
3. Applications of Data Science

Module 2: Foundations of Statistics

1. Basic Concepts of Statistics
2. Probability Theory
3. Statistical Inference

Module 3: Data Sources and Types

1. Types of Data
2. Data Sources
3. Data Storage Technologies

Module 4: Programming Skills for Data Science

1. Introduction to Python for Data Science
2. Introduction to R for Data Science
3. Module 5: Data Wrangling and Preprocessing
4. Data Imputation Techniques
5. Handling Outliers and Data Transformation

Module 6: Exploratory Data Analysis (EDA)

1. Introduction to EDA
2. Data Visualization

Module 7: Generative AI Tools for Deriving Insights

1. Introduction to Generative AI Tools
2. Applications of Generative AI

Module 8: Machine Learning

1. Introduction to Supervised Learning Algorithms
2. Introduction to Unsupervised Learning
3. Different Algorithms for Clustering
4. Association Rule Learning with Implementation

Module 9: Advanced Machine Learning

1. Ensemble Learning Techniques
2. Dimensionality Reduction
3. Advanced Optimization Techniques

Module 10: Data-Driven Decision-Making

1. Introduction to Data-Driven Decision Making
2. Open Source Tools for Data-Driven Decision Making
3. Deriving Data-Driven Insights from Sales Dataset

Module 11: Data Storytelling

1. Understanding the Power of Data Storytelling
2. Identifying Use Cases and Business Relevance
3. Crafting Compelling Narratives
4. Visualizing Data for Impact

Module 12: Capstone Project - Employee Attrition Prediction

1. Project Introduction and Problem Statement
2. Data Collection and Preparation
3. Data Analysis and Modeling
4. Data Storytelling and Presentation

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