Data Science & Machine Learning
Extract meaning from data and build models that drive decisions. Over 11 months, you will develop the analytical thinking, statistical knowledge, and technical skills that define effective data scientists in today's market.
Program Overview
Every industry in Malaysia is generating more data than it knows what to do with. The gap is not in data collection but in the ability to interpret it. Companies need people who can clean messy datasets, uncover patterns, communicate findings to non-technical stakeholders, and build predictive models that improve business outcomes. This program trains you to be that person.
You will begin with the mathematical and statistical thinking that underpins all data science work. From there, you will learn to manipulate and visualise data using Python and industry tools like Tableau. The program then advances into machine learning, where you will implement algorithms from scratch before using libraries like scikit-learn to build scalable solutions. The final months are dedicated to a capstone project where you tackle a real-world dataset and present your findings as a professional data scientist would.
Curriculum Highlights
- Statistics Foundations — Descriptive and inferential statistics, probability distributions, hypothesis testing, confidence intervals, and Bayesian thinking. The mathematical bedrock that separates data scientists from data enthusiasts.
- Python for Data Science — Python syntax, data types, functions, and scripting with a focus on analytical workflows. Jupyter notebooks as your primary working environment.
- Pandas & NumPy — Data ingestion, cleaning, transformation, and aggregation with pandas. Numerical computing and array operations with NumPy. Handling real-world data quality issues.
- Data Visualisation — Creating compelling charts and dashboards with matplotlib, seaborn, and Plotly in Python. Building interactive business dashboards in Tableau for stakeholder communication.
- SQL & Database Fundamentals — Querying relational databases, writing complex joins, subqueries, and window functions. Understanding data warehousing concepts and connecting Python to databases.
- Machine Learning Algorithms — Linear and logistic regression, decision trees, random forests, support vector machines, k-means clustering, and ensemble methods. Understanding when to apply each algorithm and why.
- Feature Engineering — Transforming raw data into meaningful features that improve model performance. Handling categorical variables, missing data, outliers, and feature selection techniques.
- Model Evaluation & Tuning — Cross-validation, hyperparameter tuning, bias-variance tradeoff, ROC curves, precision-recall analysis, and A/B testing frameworks.
- Model Deployment — Packaging models as APIs with Flask, containerisation with Docker, deploying to cloud platforms, monitoring model performance in production, and handling model drift.
- Capstone Project — A comprehensive data science project using a real-world dataset. You will define the problem, explore and clean the data, build and evaluate models, and present your findings in a professional report and live presentation.
Who This Program Is For
Analysts Ready to Level Up
You currently work with spreadsheets, SQL, or basic reporting tools and want to move into predictive analytics and machine learning. You understand data but want the programming and statistical skills to do more with it.
Career Switchers With Quantitative Backgrounds
You studied engineering, mathematics, economics, or a science discipline and want to apply your analytical mindset to a data science career. Your quantitative foundation gives you an advantage in grasping statistical concepts quickly.
Business Professionals Seeking Data Literacy
You make decisions that affect your organisation and want to base those decisions on data rather than intuition. Even if you do not pursue a full data science role, this program gives you the ability to work effectively with data teams and evaluate analytical outputs critically.
Frequently Asked Questions
How much maths do I need to know before starting?
A comfort level with basic algebra and an openness to learning statistics is sufficient. We teach the statistical concepts you need from the ground up, with clear explanations and practical examples. You do not need calculus or linear algebra to begin, though familiarity with either will help you in the machine learning modules. We also provide optional supplementary maths workshops for students who want extra support.
What tools and software will I use?
You will work primarily with Python in Jupyter notebooks, using libraries including pandas, NumPy, scikit-learn, matplotlib, seaborn, and Plotly. For business intelligence and dashboard creation, you will learn Tableau. We also cover SQL for database querying, Git for version control, and Docker for deployment. All software licences are included in your enrolment.
What does the capstone project involve?
In the final two months, you will work on a substantial data science project from end to end. You choose a dataset and problem statement with guidance from your instructor, then work through data exploration, cleaning, feature engineering, modelling, and evaluation. The project culminates in a written report and a live presentation to a panel that includes instructors and industry guests. This becomes the centrepiece of your professional portfolio.
Turn Data Into Your Career Advantage
Curious about whether data science is the right path for you? Our admissions team can discuss your background, goals, and the specifics of the program. No commitment required.
Get in Touch →