No, you don’t need any prior coding experience. The programme is designed to support beginners, introducing programming fundamentals gradually in the first year. This step-by-step approach helps students build confidence, develop essential skills, and keep up with their peers, even if they are starting from scratch.

You can specialise in Data Science, Artificial Intelligence, Cybersecurity, or Mobile Computing. You select your specialisation in the first year, which forms the core focus of your studies. In addition to your specialisation, you can choose one complementary study pathway to broaden your learning. This can be an extension of another specialisation, a second major, a minor, or electives. You can also participate in a student exchange programme, which provides an additional opportunity to study at an approved partner university and gain international experience, subject to academic approval and module mapping.

In the first year, all students learn Java through Object Oriented Programming and C++ through Data Structures and Algorithms, providing a strong foundation in coding and problem-solving.

After the first year, the programming languages you study depend on your specialisation:

• Data Science and Artificial Intelligence: Python, used for data analytics and machine learning projects.
• Mobile Computing: Java or Kotlin, used for building Android applications in Android Studio.
• Cybersecurity: Python or C++, used for programming tasks in security applications.

In my first year, we were introduced to Apache NetBeans, an integrated development environment (IDE) for learning programming basics. Over time, I’ve developed my own workflow and now primarily use Visual Studio Code and Google Colab for most of my coursework.

I use GitHub to manage my code when working with Git repositories, although it’s only required in certain modules or when specified by lecturers. For cybersecurity coursework, I also work with virtual machines and Kali Linux, which are commonly used for practical security exercises.

In the final year, students complete a capstone project that applies the skills and knowledge they’ve gained throughout the programme. Projects focus on real‑world challenges, and students may collaborate with lecturers, postgraduate students, or external organisations—for example, companies such as PETRONAS. This experience helps students develop skills in project development and management, as well as teamwork.

There are plenty of opportunities for students to apply their computing skills outside the classroom! Clubs such as Agents of Tech organise workshops, activities, and projects that allow students to put theory into practice. They can also take part in hackathons, coding competitions, and industry challenges, collaborating in teams to solve real-world problems. Additionally, networking events and industry seminars offer exposure to professionals and insights into best practices and emerging technologies.

I made the most of the opportunities available during my studies by actively participating in clubs and workshops, allowing me to develop skills, apply knowledge, and connect with others. In my first year, I approached these experiences as learning opportunities, focusing on exploration and experimentation. In my second and third years, I expanded into competitions and projects, which led to achievements such as winning a hackathon, a research-based competition, and a robotics competition.

Students gain industry exposure in a variety of ways throughout their studies. Many clubs at Taylor’s maintain links with external companies, organisations, and professionals, creating opportunities for students to engage with people from different fields. For example, the Toastmasters Club regularly hosts industry speakers and practitioners at its biweekly meetings.

The university also organises guest talks, applied workshops, and career fairs each semester, offering students insight into workplace expectations, emerging trends, and potential career pathways. While career fairs primarily support students seeking internships and jobs, they are open to students at all stages of study.

Use AI as a learning partner. When coding or tackling programming problems, let it guide you—offering suggestions, clarifying concepts, or reviewing your logic—but don’t follow it blindly. Take the time to understand why something works. Experiment, test, and implement solutions yourself. Treat AI as a tool to strengthen your skills and reinforce problem-solving, rather than as a ready-made answer.

I’d tell myself that it’s okay to start slow, as everyone learns at their own pace, and comparing yourself to others only adds pressure. Begin with coding exercises, practice projects, or simple apps, and gradually take on more complex tasks and assignments as your confidence grows. Most importantly, explore opportunities beyond the curriculum, such as personal coding projects, open-source contributions, or small research experiments, driven by genuine curiosity and interest in computing, not just to make your portfolio look impressive.