By: Ts. Dr. Faridah Hani Mohamed Salleh

In the fast-moving world of computer science, there’s a subject that has caught the interest of curriculum developers, educators, and students alike. Data Science, a cutting-edge field that enables the exploration of concealed insights within vast amounts of information, has gained immense popularity. However, this popularity has led to many questions that need to be answered. Drawing from my extensive teaching experience and involvement in research projects, I delve into the pressing concerns surrounding Data Science. The first and foremost question: Does Data Science exclusively cater to students who have reached a certain level of maturity? Defining maturity in an academic context, I suggest that students in at least their second year of a degree program possess the necessary foundation. However, another question arises: Should Diploma students be introduced to this fascinating domain? It’s crucial to consider these questions to ensure students don’t miss out on the benefits of Data Science due to untimely exposure. In my personal experience, I have found that the subject of Data Science can also be taught at the Diploma level, provided that the lecturer is skilled in selecting easily understandable domains. Domains like insurance or finance, which are foreign to most students, should be avoided. Lecturers should avoid selecting domains that may be comfortable for them and commonly used in reference books but may not be suitable for students, especially those in the early stages of their studies.

What about the opinion on the need for a strong foundation in Mathematics as a requirement? Is it critical to the point of hindering the learning process? If most people assume that a strong foundation in Mathematics may hinder students’ understanding of Data Science, I have a different opinion. In my view, the level of computer usage competency is actually more important. It may seem trivial, but this is an issue that I often face when conducting practical sessions with students. Some students encounter problems with installing tools, their computers not meeting the minimum performance requirements for data processing, and various issues related to their personal laptops. These issues could be resolved more quickly if students have a better understanding of technical computer knowledge. There are alternative options such as Google Collab or other cloud-based tools that can reduce dependence on personal or lab computers. However, the experience is not the same as downloading the tools onto a personal computer.

Regarding Mathematics, I propose an engaging approach to introducing algorithms without delving into their intricate inner workings. Instead, the focus should shift to the practical application of Mathematics in analyzing selected data. Basic mathematical knowledge suffices, as long as instructors master the art of storytelling. Using predicting house price as an example, through vivid narratives, students grasp how various data variables such as crime rate indexes, interest rates, sale prices of similar homes, time on the market, and comparable properties contribute to predicting house prices. Instructors can ignite logical thinking by emphasizing examples of negative correlation, such as how declining crime rates correspond to rising home prices, without delving into detailed formula derivations.

The next issue that often concerns both students and instructors is whether a student’s programming competency level truly affects their level of understanding. The answer is yes, especially when it comes to students completing projects. This subject usually requires students to complete projects, and the data sets used in class are usually different from the data sets students use for their own projects. Students need to have at least basic programming knowledge, as the basic code provided by instructors in the lab needs to be modified to meet the requirements of their projects.

In conclusion, many believe that Data Science subject requires extensive pre-existing knowledge for someone to learn it. However, the reality is different because the success rate of this subject largely depends on the creativity of the instructors. Finally, remember that every teaching point in this subject should be explained to the students why it is important. Never skip the reasoning, even if it may take time, as it is the foundation of Data Science. As we move forward into a data-driven future, the undeniable fascination of Data Science becomes increasingly apparent. By addressing important questions and fully embracing its potential in education, we can inspire more individuals to acquire knowledge in Data Science. This will equip our future generation with valuable expertise to confront the complexities of the modern world.