A reflection on CT

This module on Computational Thinking has helped me to learn the basics of computer science and understand how with only two numbers we have been able to design complex machines and software that are now at the core of our society.

Technology gains more ground every day and has become essential, even mandatory, in situations as quotidian as opening a bank account, applying for a job or accessing a payslip. Those who do not keep up with its constant updates not only risk staying behind in their profession but also in terms of social interaction and access to public and private aid if they ever need it. In this context, I believe that getting to know first-hand the processes and logic that make all this technology possible will be very beneficial for my studies and my future work.

What surprised me the most during this module was the realisation that something as complex and enormous as the Internet still requires a series of tiny steps that start with tasks we would assume are obvious. But they are not. Do you need a headline to be at the top of the page? You have to tell the computer the location you want. Do you want it in the centre? You have to say that as well. Professional looking results can be achieved as long as you detail what you need step by step. Before this, I would have thought that writing a headline consisted of nothing more than writing and that by some force of nature it would end up at the top and in the centre. Where else could a headline go after all?

Decomposing processes that have become natural to us implies reflection, attention to detail and self-awareness. As a student in the Computational and Data Journalism master programme, I believe that developing these skills will be very beneficial when looking for news in big datasets and designing the pathway to work efficiently in projects that will probably involve long hours and several gigabytes.

Training myself in pattern recognition will be useful when detecting trends in a sea of rows and columns while abstraction and generalisation will help me decide what the news is and the relevance it has. The ability to create algorithms will come in handy when choosing the best way to communicate my findings.

But what is more interesting is that each of the steps described involve themselves smaller processes of decomposition, pattern recognition, abstraction, generalisation and algorithms. It is a loop in whose depths you do not necessarily need to dive. However, in case of struggle, it is good to know they are there, available to be explored in your search for solutions.