How to choose the right specialism in the technological world

Since I was young, I knew that I wanted to pursue a career in technology. However, the sector includes many different fields such as software development, artificial intelligence, data science, cyber security and virtual reality, just to name a few. Sometimes, you know which specific area within your programme you are interested in pursuing, but, very often, you might not be totally sure.  

 When I joined the BSc Computer Science offered by the University of London, I was considering Web and Mobile Development as a potential specialisation. However, this programme has six specialisms on offer to choose from. After some time, I realised that I needed to explore further. Here are the three key steps I followed to make my choice: 

1. Do your research

The first step when making a choice is doing research. First, you could read blogs, books and other resources offered by the University of London’s Careers Service. Ask your classmates what they know about the subject. Also, reach out to people in the field and ask them what a day in their job is like, what tasks they do and what skills they need to perform in their role.

From the beginning, I narrowed down my choices to Data Science or Machine Learning and AI (ML/AI); fields that work hand in hand but with significant differences, and two of the most in-demand areas in Computer Science. To broaden my thoughts, I reached out to some of my classmates who were ahead of me, and they kindly shared their thoughts about both fields. In addition, I read about them and watched some videos online. Their advice helped me to understand what these two fields are really about.  

The key objective of Data Science is analysing large volumes of unstructured data to find patterns that help the organisation make better business decisions. In Data Science, you need to be very keen to do statistics and understand that subject-matter expertise in fields other than computer science might be required to get a job in that sector.  

On the other hand, ML/AI focuses on Machine Learning, Deep Learning models and developing the infrastructure to implement them. Likewise, specialists in this area tend to work on commercial software, e.g. web or mobile applications, that implement AI. ML/AI still requires a strong knowledge of mathematics, specifically in areas like calculus or linear algebra. To fully understand how AI works internally, the key goal is using these concepts to develop software that reacts to its data the way an intelligent being would. 

2. Look at what you like and where you can give your best  

Once you know what the different specialisms are about, it is time to reflect on which subjects you are eager to learn more about. In the case of Computer Science, you might have joined the programme because you like programming and want to create innovative pieces of software, or because you see a prosperous future by starting a career in this field. However, it is only through experience that you deeply understand which are your strengths and weaknesses. For example, you might love creating beautiful user interfaces but are not too excited about dealing with backends and databases. Then, User Interface (UI) and User Experience (UX) might be your niche. On the other hand, if you have previously worked in another sector and you would like to keep using what you have already learnt, then Data Science might fit your expectations better.  

 After narrowing down my options, I considered my strengths and interests. A data scientist's work is more about analysing data, writing reports and helping organisations make better decisions using ML models as a tool. A ML engineer focuses on developing new AI algorithms and setting the infrastructure needed. When I decided to pursue a degree in Computer Science, my motivation stemmed from the immense satisfaction I get after witnessing a programme I have created run successfully on a computer, meeting all the requirements and exceeding my expectations. That is why I resonate more with the latter, for my strong interest in technical tasks compared to the non-technical ones. I think that a career in ML/AI would give me more opportunities to do what I enjoy. 

3. Check the employability opportunities 

Researching the possible job posts you could apply for is helpful. The available jobs might change depending on your location. If you are open to working remotely, you will have many more options, but if you prefer to go to the office and don’t want to relocate, understanding your local market is crucial to making the right pick.  

Finally, in terms of employability, a report released by the World Economic Forum in 2023 says that AI and Machine Learning Specialists are the jobs with the biggest growth potential between 2023 and 2027 (1). This sealed the deal for me.

Final thoughts  

A friend once told me that life is long, and that your interests might change a few times during your career. You might choose to start in one field but eventually drift to another. Throughout your professional journey, you will work for various companies and get new skills at each one. Even if you stay at the same company, your role might change over the years. So, choose the specialism that fits you the best for the near future. There are still chances to keep developing yourself after completing your undergraduate degree, like completing certifications or getting a Masters.  

I hope that these tips help you to find your place within the vast field of Computer Science just like they helped me!  

Sebastian studies BSc Computer Science in Venezuela.

References  

1. World Economic Forum. (2023). The Future of Jobs Report 2023: Jobs Outlook.  https://www.weforum.org/publications/the-future-of-jobs-report-2023/