More students are eyeing data science roles than ever before, and employers are listening. All you have to do is look at the recent report by the World Economic Forum, which highlighted that data analysis was one of the most in-demand digital skills by today’s employers.
Contrary to what you might assume, companies outside the tech space are also looking for individuals with data analysis skills. Healthcare providers, banks, and even supermarkets are all attempting to recruit individuals who have the capabilities to make sense of the information collected and turn it into decisions.
Knowing there is work for those with data analysis skills and a chance to develop a long and fulfilling career, many will understandably ask: how do you actually get there?
The route can look a little different for everyone. A common starting point for most is taking the time to study computer science. Why? Although a job title says, “data analysis” or “machine learning engineer”, the foundations come from core computer science principles: logic, coding and working with data structures.
And so, as you begin to map out a career path in data, don’t forget to look closely at what a computer science degree offers. It’s a good place to begin, and here’s why:
What Studying Computer Science Really Teaches You
Yes, you will likely learn how to code by taking a computer science degree. However, there is so much more the course teaches you; it helps shape the way you think. First-year students begin by learning how to break problems into logical steps. They design efficient solutions and write clean coding despite being under pressure.
Most of the coursework they receive covers programming languages, including C++, Java, or Python. They might also receive work related to algorithms, data structures, and system designs. Of course, database work is often included as well, helping to lay a solid foundation for those considering data-related roles once they graduate.
Fear not- there is a practical side to studying computer science. For instance, there are opportunities to work in groups, which can mimic workplace collaboration, enabling you to build your skills working with others. Along with group work, these courses often have individual assessments to test your ability to deliver under deadlines.
You might also find that many universities push for hands-on learning, encouraging students to build apps, run experiments, and debug real software. This mix of theory and application makes computer science degrees highly adaptable across tech fields.
What are the advantages of all this? Once graduation comes around, aside from writing code, you will also be learning how to build things that work, ways to fix them when they break, and think like an engineer.
Why So Many Data Scientists Start With a CS Background
It’s not a coincidence that most data scientists began taking a computer science course. There is a deep crossover between the two disciplines. How so? Well, if you can understand algorithms and logic, picking up data processing or machine learning workflows becomes more straightforward.
For instance, a CS background often includes maths-heavy content. As such, topics like linear algebra and statistics are essential in computer and data science. If students are trained in coding, they already have the tools to work with data libraries and frameworks. Knowing how to build systems means they can handle the infrastructure side of analytics, too: databases, pipelines, and deployment.
And let’s be honest: Python is everywhere. So, if you’ve completed a few computer science modules, it’s likely that you’ve also probably written scripts in it. That head start gives CS grads an edge when stepping into roles where data wrangling and automation are daily tasks.
What sets them apart? Technical skills do help, but structured thinking is the secret. This skill is perfect for analysing datasets, spotting patterns, and testing hypotheses.
What to Look For When Picking a Degree That Sets You Up for Data
The question now is: how do I pick the right course? As you compare universities and courses, one thing you will quickly spot is that not all computer science degrees are designed the same. You might find that one has a heavy focus on the theoretical side, whilst another prioritises practical skills or emerging fields like machine learning. If you’re hoping to move into data science, the details of the course structure matter, and so you have to pick wisely.
A great place to start is checking if the programme offers modules in areas like statistics, data analysis, or artificial intelligence. Fortunately, many universities allow optional modules. What’s beneficial about these modules is they provide you with early exposure to the topics you would likely encounter in a data-driven job. And so, it helps prepare you for when you enter the industry and seek a job.
Another factor is whether the course offers placement years, industry projects, or lab-based learning. These are ideal if you hope to apply your skills in real-world settings before graduating.
Don’t forget about research support and resources, too. Does the university have links with tech firms or research groups in AI and data science? Are there active societies or hackathons where you can experiment with projects outside of lectures?
Exploring different options can be a game-changer when trying to identify the best university to study Computer Science, particularly if your end goal is a role in data science. The right programme will do more than just tick boxes—it’ll challenge you, stretch your thinking, and give you tools that matter in practice.
Thinking Beyond Graduation: What Comes Next?
This title sums up what many students think when graduation is around the corner: “What’s next for me?” Firstly, congratulations are in order when finishing a degree. After all, this is a significant accomplishment!”. However, it is the first step in what is likely a long journey. What comes after depends on where you want to go, and how well you’ve prepared while studying.
Graduating from university, and immediately finding themselves in a full-time role is a common route for many students to graduate from university. If this is a route you consider, one thing you will spot is that junior data analyst and entry-level developer jobs often list a computer science degree as a preferred requirement.
If diving straight into the world of work is not for you, you might find yourself following the same path as other students: pursuing further study. For example, a master’s in data science can help you build on what you have already learned and open doors to more specialised roles.
Then there are short, intensive paths like coding bootcamps or online certifications. These are popular with graduates looking to sharpen specific skills, say, advanced machine learning or cloud computing, without committing to another multi-year course.
No matter the route, it’s about continuing to learn, staying adaptable, and building a profile that shows more than grades.
From Python Projects to Real Paycheques
Nothing can prepare you for the transition from academic learning to full-time employment. After years in education, moving from lecture halls to workplaces can feel like a big leap. However, when your degree has laid a bit of the groundwork, you might find this transition a natural step in your journey. As you look for jobs and are invited for interviews, you will notice that employers want people who can think critically, write clean code, and communicate findings clearly. And so, having a strong computer science background supports all of that.
In addition to what skills a candidate possesses, employers also look for potential. Yes, experience does help, and you will often find that’s what a few employers look for in candidates. Naturally, this can be disheartening. Thankfully, there are employers that do value potential. And so, if you have worked on relevant personal projects, contributed to open-source code, or completed a short data science course alongside your degree, it all counts. Hiring managers know that the best candidates are those who keep learning, even outside formal education.
Ready to Start Your Journey?
Choosing what to study is one of the biggest decisions you’ll make early in your career. If you’re aiming for a future in data science, starting with a computer science degree gives you an edge. During your time studying, you will gain technical confidence, learn how systems work behind the scenes, and build the kind of mindset that handles complexity with ease.
But it’s not just about getting into university. It’s about what you do once you’re there. Look for chances to expand beyond your course: join coding challenges, try freelance projects, and contribute to student tech communities. All of these experiences, regardless of how small, can sometimes end up being just as important as your final grade.
Data science isn’t a shortcut or a trend. It’s a career built on curiosity, consistency, and skill. And it starts with building the right base. Are you thinking about where to begin? If so, take the time to find a course that challenges you, supports your goals, and offers the space to grow. Yes, it might seem like a lot of effort, but what you put in now will shape where you end up later.
