What you study typically dictates your future career. Picking an academic subject is a decision that pairs your passion with practicality, particularly in the computer science and data science fields.

If you’re at a crossroads between choosing one or the other, think about which path aligns with your interests and gives you the best chance of building a bright digital future.

Understanding the Core of Computer Science

Computer science is the backbone of technology. This field prepares you for understanding how software and systems work. It teaches the basics of coding and the complexities of algorithms and network security, all within the same field.

It’s a broad discipline with a knack for problem-solving and innovative thinking. When you master it, you might be crafting the next big app or securing cyber spaces for major companies.

Diving Into Data Science

In contrast, data science zooms in more closely on the digital age’s most precious resource: data. A degree in data science equips you with the knowledge to sift through mountains of information and extract insights that can be used in various industries. For example, it could help healthcare professionals uncover patterns in patient care, sports agents devise new strategies based on big data, or businesses to plan out a targeted marketing campaign.

Data science is about pattern recognition, predictive modeling, and telling stories through data visualization. It’s where statistics meet strategy and empower those in decision-making positions with actionable intelligence.

Comparing Curriculums: Computer Science vs Data Science Degree

Both degrees share a foundation in math and analytical thinking in terms of curricula. Regardless, they have distinct differences:

  • Computer Science students are immersed in programming languages, software engineering principles, and computing theory. Their tasks consist primarily of building, designing, and optimizing systems.
  • Data Science coursework, on the other hand, mixes together statistics, machine learning, data visualization, and ethical considerations in data handling. It focuses on the lifecycle of data analysis, from collection to communication.
  • Each curriculum imparts the basic and advanced technical skills and fosters critical thinking. Once they graduate from either course, graduates will have the means to handle complex problems with creative solutions.

Career Trajectories: Data Science Degree vs Computer Science

Graduates from both fields are in high demand, but the roads they travel can look quite different.

  • Computer Science aficionados might be developing software, protecting users against cyber threats, patching and upgrading existing systems, or designing new computing hardware.
  • Data Science experts are likely to take on roles like data analysis, predictive modeling, or AI and machine learning engineering.

Fortunately, neither choice will leave you wanting in terms of salary. The sectors are thirsty for the talent and prepared to pay well for the best talent. The salary shouldn’t affect your choice, but whether your passion lies in creation versus analysis.

For instance, in Germany, you’re looking at an average salary of about €50,000 ($54,635) as of 2024. When you compare these numbers to the tech field in the U.S., salaries in countries like the U.K., Poland, France, Germany, and Spain range from 34% to 63% of what their counterparts make in the U.S. If you’re in the tech industry in Europe, what you take home can vary quite a bit depending on where you are.

In the U.K., the average salary for data scientists as of 2024 is around $67,254 per year, with potential additional compensation bringing it up to $79,978. Meanwhile, in Germany, the median salary for a data scientist is just slightly less, around €66,000 ($72,111) per year.

Educational Prerequisites and Learning Outcomes

Before enrolling into either of these fields, you must have a solid base in mathematics and a talent for problem-solving. More specifically, computer science aspirants should get ready for high-level programming, so basic familiarity with programming logic, languages (any would help), and algorithms will do wonders. Just as importantly, you should also have a strong grip on logical reasoning.

Furthermore, data science enthusiasts will need to have a solid understanding of statistics and a knack for critical thinking. Graduates from both fields emerge as tech-savvy professionals who can tackle tomorrow’s challenges with a deep understanding of tech nuances.

OPIT’s Approach to Technology Education

OPIT is at the heart of technology education. The service offers MSc in Applied Data Science and AI and BSc in Modern Computer Science. Both programs have the future in mind, yours and of that of the tech industry as a whole. The programs mix theoretical knowledge with hands-on experience to meet the demands of the job market.

They diverge in focus but converge in aim: to forge skilled professionals ready to make an impact. Best of all, the programs set themselves apart from the traditional classroom education with personalized study that you can do at your own time, without constrictive exams. Instead, the programs focus on continuous learning.

Making Your Decision: Factors to Consider

Now, while you might have a better understanding of what each field represents, there’s a lot more to it. The choice between data science and computer science hinges on a few factors:

  • Decide if you are more interested by the prospect of developing software or deciphering data patterns.
  • Think about where you see yourself in the tech industry and the type of projects that excite you.
  • Keep an eye on the future, understand which skills are likely to remain in high demand, and whether they suit you.
  • These considerations can put you on track for a degree that fuels your passion and boosts your career prospects.

Two Options, One Choice

Data science and computer science degrees are both lucrative, in demand, creative, and engaging careers. More than simply academic choices, they will determine what professions you can enter and your future opportunities. Ultimately, your interests, skills, and strengths should decide which path you take. Both pay well and both reward hard work, so choose wisely. Either way, the possibilities are vast and continue to grow by the day.

Related posts

Sage: The ethics of AI: how to ensure your firm is fair and transparent
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Mar 7, 2025 3 min read

Source:


By Chris Torney

Artificial intelligence (AI) and machine learning have the potential to offer significant benefits and opportunities to businesses, from greater efficiency and productivity to transformational insights into customer behaviour and business performance. But it is vital that firms take into account a number of ethical considerations when incorporating this technology into their business operations. 

The adoption of AI is still in its infancy and, in many countries, there are few clear rules governing how companies should utilise the technology. However, experts say that firms of all sizes, from small and medium-sized businesses (SMBs) to international corporations, need to ensure their implementation of AI-based solutions is as fair and transparent as possible. Failure to do so can harm relationships with customers and employees, and risks causing serious reputational damage as well as loss of trust.

What are the main ethical considerations around AI?

According to Pierluigi Casale, professor in AI at the Open Institute of Technology, the adoption of AI brings serious ethical considerations that have the potential to affect employees, customers and suppliers. “Fairness, transparency, privacy, accountability, and workforce impact are at the core of these challenges,” Casale explains. “Bias remains one of AI’s biggest risks: models trained on historical data can reinforce discrimination, and this can influence hiring, lending and decision-making.”

Part of the problem, he adds, is that many AI systems operate as ‘black boxes’, which makes their decision-making process hard to understand or interpret. “Without clear explanations, customers may struggle to trust AI-driven services; for example, employees may feel unfairly assessed when AI is used for performance reviews.”

Casale points out that data privacy is another major concern. “AI relies on vast datasets, increasing the risk of breaches or misuse,” he says. “All companies operating in Europe must comply with regulations such as GDPR and the AI Act, ensuring responsible data handling to protect customers and employees.”

A third significant ethical consideration is the potential impact of AI and automation on current workforces. Businesses may need to think about their responsibilities in terms of employees who are displaced by technology, for example by introducing training programmes that will help them make the transition into new roles.

Olivia Gambelin, an AI ethicist and the founder of advisory network Ethical Intelligence, says the AI-related ethical considerations are likely to be specific to each business and the way it plans to use the technology. “It really does depend on the context,” she explains. “You’re not going to find a magical checklist of five things to consider on Google: you actually have to do the work, to understand what you are building.”

This means business leaders need to work out how their organisation’s use of AI is going to impact the people – the customers and employees – that come into contact with it, Gambelin says. “Being an AI-enabled company means nothing if your employees are unhappy and fearful of their jobs, and being an AI-enabled service provider means nothing if it’s not actually connecting with your customers.”

Read the full article below:

Read the article
Reuters: EFG Watch: DeepSeek poses deep questions about how AI will develop
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Feb 10, 2025 4 min read

Source:

  • Reuters, Published on February 10th, 2025.

By Mike Scott

Summary

  • DeepSeek challenges assumptions about AI market and raises new ESG and investment risks
  • Efficiency gains significant – similar results being achieved with less computing power
  • Disruption fuels doubts over Big Tech’s long-term AI leadership and market valuations
  • China’s lean AI model also casts doubt on costly U.S.-backed Stargate project
  • Analysts see DeepSeek as a counter to U.S. tariffs, intensifying geopolitical tensions

February 10 – The launch by Chinese company DeepSeek, opens new tab of its R1 reasoning model last month caused chaos in U.S. markets. At the same time, it shone a spotlight on a host of new risks and challenged market assumptions about how AI will develop.

The shock has since been overshadowed by President Trump’s tariff wars, opens new tab, but DeepSeek is set to have lasting and significant implications, observers say. It is also a timely reminder of why companies and investors need to consider ESG risks, and other factors such as geopolitics, in their investment strategies.

“The DeepSeek saga is a fascinating inflection point in AI’s trajectory, raising ESG questions that extend beyond energy and market concentration,” Peter Huang, co-founder of Openware AI, said in an emailed response to questions.

DeepSeek put the cat among the pigeons by announcing that it had developed its model for around $6 million, a thousandth of the cost of some other AI models, while also using far fewer chips and much less energy.

Camden Woollven, group head of AI product marketing at IT governance and compliance group GRC International, said in an email that “smaller companies and developers who couldn’t compete before can now get in the game …. It’s like we’re seeing a democratisation of AI development. And the efficiency gains are significant as they’re achieving similar results with much less computing power, which has huge implications for both costs and environmental impact.”

The impact on AI stocks and companies associated with the sector was severe. Chipmaker Nvidia lost almost $600 billion in market capitalisation after the DeepSeek announcement on fears that demand for its chips would be lower, but there was also a 20-30% drop in some energy stocks, said Stephen Deadman, UK associate partner at consultancy Sia.

As Reuters reported, power producers were among the biggest winners in the S&P 500 last year, buoyed by expectations of ballooning demand from data centres to scale artificial intelligence technologies, yet they saw the biggest-ever one-day drops after the DeepSeek announcement.

One reason for the massive sell-off was the timing – no-one was expecting such a breakthrough, nor for it to come from China. But DeepSeek also upended the prevailing narrative of how AI would develop, and who the winners would be.

Tom Vazdar, professor of cybersecurity and AI at Open Institute of Technology (OPIT), pointed out in an email that it called into question the premise behind the Stargate Project,, opens new tab a $500 billion joint venture by OpenAI, SoftBank and Oracle to build AI infrastructure in the U.S., which was announced with great fanfare by Donald Trump just days before DeepSeek’s announcement.

“Stargate has been premised on the notion that breakthroughs in AI require massive compute and expensive, proprietary infrastructure,” Vazdar said in an email.

There are also dangers in markets being dominated by such a small group of tech companies. As Abbie Llewellyn-Waters, Investment manager at Jupiter Asset Management, pointed out in a research note, the “Magnificent Seven” tech stocks had accounted for nearly 60% of the index’s gains over the previous two years. The group of mega-caps comprised more than a third of the S&P 500’s total value in December 2024.

Read the full article below:

Read the article