Data analytics is a science that is all about taking raw datasets and translating them into insights that you (or others) can use. Think of it as the conduit between the reams of data an organization collects and the management team. As a data analyst, you’re the person who makes sense of the numbers so management can take action.


At least, that’s how data analytics works in a business context. Switch to the research side of things and you’ll play a crucial role in interpreting the results of complex experiments by helping researchers understand the factors that lead to their results and the effects of changes they make.


Getting your start in this field usually requires you to complete a BSc in computer science with data analytics. This article looks at five of the best options provided by some of the world’s top universities.


Top BSc Degrees in Computer Science With Data Analytics Programs


In creating our list of the five best BSc computer science with data analytics programs, we considered the following criteria:

  • Reputation – A good reputation is like word of mouth for a university. We looked for institutions that have an established track record of quality courses, both in the AI field and outside of it.
  • Curriculum – Many computer science degrees have an analytics component but don’t focus on it as a specialization. The courses we chose put data analytics in the spotlight.
  • Faculty Expertise – Who wants to learn from people who don’t have solid reputations in the data analytics industry? The people who teach you are as important (perhaps even more important) as the content they teach.
  • Industry Connections – A good course is like a tree. The course itself is the trunk, which then branches off into all sorts of industries. You want a course with plenty of branches (i.e., many paths into the industry).
  • Support and Resources – Data analytics isn’t a simple concept that you can pick up with a few hours of study. It’s like a vast ocean and it’s easy to get lost. The right support and resources are like a compass that keeps the student on track.


Top Programs

With the above criteria in mind, we’ve collected five great BSc computer science with data analytics programs for you to consider.


1 – Computer Science With Pathway in Data Analytics (Middle East College)


When universities come together, the result is usually a top-notch degree that allows you to draw from global expertise. That’s what you get with Middle East College’s course, as it’s offered in conjunction with the UK’s Coventry University.


It’s an eight-semester course that focuses on data collection, codification, and treatment, with as much importance placed on practical application as on academic theory. Entry requirements are strict and require:

  • A General Education Certificate (GEC) or similar
  • Either a General Foundation Programme (GFP) certificate or a passing grade in the university-administered MEC placement test
    • Scoring 60% or above in each component of the MEC is a must if you want to use it to replace a GFP.

The big selling point for this course is the link to Coventry University, which has been among the top 15 universities in the UK for over half a decade. That link also creates career opportunities, with the Middle East College faculty exposing you to Asian opportunities while Coventry University can provide a route into the UK for international students.


2 – Bachelor of Science in Data Science and Analytics (St. Ambrose University)


Ranked as the top data analytics program in the world by Bachelor Studies, St. Ambrose’s course is a four-year degree that offers internships to some of the world’s leading companies. This internship program is so extensive that over 75% of the university’s students end up with a work placement that can provide them with a direct route into a career.


As for the course itself, you’ll develop foundational knowledge in statistics and computing before moving on to practical ways to apply that knowledge. The course also has an ethical component, which is crucial given the potentially controversial means some companies use to collect data.


International students need to achieve the equivalent of an American 2.5 out of 4.0 Grade Point Average (GPA), making this one of the easier courses to get onto. You also have to complete a Declaration of Finances form (available via the university’s website) to demonstrate proof of funding for your studies.


3 – BSc Digital Business & Data Science (University of Applied Sciences Europe)


The Hamburg-based University of Applied Sciences Europe is among the top 25 private universities in the continent and it’s a popular choice for international students. Its BSc computer science with data analytics program is interesting because it combines the fundamentals of data science with business concepts. Beyond learning advanced programming and analytics concepts, you’ll discover how those concepts apply in fields as varied as economics and cybersecurity. Throw in some marketing and entrepreneurship modules and this is an excellent choice for the prospective start-up owner.


Entry requirements are fairly simple. You’ll need proof of a high school diploma (or your country’s equivalent), which you submit alongside a CV and demonstration of English-language proficiency. A passing grade in an IELTS or TOEFL exam should do the job for the latter requirement.


Non-EU students have an extra hurdle to jump – a tuition deposit. You have to pay €3,000 upfront, which serves as a reservation fee for the course. The good news is that this fee counts toward your full tuition, so it’s deducted from the total. Think of it as paying money upfront for a restaurant reservation, with that money going toward the final bill.


4 – Data Science BSc (Warwick University)


Ranked as the 10th-best university in the UK and in the top 100 in the world, Warwick University is a good performer in terms of pure credentials. But the school’s state-of-the-art statistics department makes it stand out, with its research department being touted as “world-leading.”


Its Data Science BSc takes in plenty of the skills you’ll use in data analytics, including how to parse through massive datasets to get to crucial information. The scope of this work is particularly impressive, with the course teaching how data analytics applies in industries as varied as finance and social networks. Studying (and even working) abroad is also offered to those who want to build their networks through their studies.


Entry requirements are stringent, with students generally expected to have at least two (and usually three) A* A-Level grades, or equivalents, to get in. The university’s website digs into more specific requirements for international students. This is an English-language course, too, so you’ll need proof of your English-speaking abilities or have to pass the university’s Pre-Sessional English Course before you’re considered for entry.


5 – BSc in Data Science and Analytics (National University of Singapore)


Ranked as the 11th best university in the world by QS University Rankings, the National University of Singapore is a trailblazer in the data analytics field. To get in, you’ll need to show the equivalent of an H2 pass in mathematics or further mathematics, which is roughly equivalent to an A grade at A-Level in the UK.


The course itself is a four-year honors program that starts by teaching you the foundational analytical methods applied in data science. From there, it branches into teaching how these concepts apply in real-world scenarios before introducing you to tools and techniques you’ll use in practical work.


Experiential learning is key to the course, with the National University of Singapore calling it “industry-driven” to highlight that this is a course that teaches you how to drive the car, as well as showing you what lies under the hood. To support this approach, the university runs its “Co-operative Education Programme” which combines academic study with several internships over four years of study.

Benefits of Pursuing a BSc in Computer Science With Data Analytics


By now, you’re probably asking yourself a big question: “Why should I study a BSc in computer science with data analytics?


Reason 1 – Develop In-Depth Knowledge


A data analytics bachelor’s degree teaches you how to use the tools and techniques needed in the field. But the theory that underpins those tools, along with the programming languages you’ll use, is near-universal in terms of its usefulness. As a result, following this degree track opens up career opportunities that extend into the software programming and computing fields, as well as analytics.


Reason 2 – Enhanced Employability


Building on the previous point, the skills you develop as part of a BSc in computer science with data analytics will make you seem like the goose that lays the golden eggs to employers. You’ll have such a varied skillset that you can lend your hand to almost anything in the computing sector. Salaries are solid, too, with data analysts earning an average of €55,000 per year in Germany alone.


Reason 3 – Opportunities for Further Education


If a data analytics BSc is the equivalent of drawing up a blueprint for a house, later educational pursuits are all about building that house into something special. These courses lay the groundwork for later education (such as OPIT’s Master in Applied Data Science and AI), in addition to making it easier for you to earn professional certifications that look great on your CV.


Tips for Choosing the Right BSc Computer Science With Data Analytics Program


Right now, you’re at a crossroads that seems to branch off into an infinite number of paths. There are so many data analytics courses to choose from that it’s hard to know which way to turn. Use these tips to ensure you pick the right one:

  • Align your course selection with your career goals – if it doesn’t take you closer to where you want to be then it’s not the course for you.
  • Dig deeper into what each course offers by comparing curricula to see which courses have gaps and which cover everything you want to learn.
  • Location and general student life are important because you need to have a life outside of education, so pay attention to both.
  • The cost of tuition can often be like a brick wall to students, but research into financial aid often helps you to find the ladder that gets you over that wall.
  • If you have the opportunity, speak to faculty and alumni to discover what makes the course so special.

Keep Exploring to Find the Right Course for You

The five programs covered here are among the best BSc computer science with data analytics courses in the world, but that doesn’t necessarily mean they’re right for you. Exploration is key, as you must transform into an explorer to navigate your way toward the course that fits your needs from career, life, and passion perspectives. Make the right choices, and you’ll put yourself on course for a data-driven career that’s rewarding on both the mental and financial levels.

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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.”

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Reuters: EFG Watch: DeepSeek poses deep questions about how AI will develop
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  • Reuters, Published on February 10th, 2025.

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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
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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.

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