By 2025 the global data volume will approach 175 zettabytes. Store that information on DVDs and the stack would reach the moon 23 times over. That sheer volume of data means that professionals are required to make sense of the information.

Sectors like finance, healthcare, manufacturing, and telecoms use vast amounts of data and present attractive career opportunities. Choosing the best degree for data science can open up new doors for those interested in playing a leading role in the lucrative field of data science.

Understanding Data Science and Its Educational Pathways

Data science has always been important. Businesses have been leveraging the power of data ever since the term was invented, but the data landscape is changing.

Today, data science combines math and statistics, advanced analytics, specialized programming, Artificial Intelligence (AI), and machine learning to provide actionable strategic insights to organizations.

Aspirant professionals interested in playing a data-centric role in the success of an organization need appropriate, respected, and relevant qualifications, and finding the best degree for data science is the first step on the road to success.

That road offers many routes toward success in the individual’s chosen field.

Some data science career trajectories include:

  • Data scientist
  • Data analyst
  • Business analyst
  • Business intelligence analyst

Mid-Level

  • Data architect
  • Data engineer
  • Senior business analyst

Senior-Level

  • Lead data scientist
  • Director of data science
  • Vice president of data science
  • Chief information officer
  • Chief operations officer

What to Look For in a Data Science Degree

A firm foundation is essential for a rewarding career in data science, and that foundation must include a recognized undergraduate degree. The best degree for data science will be obtained from an accredited and well-respected education institution. It will provide foundational skills in areas such as data analysis, machine learning, big data, and statistical analysis (among others).

However, the structure of the coursework is important. An undergraduate degree in data science should:

  • Provide a solid understanding of principles and theory
  • Offer practical experience based on real-world immersion
  • Give opportunities for specialization

In addition, a world-class program will emphasize teamwork, innovation and effective communication and offer the chance to make industry connections.

Best Degree for Data Science: Which One Should You Choose?

Navigating the sometimes murky waters of higher education can be a daunting task, especially when it comes to choosing the best degree for data science, but here are some well-respected choices.

1. M.S. In Data Analytics – Franklin University

This online qualification will equip the professional with the statistical skills required to conduct descriptive and predictive analytics. It also provides the programming skillset necessary to create and apply computer algorithms and the tools and platforms to visualize and mine big data. Students can expect to complete the coursework in around 19 months.

2. Bachelor of Science in Industrial Systems Data Analytics – Lakeland University

The strength of this qualification from Lakeland is its focus on both programming and data management. The flexibility of the on-campus/online program makes it a very attractive option for those who already hold a 9-5 job. This program provides students with essential skills in programming, statistics, data analysis, and visualization.

3. Bachelor of Science in Data Analytics – Southern New Hampshire University

Although this is an online course, the experience of using advanced analytical tools to solve real-world challenges will provide potential employers with peace of mind. Also on offer is a focus on project management, which is essential given the complexities of data-driven projects. Focus areas include data analytics, computer science, and computer programming. The course should take four years to complete, although online delivery allows students to graduate more quickly

4. Bachelor of Science in Computer Science – Full Sail University

This program focuses on data structure and system design. The online and on-campus study option means that students can finish the coursework in less than 80 weeks. Focusing on core competencies such as computer science, computer programming, and data science, it is the perfect qualification for those entering the potentially rewarding world of data science.

5. Bachelor of Science in Data Analytics – Lynn University

The 100% online undergraduate qualification in data analytics can be completed in four years or less. Coursework includes business analytics, advanced business techniques, data programming, and data mining. With a focus on real-world solutions, this is a program that will pay dividends in increased employability in a highly competitive environment.

OPIT’s Bachelor’s and Master’s Programs in Data Science

OPIT’s Bachelor’s (BSc) in Modern Computer Science and Master’s Degrees (MSc) in Applied Digital Business and Applied Data Science & Artificial Intelligence have been designed with input from industry leaders and feature real-world application of the skills gained through study. This approach results in qualifications that are extremely attractive to potential employers.

The BSC in Modern Computer Science

The coursework of the six-term Bachelor’s in Modern Computer Science is delivered entirely using state-of-the-art platforms designed for ease of use and flexibility.

Both potential employers, academics, and industry professionals have had a hand in developing this degree. It aims to provide graduates with theoretical and practical 360-degree foundational skills, including such coursework as programming, software development, database development and functionality. Students will also dive into more complex topics like cloud computing, cybersecurity, data science, and the ever-more important subject of Artificial Intelligence.

The MSC in Applied Digital Business and Applied Data Science & Artificial Intelligence

The 12–18 month Master’s Degree (MSc) in Applied Digital Business from OPIT supplies students with the knowledge and skills to tackle real-world challenges in technology, digitalization, and business. Coursework includes strategically orientated subjects such as digital transformation, digital finance, entrepreneurship, and digital product management. Students will also explore real-world applications with a capstone project and dissertation based on a real-world case study.

The 12-18 month Master’s in Applied Data Science & Artificial Intelligence is at the cutting edge of data science specialization. Online delivery of coursework means that students have incredible flexibility and can complete coursework at their own pace, a boon for busy professionals. Like other OPIT Master’s courses, this program emphasizes foundational principles and courses with content applicable to real-world challenges that can be analyzed using data science and AI. Coursework includes business principles, data science, machine learning, and Artificial Intelligence.

Why Consider OPIT for Your Data Science Education?

OPIT’s affordable, fully accredited, and internationally recognized degrees leverage knowledge from leading academics and industry leaders. This ensures the most relevant course content and resources, all delivered via cutting-edge online platforms. The institute’s flexible scheduling, the blend of theoretical and practical knowledge, and hands-on experience deliver an educational experience unlike any other available today.

The Future of Data Science and the Role of Education

The amount of data that has to be gathered, stored and analyzed by businesses is growing exponentially. This has fueled increasing demand for skilled and qualified data scientists. Employers are looking for the best of the best, and one of the time-proven ways to stand out from the crowd is by obtaining a recognized and respected qualification – the best degree for data science.

Of course, the learning doesn’t stop at one degree. Data science pioneers know the importance of lifelong learning and staying abreast of the latest methodologies, trends, and advancements.

A Data Science Degree – Making the Right Choice

Choosing the best degree for data science can be a challenge, but that challenge becomes manageable when one whittles down the choices. Make sure that the education provider you choose has impeccable credentials and a good reputation. Both of these are based on the delivery of exceptional course content that focuses on both theory and real-world experience.

Employers want graduates who can hit the ground running. Choosing a degree from OPIT means that the employee can start adding real value to organizational strategy from Day 1, and that is what employers want.

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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.”

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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.

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