Data is the digital powerhouse, and data science is the driving force behind it. It’s a tool for uncovering stories hidden in data, predicting the future, and making smart decisions that shape industries. So, what can you do with a data science degree? A whole lot, it turns out. Let’s find out more.

Exploring Career Paths with a Data Science Degree:

The demand for data-savvy professionals is skyrocketing across various sectors. Let’s break down the “who’s who” in data science and see where you could fit in.

  • As a data scientist, you’re at the forefront of unearthing insights from a mass of data. Day to day, you will build predictive models and algorithms and drive strategic decisions.
  • The machine learning engineer role means you develop systems that learn from data and improve themselves without human intervention: smart algorithms that predict user behavior, automate tasks, and even drive cars.
  • Data analysts turn data into easily understandable insights. Their toolkit includes statistical analysis, data visualization, and a knack for spotting trends for informed decision-making.
  • As a business intelligence analyst, you bridge data and strategy to help organizations make smarter decisions through data. This involves analyzing market trends, monitoring competition, and creating dashboards of the company’s performance.

All this is just scratching the surface. When pondering “what jobs can you get with a data science degree,” there’s nearly limitless potential. With a data science degree, you could work anywhere from tech giants and finance firms to healthcare organizations and government agencies. For just a few examples, you could predict the financial trends and outcomes of a healthcare initiative or follow student progress in an educational institution.

Is a Data Science Degree Worth It?

A data science degree opens pathways to various industries, like online marketing, finances, environment, or entertainment. Clearly, data is everywhere, and so is the demand for those who can understand and manipulate it.

With how widely applicable data science is, salary potential is unsurprisingly vast. It’s a field where six-figure salaries are the norm, not the exception. The median annual wage for data scientist is £59,582 per year in London, and around €78,646 in Berlin. And that’s just the median—many data scientists earn significantly more, especially as they gain experience in high-demand areas.

The demand for data professionals is through the roof. Every company tries to become more data-driven and needs people who can analyze, interpret, and leverage data. This demand translates to job security and plenty of opportunities to advance your career.

Personal growth is another massive perk. Data science is in a permanent flux, which means you’re always learning. New programming languages, machine learning algorithms, or ways to visualize data are being introduced to put you on the cutting edge of tech.

Employment for data scientists might soar by 35% from 2022 to 2032, with an average of 17,700 job openings each year, a much faster growth than the average. Salaries range impressively from $95,000 to $250,000 when expressed in USD.

What to Do With a Data Science Degree Beyond Traditional Paths:

Here are some thought-provoking directions for what to do with a data science degree.

Entrepreneurship

Data science acumen can see you launching startups that use big data. Perhaps you could build apps that predict consumer behavior or platforms that personalize education. Your ability to extract insights from data can identify untapped markets or create entirely new service categories.

Consultancy

As a consultant, you can be the beacon of wisdom for businesses across the spectrum. Your know-how could create a more optimal retail supply chain, mitigate financial risks for a bank, or measure the impact of a nonprofit’s programs.

Positions in Non-Tech Industries

Data science is infiltrating every corner of the economy. You can use data to improve manufacturing, make hospital conditions better for patients, optimize crop yields in agriculture, or contribute to saving the environment by following emission trends. Your skills could lead to breakthroughs in sustainability, quality of life, and more.

Cross-Disciplinary

The intersection of data science with other fields opens up exciting new roles. Consider a career as a digital humanities researcher, where you apply data analysis to uncover trends in literature, art, or history. Or perhaps you could become a legal tech consultant who predicts trial outcomes or analyzes legal documents. Data science collaborating with other disciplines can lead to entirely new fields of study.

Navigating the Intersection: Data Science and Cybersecurity

Data science’s knack for sifting through mountains of data to uncover hidden patterns or predict future threats complements cybersecurity’s focus on protecting these insights and the systems that house them. Therefore, you might have a dual focus: using analytical techniques for data security and applying security principles to protect data integrity. The synergy bolsters defense mechanisms and makes data analysis more sophisticated and broader.

OPIT’s Distinctive Educational Offerings

Studying online makes sense – it’s flexible so you can learn at your own pace, and lets you connect with peers and experts from all over the world. It’s also much more accessible and affordable than traditional education. Starting with the Bachelor’s Degree (BSc) in Modern Computer Science, OPIT gives you a solid foundation to make a mark in data science. This program covers the essentials—programming, software development, databases, and cybersecurity. It’s equally valuable to professionals to boost their skills as well as fresh high school graduates who want a future in computer science.

Furthermore, OPIT’s Master’s Degrees (MSc) in Applied Digital Business and Applied Data Science and AI bring together the business and technology of the future now. These programs reveal the symbiosis between tech and business. Students spearhead digital strategies, manage digital products, and navigate digital finance. In an economy increasingly defined by digital interactions, these degrees prepare you to be at the forefront.

OPIT, as your educational partner, combines career-aligned curricula, flexible studying, creative testing, and the chance to connect to top-dog industry experts.

Data Science Is a Door Opener

Let’s recap the question: “Is a data science degree worth it?” With a data science degree from OPIT, the career paths you take are promising, no matter where you go. If your passion lies in crunching numbers to reveal hidden patterns or using insights to drive business strategies, the qualifications can lead you to numerous possibilities.

Think long and hard about your aspirations and interests, and consider how they align with the power of data science. There will never be a dull moment in your data science career, and OPIT’s program is a surefire way to get you there.

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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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