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    Finding the Best Degree for Data Science Success

    OPIT - Open Institute of Technology
    OPIT - Open Institute of Technology
    Mar 18, 2024 • 8 min read
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    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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    CCN: Australia Tightens Crypto Oversight as Exchanges Expand, Testing Industry’s Appetite for Regulation
    OPIT - Open Institute of Technology
    OPIT - Open Institute of Technology
    Mar 31, 2025 • 3 min read

    Source:

    • CCN, published on March 29th, 2025

    By Kurt Robson

    Over the past few months, Australia’s crypto industry has undergone a rapid transformation following the government’s proposal to establish a stricter set of digital asset regulations.

    A series of recent enforcement measures and exchange launches highlight the growing maturation of Australia’s crypto landscape.

    Experts remain divided on how the new rules will impact the country’s burgeoning digital asset industry.

    New Crypto Regulation

    On March 21, the Treasury Department said that crypto exchanges and custody services will now be classified under similar rules as other financial services in the country.

    “Our legislative reforms will extend existing financial services laws to key digital asset platforms, but not to all of the digital asset ecosystem,” the Treasury said in a statement.

    The rules impose similar regulations as other financial services in the country, such as obtaining a financial license, meeting minimum capital requirements, and safeguarding customer assets.

    The proposal comes as Australian Prime Minister Anthony Albanese’s center-left Labor government prepares for a federal election on May 17.

    Australia’s opposition party, led by Peter Dutton, has also vowed to make crypto regulation a top priority of the government’s agenda if it wins.

    Australia’s Crypto Growth

    Triple-A data shows that 9.6% of Australians already own digital assets, with some experts believing new rules will push further adoption.

    Europe’s largest crypto exchange, WhiteBIT, announced it was entering the Australian market on Wednesday, March 26.

    The company said that Australia was “an attractive landscape for crypto businesses” despite its complexity.

    In March, Australia’s Swyftx announced it was acquiring New Zealand’s largest cryptocurrency exchange for an undisclosed sum.

    According to the parties, the merger will create the second-largest platform in Australia by trading volume.

    “Australia’s new regulatory framework is akin to rolling out the welcome mat for cryptocurrency exchanges,” Alexander Jader, professor of Digital Business at the Open Institute of Technology, told CCN.

    “The clarity provided by these regulations is set to attract a wave of new entrants,” he added.

    Jader said regulatory clarity was “the lifeblood of innovation.” He added that the new laws can expect an uptick “in both local and international exchanges looking to establish a foothold in the market.”

    However, Zoe Wyatt, partner and head of Web3 and Disruptive Technology at Andersen LLP, believes that while the new rules will benefit more extensive exchanges looking for more precise guidelines, they will not “suddenly turn Australia into a global crypto hub.”

    “The Web3 community is still largely looking to the U.S. in anticipation of a more crypto-friendly stance from the Trump administration,” Wyatt added.

    Read the full article below:

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    Agenda Digitale: Generative AI in the Enterprise – A Guide to Conscious and Strategic Use
    OPIT - Open Institute of Technology
    OPIT - Open Institute of Technology
    Mar 31, 2025 • 6 min read

    Source:

    By Zorina Alliata, Professor of Responsible Artificial Intelligence e Digital Business & Innovation at OPIT – Open Institute of Technology

    Integrating generative AI into your business means innovating, but also managing risks. Here’s how to choose the right approach to get value

    The adoption of generative AI in the enterprise is growing rapidly, bringing innovation to decision-making, creativity and operations. However, to fully exploit its potential, it is essential to define clear objectives and adopt strategies that balance benefits and risks.

    Over the course of my career, I have been fortunate to experience firsthand some major technological revolutions – from the internet boom to the “renaissance” of artificial intelligence a decade ago with machine learning.

    However, I have never seen such a rapid rate of adoption as the one we are experiencing now, thanks to generative AI. Although this type of AI is not yet perfect and presents significant risks – such as so-called “hallucinations” or the possibility of generating toxic content – ​​it fills a real need, both for people and for companies, generating a concrete impact on communication, creativity and decision-making processes.

    Defining the Goals of Generative AI in the Enterprise

    When we talk about AI, we must first ask ourselves what problems we really want to solve. As a teacher and consultant, I have always supported the importance of starting from the specific context of a company and its concrete objectives, without inventing solutions that are as “smart” as they are useless.

    AI is a formidable tool to support different processes: from decision-making to optimizing operations or developing more accurate predictive analyses. But to have a significant impact on the business, you need to choose carefully which task to entrust it with, making sure that the solution also respects the security and privacy needs of your customers .

    Understanding Generative AI to Adopt It Effectively

    A widespread risk, in fact, is that of being guided by enthusiasm and deploying sophisticated technology where it is not really needed. For example, designing a system of reviews and recommendations for films requires a certain level of attention and consumer protection, but it is very different from an X-ray reading service to diagnose the presence of a tumor. In the second case, there is a huge ethical and medical risk at stake: it is necessary to adapt the design, control measures and governance of the AI ​​to the sensitivity of the context in which it will be used.

    The fact that generative AI is spreading so rapidly is a sign of its potential and, at the same time, a call for caution. This technology manages to amaze anyone who tries it: it drafts documents in a few seconds, summarizes or explains complex concepts, manages the processing of extremely complex data. It turns into a trusted assistant that, on the one hand, saves hours of work and, on the other, fosters creativity with unexpected suggestions or solutions.

    Yet, it should not be forgotten that these systems can generate “hallucinated” content (i.e., completely incorrect), or show bias or linguistic toxicity where the starting data is not sufficient or adequately “clean”. Furthermore, working with AI models at scale is not at all trivial: many start-ups and entrepreneurs initially try a successful idea, but struggle to implement it on an infrastructure capable of supporting real workloads, with adequate governance measures and risk management strategies. It is crucial to adopt consolidated best practices, structure competent teams, define a solid operating model and a continuous maintenance plan for the system.

    The Role of Generative AI in Supporting Business Decisions

    One aspect that I find particularly interesting is the support that AI offers to business decisions. Algorithms can analyze a huge amount of data, simulating multiple scenarios and identifying patterns that are elusive to the human eye. This allows to mitigate biases and distortions – typical of exclusively human decision-making processes – and to predict risks and opportunities with greater objectivity.

    At the same time, I believe that human intuition must remain key: data and numerical projections offer a starting point, but context, ethics and sensitivity towards collaborators and society remain elements of human relevance. The right balance between algorithmic analysis and strategic vision is the cornerstone of a responsible adoption of AI.

    Industries Where Generative AI Is Transforming Business

    As a professor of Responsible Artificial Intelligence and Digital Business & Innovation, I often see how some sectors are adopting AI extremely quickly. Many industries are already transforming rapidly. The financial sector, for example, has always been a pioneer in adopting new technologies: risk analysis, fraud prevention, algorithmic trading, and complex document management are areas where generative AI is proving to be very effective.

    Healthcare and life sciences are taking advantage of AI advances in drug discovery, advanced diagnostics, and the analysis of large amounts of clinical data. Sectors such as retail, logistics, and education are also adopting AI to improve their processes and offer more personalized experiences. In light of this, I would say that no industry will be completely excluded from the changes: even “humanistic” professions, such as those related to medical care or psychological counseling, will be able to benefit from it as support, without AI completely replacing the relational and care component.

    Integrating Generative AI into the Enterprise: Best Practices and Risk Management

    A growing trend is the creation of specialized AI services AI-as-a-Service. These are based on large language models but are tailored to specific functionalities (writing, code checking, multimedia content production, research support, etc.). I personally use various AI-as-a-Service tools every day, deriving benefits from them for both teaching and research. I find this model particularly advantageous for small and medium-sized businesses, which can thus adopt AI solutions without having to invest heavily in infrastructure and specialized talent that are difficult to find.

    Of course, adopting AI technologies requires companies to adopt a well-structured risk management strategy, covering key areas such as data protection, fairness and lack of bias in algorithms, transparency towards customers, protection of workers, definition of clear responsibilities regarding automated decisions and, last but not least, attention to environmental impact. Each AI model, especially if trained on huge amounts of data, can require significant energy consumption.

    Furthermore, when we talk about generative AI and conversational models , we add concerns about possible inappropriate or harmful responses (so-called “hallucinations”), which must be managed by implementing filters, quality control and continuous monitoring processes. In other words, although AI can have disruptive and positive effects, the ultimate responsibility remains with humans and the companies that use it.

    Read the full article below (in Italian):

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