Data is the heartbeat of the digital realm. And when something is so important, you want to ensure you deal with it properly. That’s where data structures come into play.
But what is data structure exactly?
In the simplest terms, a data structure is a way of organizing data on a computing machine so that you can access and update it as quickly and efficiently as possible. For those looking for a more detailed data structure definition, we must add processing, retrieving, and storing data to the purposes of this specialized format.
With this in mind, the importance of data structures becomes quite clear. Neither humans nor machines could access or use digital data without these structures.
But using data structures isn’t enough on its own. You must also use the right data structure for your needs.
This article will guide you through the most common types of data structures, explain the relationship between data structures and algorithms, and showcase some real-world applications of these structures.
Armed with this invaluable knowledge, choosing the right data structure will be a breeze.
Types of Data Structures
Like data, data structures have specific characteristics, features, and applications. These are the factors that primarily dictate which data structure should be used in which scenario. Below are the most common types of data structures and their applications.
Primitive Data Structures
Take one look at the name of this data type, and its structure won’t surprise you. Primitive data structures are to data what cells are to a human body – building blocks. As such, they hold a single value and are typically built into programming languages. Whether you check data structures in C or data structures in Java, these are the types of data structures you’ll find.
- Integer (signed or unsigned) – Representing whole numbers
- Float (floating-point numbers) – Representing real numbers with decimal precision
- Character – Representing integer values as symbols
- Boolean – Storing true or false logical values
Non-Primitive Data Structures
Combine primitive data structures, and you get non-primitive data structures. These structures can be further divided into two types.
Linear Data Structures
As the name implies, a linear data structure arranges the data elements linearly (sequentially). In this structure, each element is attached to its predecessor and successor.
The most commonly used linear data structures (and their real-life applications) include the following:
- In arrays, multiple elements of the same type are stored together in the same location. As a result, they can all be processed relatively quickly. (library management systems, ticket booking systems, mobile phone contacts, etc.)
- Linked lists. With linked lists, elements aren’t stored at adjacent memory locations. Instead, the elements are linked with pointers indicating the next element in the sequence. (music playlists, social media feeds, etc.)
- These data structures follow the Last-In-First-Out (LIFO) sequencing order. As a result, you can only enter or retrieve data from one stack end (browsing history, undo operations in word processors, etc.)
- Queues follow the First-In-First-Out (FIFO) sequencing order (website traffic, printer task scheduling, video queues, etc.)
Non-Linear Data Structures
A non-linear data structure also has a pretty self-explanatory name. The elements aren’t placed linearly. This also means you can’t traverse all of them in a single run.
- Trees are tree-like (no surprise there!) hierarchical data structures. These structures consist of nodes, each filled with specific data (routers in computer networks, database indexing, etc.)
- Combine vertices (or nodes) and edges, and you get a graph. These data structures are used to solve the most challenging programming problems (modeling, computation flow, etc.)
Advanced Data Structures
Venture beyond primitive data structures (building blocks for data structures) and basic non-primitive data structures (building blocks for more sophisticated applications), and you’ll reach advanced data structures.
- Hash tables. These advanced data structures use hash functions to store data associatively (through key-value pairs). Using the associated values, you can quickly access the desired data (dictionaries, browser searching, etc.)
- Heaps are specialized tree-like data structures that satisfy the heap property (every tree element is larger than its descendant.)
- Tries store strings that can be organized in a visual graph and retrieved when necessary (auto-complete function, spell checkers, etc.)
Algorithms for Data Structures
There is a common misconception that data structures and algorithms in Java and other programming languages are one and the same. In reality, algorithms are steps used to structure data and solve other problems. Check out our overview of some basic algorithms for data structures.
Searching Algorithms
Searching algorithms are used to locate specific elements within data structures. Whether you’re searching for specific data structures in C++ or another programming language, you can use two types of algorithms:
- Linear search: starts from one end and checks each sequential element until the desired element is located
- Binary search: looks for the desired element in the middle of a sorted list of items (If the elements aren’t sorted, you must do that before a binary search.)
Sorting Algorithms
Whenever you need to arrange elements in a specific order, you’ll need sorting algorithms.
- Bubble sort: Compares two adjacent elements and swaps them if they’re in the wrong order
- Selection sort: Sorts lists by identifying the smallest element and placing it at the beginning of the unsorted list
- Insertion sort: Inserts the unsorted element in the correct position straight away
- Merge sort: Divides unsorted lists into smaller sections and orders each separately (the so-called divide-and-conquer principle)
- Quick sort: Also relies on the divide-and-conquer principle but employs a pivot element to partition the list (elements smaller than the pivot element go back, while larger ones are kept on the right)
Tree Traversal Algorithms
To traverse a tree means to visit its every node. Since trees aren’t linear data structures, there’s more than one way to traverse them.
- Pre-order traversal: Visits the root node first (the topmost node in a tree), followed by the left and finally the right subtree
- In-order traversal: Starts with the left subtree, moves to the root node, and ends with the right subtree
- Post-order traversal: Visits the nodes in the following order: left subtree, right subtree, the root node
Graph Traversal Algorithms
Graph traversal algorithms traverse all the vertices (or nodes) and edges in a graph. You can choose between two:
- Depth-first search – Focuses on visiting all the vertices or nodes of a graph data structure located one above the other
- Breadth-first search – Traverses the adjacent nodes of a graph before moving outwards
Applications of Data Structures
Data structures are critical for managing data. So, no wonder their extensive list of applications keeps growing virtually every day. Check out some of the most popular applications data structures have nowadays.
Data Organization and Storage
With this application, data structures return to their roots: they’re used to arrange and store data most efficiently.
Database Management Systems
Database management systems are software programs used to define, store, manipulate, and protect data in a single location. These systems have several components, each relying on data structures to handle records to some extent.
Let’s take a library management system as an example. Data structures are used every step of the way, from indexing books (based on the author’s name, the book’s title, genre, etc.) to storing e-books.
File Systems
File systems use specific data structures to represent information, allocate it to the memory, and manage it afterward.
Data Retrieval and Processing
With data structures, data isn’t stored and then forgotten. It can also be retrieved and processed as necessary.
Search Engines
Search engines (Google, Bing, Yahoo, etc.) are arguably the most widely used applications of data structures. Thanks to structures like tries and hash tables, search engines can successfully index web pages and retrieve the information internet users seek.
Data Compression
Data compression aims to accurately represent data using the smallest storage amount possible. But without data structures, there wouldn’t be data compression algorithms.
Data Encryption
Data encryption is crucial for preserving data confidentiality. And do you know what’s crucial for supporting cryptography algorithms? That’s right, data structures. Once the data is encrypted, data structures like hash tables also aid with value key storage.
Problem Solving and Optimization
At their core, data structures are designed for optimizing data and solving specific problems (both simple and complex). Throw their composition into the mix, and you’ll understand why these structures have been embraced by fields that heavily rely on mathematics and algorithms for problem-solving.
Artificial Intelligence
Artificial intelligence (AI) is all about data. For machines to be able to use this data, it must be properly stored and organized. Enter data structures.
Arrays, linked lists, queues, graphs, and stacks are just some structures used to store data for AI purposes.
Machine Learning
Data structures used for machine learning (MI) are pretty similar to other computer science fields, including AI. In machine learning, data structures (both linear and non-linear) are used to solve complex mathematical problems, manipulate data, and implement ML models.
Network Routing
Network routing refers to establishing paths through one or more internet networks. Various routing algorithms are used for this purpose and most heavily rely on data structures to find the best patch for the incoming data packet.
Data Structures: The Backbone of Efficiency
Data structures are critical in our data-driven world. They allow straightforward data representation, access, and manipulation, even in giant databases. For this reason, learning about data structures and algorithms further can open up a world of possibilities for a career in data science and related fields.
-
From the same author
Read the articleSource:
- The Yuan, Published on October 25th, 2024.
By Zorina Alliata
ALEXANDRIA, VIRGINIA – In recent years, artificial intelligence (AI) has grown and developed into something much bigger than most people could have ever expected. Jokes about robots living among humans no longer seem so harmless, and the average person began to develop a new awareness of AI and all its uses. Unfortunately, however – as is often a human tendency – people became hyper-fixated on the negative aspects of AI, often forgetting about all the good it can do. One should therefore take a step back and remember that humanity is still only in the very early stages of developing real intelligence outside of the human brain, and so at this point AI is almost like a small child that humans are raising.
AI is still developing, growing, and adapting, and like any new tech it has its drawbacks. At one point, people had fears and doubts about electricity, calculators, and mobile phones – but now these have become ubiquitous aspects of everyday life, and it is not difficult to imagine a future in which this is the case for AI as well.
The development of AI certainly comes with relevant and real concerns that must be addressed – such as its controversial role in education, the potential job losses it might lead to, and its bias and inaccuracies. For every fear, however, there is also a ray of hope, and that is largely thanks to people and their ingenuity.
Looking at education, many educators around the world are worried about recent developments in AI. The frequently discussed ChatGPT – which is now on its fourth version – is a major red flag for many, causing concerns around plagiarism and creating fears that it will lead to the end of writing as people know it. This is one of the main factors that has increased the pessimistic reporting about AI that one so often sees in the media.
However, when one actually considers ChatGPT in its current state, it is safe to say that these fears are probably overblown. Can ChatGPT really replace the human mind, which is capable of so much that AI cannot replicate? As for educators, instead of assuming that all their students will want to cheat, they should instead consider the options for taking advantage of new tech to enhance the learning experience. Most people now know the tell-tale signs for identifying something that ChatGPT has written. Excessive use of numbered lists, repetitive language and poor comparison skills are just three ways to tell if a piece of writing is legitimate or if a bot is behind it. This author personally encourages the use of AI in the classes I teach. This is because it is better for students to understand what AI can do and how to use it as a tool in their learning instead of avoiding and fearing it, or being discouraged from using it no matter the circumstances.
Educators should therefore reframe the idea of ChatGPT in their minds, have open discussions with students about its uses, and help them understand that it is actually just another tool to help them learn more efficiently – and not a replacement for their own thoughts and words. Such frank discussions help students develop their critical thinking skills and start understanding their own influence on ChatGPT and other AI-powered tools.
By developing one’s understanding of AI’s actual capabilities, one can begin to understand its uses in everyday life. Some would have people believe that this means countless jobs will inevitably become obsolete, but that is not entirely true. Even if AI does replace some jobs, it will still need industry experts to guide it, meaning that entirely new jobs are being created at the same time as some older jobs are disappearing.
Adapting to AI is a new challenge for most industries, and it is certainly daunting at times. The reality, however, is that AI is not here to steal people’s jobs. If anything, it will change the nature of some jobs and may even improve them by making human workers more efficient and productive. If AI is to be a truly useful tool, it will still need humans. One should remember that humans working alongside AI and using it as a tool is key, because in most cases AI cannot do the job of a person by itself.
Is AI biased?
Why should one view AI as a tool and not a replacement? The main reason is because AI itself is still learning, and AI-powered tools such as ChatGPT do not understand bias. As a result, whenever ChatGPT is asked a question it will pull information from anywhere, and so it can easily repeat old biases. AI is learning from previous data, much of which is biased or out of date. Data about home ownership and mortgages, e.g., are often biased because non-white people in the United States could not get a mortgage until after the 1960s. The effect on data due to this lending discrimination is only now being fully understood.
AI is certainly biased at times, but that stems from human bias. Again, this just reinforces the need for humans to be in control of AI. AI is like a young child in that it is still absorbing what is happening around it. People must therefore not fear it, but instead guide it in the right direction.
For AI to be used as a tool, it must be treated as such. If one wanted to build a house, one would not expect one’s tools to be able to do the job alone – and AI must be viewed through a similar lens. By acknowledging this aspect of AI and taking control of humans’ role in its development, the world would be better placed to reap the benefits and quash the fears associated with AI. One should therefore not assume that all the doom and gloom one reads about AI is exactly as it seems. Instead, people should try experimenting with it and learning from it, and maybe soon they will realize that it was the best thing that could have happened to humanity.
Read the full article below:
Read the articleSource:
- The European Business Review, Published on October 27th, 2024.
By Lokesh Vij
Lokesh Vij is a Professor of BSc in Modern Computer Science & MSc in Applied Data Science & AI at Open Institute of Technology. With over 20 years of experience in cloud computing infrastructure, cybersecurity and cloud development, Professor Vij is an expert in all things related to data and modern computer science.
In today’s rapidly evolving technological landscape, the fields of blockchain and cloud computing are transforming industries, from finance to healthcare, and creating new opportunities for innovation. Integrating these technologies into education is not merely a trend but a necessity to equip students with the skills they need to thrive in the future workforce. Though both technologies are independently powerful, their potential for innovation and disruption is amplified when combined. This article explores the pressing questions surrounding the inclusion of blockchain and cloud computing in education, providing a comprehensive overview of their significance, benefits, and challenges.
The Technological Edge and Future Outlook
Cloud computing has revolutionized how businesses and individuals’ access and manage data and applications. Benefits like scalability, cost efficiency (including eliminating capital expenditure – CapEx), rapid innovation, and experimentation enable businesses to develop and deploy new applications and services quickly without the constraints of traditional on-premises infrastructure – thanks to managed services where cloud providers manage the operating system, runtime, and middleware, allowing businesses to focus on development and innovation. According to Statista, the cloud computing market is projected to reach a significant size of Euro 250 billion or even higher by 2028 (from Euro 110 billion in 2024), with a substantial Compound Annual Growth Rate (CAGR) of 22.78%. The widespread adoption of cloud computing by businesses of all sizes, coupled with the increasing demand for cloud-based services and applications, fuels the need for cloud computing professionals.
Blockchain, a distributed ledger technology, has paved the way by providing a secure, transparent, and tamper-proof way to record transactions (highly resistant to hacking and fraud). In 2021, European blockchain startups raised $1.5 billion in funding, indicating strong interest and growth potential. Reports suggest the European blockchain market could reach $39 billion by 2026, with a significant CAGR of over 47%. This growth is fueled by increasing adoption in sectors like finance, supply chain, and healthcare.
Addressing the Skills Gap
Reports from the World Economic Forum indicate that 85 million jobs may be displaced by a shift in the division of labor between humans and machines by 2025. However, 97 million new roles may emerge that are more adapted to the new division of labor between humans, machines, and algorithms, many of which will require proficiency in cloud computing and blockchain.
Furthermore, the World Economic Forum predicts that by 2027, 10% of the global GDP will be tokenized and stored on the blockchain. This massive shift means a surge in demand for blockchain professionals across various industries. Consider the implications of 10% of the global GDP being on the blockchain: it translates to a massive need for people who can build, secure, and manage these systems. We’re talking about potentially millions of jobs worldwide.
The European Blockchain Services Infrastructure (EBSI), an EU initiative, aims to deploy cross-border blockchain services across Europe, focusing on areas like digital identity, trusted data sharing, and diploma management. The EU’s MiCA (Crypto-Asset Regulation) regulation, expected to be fully implemented by 2025, will provide a clear legal framework for crypto-assets, fostering innovation and investment in the blockchain space. The projected growth and supportive regulatory environment point to a rising demand for blockchain professionals in Europe. Developing skills related to EBSI and its applications could be highly advantageous, given its potential impact on public sector blockchain adoption. Understanding the MiCA regulation will be crucial for blockchain roles related to crypto-assets and decentralized finance (DeFi).
Furthermore, European businesses are rapidly adopting digital technologies, with cloud computing as a core component of this transformation. GDPR (Data Protection Regulations) and other data protection laws push businesses to adopt secure and compliant cloud solutions. Many European countries invest heavily in cloud infrastructure and promote cloud adoption across various sectors. Artificial intelligence and machine learning will be deeply integrated into cloud platforms, enabling smarter automation, advanced analytics, and more efficient operations. This allows developers to focus on building applications without managing servers, leading to faster development cycles and increased scalability. Processing data closer to the source (like on devices or local servers) will become crucial for applications requiring real-time responses, such as IoT and autonomous vehicles.
The projected growth indicates a strong and continuous demand for blockchain and cloud professionals in Europe and worldwide. As we stand at the “crossroads of infinity,” there is a significant skill shortage, which will likely increase with the rapid adoption of these technologies. A 2023 study by SoftwareOne found that 95% of businesses globally face a cloud skills gap. Specific skills in high demand include cloud security, cloud-native development, and expertise in leading cloud platforms like AWS, Azure, and Google Cloud. The European Commission’s Digital Economy and Society Index (DESI) highlights a need for improved digital skills in areas like blockchain to support the EU’s digital transformation goals. A 2023 report by CasperLabs found that 90% of businesses in the US, UK, and China adopt blockchain, but knowledge gaps and interoperability challenges persist.
The Role of Educational Institutions
This surge in demand necessitates a corresponding increase in qualified individuals who can design, implement, and manage cloud-based and blockchain solutions. Educational institutions have a critical role to play in bridging this widening skills gap and ensuring a pipeline of talent ready to meet the demands of this burgeoning industry.
To effectively prepare the next generation of cloud computing and blockchain experts, educational institutions need to adopt a multi-pronged approach. This includes enhancing curricula with specialized programs, integrating cloud and blockchain concepts into existing courses, and providing hands-on experience with leading technology platforms.
Furthermore, investing in faculty development to ensure they possess up-to-date knowledge and expertise is crucial. Collaboration with industry partners through internships, co-teach programs, joint research projects, and mentorship programs can provide students with invaluable real-world experience and insights.
Beyond formal education, fostering a culture of lifelong learning is essential. Offering continuing education courses, boot camps, and online resources enables professionals to upskill or reskill and stay abreast of the latest advancements in cloud computing. Actively promoting awareness of career paths and opportunities in this field and facilitating connections with potential employers can empower students to thrive in the dynamic and evolving landscape of cloud computing and blockchain technologies.
By taking these steps, educational institutions can effectively prepare the young generation to fill the skills gap and thrive in the rapidly evolving world of cloud computing and blockchain.
Read the full article below:
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Related posts
Source:
- The Yuan, Published on October 25th, 2024.
By Zorina Alliata
Artificial intelligence is a classic example of a mismatch between perceptions and reality, as people tend to overlook its positive aspects and fear it far more than what is warranted by its actual capabilities, argues AI strategist and professor Zorina Alliata.
ALEXANDRIA, VIRGINIA – In recent years, artificial intelligence (AI) has grown and developed into something much bigger than most people could have ever expected. Jokes about robots living among humans no longer seem so harmless, and the average person began to develop a new awareness of AI and all its uses. Unfortunately, however – as is often a human tendency – people became hyper-fixated on the negative aspects of AI, often forgetting about all the good it can do. One should therefore take a step back and remember that humanity is still only in the very early stages of developing real intelligence outside of the human brain, and so at this point AI is almost like a small child that humans are raising.
AI is still developing, growing, and adapting, and like any new tech it has its drawbacks. At one point, people had fears and doubts about electricity, calculators, and mobile phones – but now these have become ubiquitous aspects of everyday life, and it is not difficult to imagine a future in which this is the case for AI as well.
The development of AI certainly comes with relevant and real concerns that must be addressed – such as its controversial role in education, the potential job losses it might lead to, and its bias and inaccuracies. For every fear, however, there is also a ray of hope, and that is largely thanks to people and their ingenuity.
Looking at education, many educators around the world are worried about recent developments in AI. The frequently discussed ChatGPT – which is now on its fourth version – is a major red flag for many, causing concerns around plagiarism and creating fears that it will lead to the end of writing as people know it. This is one of the main factors that has increased the pessimistic reporting about AI that one so often sees in the media.
However, when one actually considers ChatGPT in its current state, it is safe to say that these fears are probably overblown. Can ChatGPT really replace the human mind, which is capable of so much that AI cannot replicate? As for educators, instead of assuming that all their students will want to cheat, they should instead consider the options for taking advantage of new tech to enhance the learning experience. Most people now know the tell-tale signs for identifying something that ChatGPT has written. Excessive use of numbered lists, repetitive language and poor comparison skills are just three ways to tell if a piece of writing is legitimate or if a bot is behind it. This author personally encourages the use of AI in the classes I teach. This is because it is better for students to understand what AI can do and how to use it as a tool in their learning instead of avoiding and fearing it, or being discouraged from using it no matter the circumstances.
Educators should therefore reframe the idea of ChatGPT in their minds, have open discussions with students about its uses, and help them understand that it is actually just another tool to help them learn more efficiently – and not a replacement for their own thoughts and words. Such frank discussions help students develop their critical thinking skills and start understanding their own influence on ChatGPT and other AI-powered tools.
By developing one’s understanding of AI’s actual capabilities, one can begin to understand its uses in everyday life. Some would have people believe that this means countless jobs will inevitably become obsolete, but that is not entirely true. Even if AI does replace some jobs, it will still need industry experts to guide it, meaning that entirely new jobs are being created at the same time as some older jobs are disappearing.
Adapting to AI is a new challenge for most industries, and it is certainly daunting at times. The reality, however, is that AI is not here to steal people’s jobs. If anything, it will change the nature of some jobs and may even improve them by making human workers more efficient and productive. If AI is to be a truly useful tool, it will still need humans. One should remember that humans working alongside AI and using it as a tool is key, because in most cases AI cannot do the job of a person by itself.
Is AI biased?
Why should one view AI as a tool and not a replacement? The main reason is because AI itself is still learning, and AI-powered tools such as ChatGPT do not understand bias. As a result, whenever ChatGPT is asked a question it will pull information from anywhere, and so it can easily repeat old biases. AI is learning from previous data, much of which is biased or out of date. Data about home ownership and mortgages, e.g., are often biased because non-white people in the United States could not get a mortgage until after the 1960s. The effect on data due to this lending discrimination is only now being fully understood.
AI is certainly biased at times, but that stems from human bias. Again, this just reinforces the need for humans to be in control of AI. AI is like a young child in that it is still absorbing what is happening around it. People must therefore not fear it, but instead guide it in the right direction.
For AI to be used as a tool, it must be treated as such. If one wanted to build a house, one would not expect one’s tools to be able to do the job alone – and AI must be viewed through a similar lens. By acknowledging this aspect of AI and taking control of humans’ role in its development, the world would be better placed to reap the benefits and quash the fears associated with AI. One should therefore not assume that all the doom and gloom one reads about AI is exactly as it seems. Instead, people should try experimenting with it and learning from it, and maybe soon they will realize that it was the best thing that could have happened to humanity.
Read the full article below:
Source:
- The European Business Review, Published on October 27th, 2024.
By Lokesh Vij
Lokesh Vij is a Professor of BSc in Modern Computer Science & MSc in Applied Data Science & AI at Open Institute of Technology. With over 20 years of experience in cloud computing infrastructure, cybersecurity and cloud development, Professor Vij is an expert in all things related to data and modern computer science.
In today’s rapidly evolving technological landscape, the fields of blockchain and cloud computing are transforming industries, from finance to healthcare, and creating new opportunities for innovation. Integrating these technologies into education is not merely a trend but a necessity to equip students with the skills they need to thrive in the future workforce. Though both technologies are independently powerful, their potential for innovation and disruption is amplified when combined. This article explores the pressing questions surrounding the inclusion of blockchain and cloud computing in education, providing a comprehensive overview of their significance, benefits, and challenges.
The Technological Edge and Future Outlook
Cloud computing has revolutionized how businesses and individuals’ access and manage data and applications. Benefits like scalability, cost efficiency (including eliminating capital expenditure – CapEx), rapid innovation, and experimentation enable businesses to develop and deploy new applications and services quickly without the constraints of traditional on-premises infrastructure – thanks to managed services where cloud providers manage the operating system, runtime, and middleware, allowing businesses to focus on development and innovation. According to Statista, the cloud computing market is projected to reach a significant size of Euro 250 billion or even higher by 2028 (from Euro 110 billion in 2024), with a substantial Compound Annual Growth Rate (CAGR) of 22.78%. The widespread adoption of cloud computing by businesses of all sizes, coupled with the increasing demand for cloud-based services and applications, fuels the need for cloud computing professionals.
Blockchain, a distributed ledger technology, has paved the way by providing a secure, transparent, and tamper-proof way to record transactions (highly resistant to hacking and fraud). In 2021, European blockchain startups raised $1.5 billion in funding, indicating strong interest and growth potential. Reports suggest the European blockchain market could reach $39 billion by 2026, with a significant CAGR of over 47%. This growth is fueled by increasing adoption in sectors like finance, supply chain, and healthcare.
Addressing the Skills Gap
Reports from the World Economic Forum indicate that 85 million jobs may be displaced by a shift in the division of labor between humans and machines by 2025. However, 97 million new roles may emerge that are more adapted to the new division of labor between humans, machines, and algorithms, many of which will require proficiency in cloud computing and blockchain.
Furthermore, the World Economic Forum predicts that by 2027, 10% of the global GDP will be tokenized and stored on the blockchain. This massive shift means a surge in demand for blockchain professionals across various industries. Consider the implications of 10% of the global GDP being on the blockchain: it translates to a massive need for people who can build, secure, and manage these systems. We’re talking about potentially millions of jobs worldwide.
The European Blockchain Services Infrastructure (EBSI), an EU initiative, aims to deploy cross-border blockchain services across Europe, focusing on areas like digital identity, trusted data sharing, and diploma management. The EU’s MiCA (Crypto-Asset Regulation) regulation, expected to be fully implemented by 2025, will provide a clear legal framework for crypto-assets, fostering innovation and investment in the blockchain space. The projected growth and supportive regulatory environment point to a rising demand for blockchain professionals in Europe. Developing skills related to EBSI and its applications could be highly advantageous, given its potential impact on public sector blockchain adoption. Understanding the MiCA regulation will be crucial for blockchain roles related to crypto-assets and decentralized finance (DeFi).
Furthermore, European businesses are rapidly adopting digital technologies, with cloud computing as a core component of this transformation. GDPR (Data Protection Regulations) and other data protection laws push businesses to adopt secure and compliant cloud solutions. Many European countries invest heavily in cloud infrastructure and promote cloud adoption across various sectors. Artificial intelligence and machine learning will be deeply integrated into cloud platforms, enabling smarter automation, advanced analytics, and more efficient operations. This allows developers to focus on building applications without managing servers, leading to faster development cycles and increased scalability. Processing data closer to the source (like on devices or local servers) will become crucial for applications requiring real-time responses, such as IoT and autonomous vehicles.
The projected growth indicates a strong and continuous demand for blockchain and cloud professionals in Europe and worldwide. As we stand at the “crossroads of infinity,” there is a significant skill shortage, which will likely increase with the rapid adoption of these technologies. A 2023 study by SoftwareOne found that 95% of businesses globally face a cloud skills gap. Specific skills in high demand include cloud security, cloud-native development, and expertise in leading cloud platforms like AWS, Azure, and Google Cloud. The European Commission’s Digital Economy and Society Index (DESI) highlights a need for improved digital skills in areas like blockchain to support the EU’s digital transformation goals. A 2023 report by CasperLabs found that 90% of businesses in the US, UK, and China adopt blockchain, but knowledge gaps and interoperability challenges persist.
The Role of Educational Institutions
This surge in demand necessitates a corresponding increase in qualified individuals who can design, implement, and manage cloud-based and blockchain solutions. Educational institutions have a critical role to play in bridging this widening skills gap and ensuring a pipeline of talent ready to meet the demands of this burgeoning industry.
To effectively prepare the next generation of cloud computing and blockchain experts, educational institutions need to adopt a multi-pronged approach. This includes enhancing curricula with specialized programs, integrating cloud and blockchain concepts into existing courses, and providing hands-on experience with leading technology platforms.
Furthermore, investing in faculty development to ensure they possess up-to-date knowledge and expertise is crucial. Collaboration with industry partners through internships, co-teach programs, joint research projects, and mentorship programs can provide students with invaluable real-world experience and insights.
Beyond formal education, fostering a culture of lifelong learning is essential. Offering continuing education courses, boot camps, and online resources enables professionals to upskill or reskill and stay abreast of the latest advancements in cloud computing. Actively promoting awareness of career paths and opportunities in this field and facilitating connections with potential employers can empower students to thrive in the dynamic and evolving landscape of cloud computing and blockchain technologies.
By taking these steps, educational institutions can effectively prepare the young generation to fill the skills gap and thrive in the rapidly evolving world of cloud computing and blockchain.
Read the full article below:
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