Most people feel much better when they organize their personal spaces. Whether that’s an office, living room, or bedroom, it feels good to have everything arranged. Besides giving you a sense of peace and satisfaction, a neatly-organized space ensures you can find everything you need with ease.

The same goes for programs. They need data structures, i.e., ways of organizing data to ensure optimized processing, storage, and retrieval. Without data structures, it would be impossible to create efficient, functional programs, meaning the entire computer science field wouldn’t have its foundation.

Not all data structures are created equal. You have primitive and non-primitive structures, with the latter being divided into several subgroups. If you want to be a better programmer and write reliable and efficient codes, you need to understand the key differences between these structures.

In this introduction to data structures, we’ll cover their classifications, characteristics, and applications.

Primitive Data Structures

Let’s start our journey with the simplest data structures. Primitive data structures (simple data types) consist of characters that can’t be divided. They aren’t a collection of data and can store only one type of data, hence their name. Since primitive data structures can be operated (manipulated) directly according to machine instructions, they’re invaluable for the transmission of information between the programmer and the compiler.

There are four basic types of primitive data structures:

  • Integers
  • Floats
  • Characters
  • Booleans

Integers

Integers store positive and negative whole numbers (along with the number zero). As the name implies, integer data types use integers (no fractions or decimal points) to store precise information. If a value doesn’t belong to the numerical range integer data types support, the server won’t be able to store it.

The main advantages here are space-saving and simplicity. With these data types, you can perform arithmetic operations and store quantities and counts.

Floats

Floats are the opposite of integers. In this case, you have a “floating” number or a number that isn’t whole. They offer more precision but still have a high speed. Systems that have very small or extremely large numbers use floats.

Characters

Next, you have characters. As you may assume, character data types store characters. The characters can be a string of uppercase and/or lowercase single or multibyte letters, numbers, or other symbols that the code set “approves.”

Booleans

Booleans are the third type of data supported by computer programs (the other two are numbers and letters). In this case, the values are positive/negative or true/false. With this data type, you have a binary, either/or division, so you can use it to represent values as valid or invalid.

Linear Data Structures

Let’s move on to non-primitive data structures. The first on our agenda are linear data structures, i.e., those that feature data elements arranged sequentially. Every single element in these structures is connected to the previous and the following element, thus creating a unique linear arrangement.

Linear data structures have no hierarchy; they consist of a single level, meaning the elements can be retrieved in one run.

We can distinguish several types of linear data structures:

  • Arrays
  • Linked lists
  • Stacks
  • Queues

Arrays

Arrays are collections of data elements belonging to the same type. The elements are stored at adjoining locations, and each one can be accessed directly, thanks to the unique index number.

Arrays are the most basic data structures. If you want to conquer the data science field, you should learn the ins and outs of these structures.

They have many applications, from solving matrix problems to CPU scheduling, speech processing, online ticket booking systems, etc.

Linked Lists

Linked lists store elements in a list-like structure. However, the nodes aren’t stored at contiguous locations. Here, every node is connected (linked) to the subsequent node on the list with a link (reference).

One of the best real-life applications of linked lists is multiplayer games, where the lists are used to keep track of each player’s turn. You also use linked lists when viewing images and pressing right or left arrows to go to the next/previous image.

Stacks

The basic principles behind stacks are LIFO (last in, first out) or FILO (first in, last out). These data structures stick to a specific order of operations and entering and retrieving information can be done only from one end. Stacks can be implemented through linked lists or arrays and are parts of many algorithms.

With stacks, you can evaluate and convert arithmetic expressions, check parentheses, process function calls, undo/redo your actions in a word processor, and much more.

Queues

In these linear structures, the principle is FIFO (first in, first out). The data the program stores first will be the first to process. You could say queues work on a first-come, first-served basis. Unlike stacks, queues aren’t limited to entering and retrieving information from only one end. Queues can be implemented through arrays, linked lists, or stacks.

There are three types of queues:

  • Simple
  • Circular
  • Priority

You use these data structures for job scheduling, CPU scheduling, multiple file downloading, and transferring data.

Non-Linear Data Structures

Non-linear and linear data structures are two diametrically opposite concepts. With non-linear structures, you don’t have elements arranged sequentially. This means there isn’t a single sequence that connects all elements. In this case, you have elements that can have multiple paths to each other. As you can imagine, implementing non-linear data structures is no walk in the park. But it’s worth it. These structures allow multi-level storage (hierarchy) and offer incredible memory efficiency.

Here are three types of non-linear data structures we’ll cover:

  • Trees
  • Graphs
  • Hash tables

Trees

Naturally, trees have a tree-like structure. You start at the root node, which is divided into other nodes, and end up with leaf modes. Every node has one “parent” but can have multiple “children,” depending on the structure. All nodes contain some type of data.

Tree structures provide easier access to specific data and guarantee efficiency.

Three structures are often used in game development and indexing databases. You’ll also use them in machine learning, particularly decision analysis.

Graphs

The two most important elements of every graph are vertices (nodes) and edges. A graph is essentially a finite collection of vertices connected by edges. Although they may look simple, graphs can handle the most complex tasks. They’re used in operating systems and the World Wide Web.

You unconsciously use graphs with Google Maps. When you want to know the directions to a specific location, you enter it in the map. At that point, the location becomes the node, and the path that guides you is the edge.

Hash Tables

With hash tables, you store information in an associative manner. Every data value gets its unique index value, meaning you can quickly find exactly what you’re looking for.

This may sound complex, so let’s check out a real-life example. Think of a library with over 30,000 books. Every book gets a number, and the librarian uses this number when trying to locate it or learn more details about it.

That’s exactly how hash tables work. They make the search process and insertion much faster, which is why they have a wide array of applications.

Specialized Data Structures

When data structures can’t be classified as either linear or non-linear, they’re called specialized data structures. These structures have unique applications and principles and are used to represent specialized objects.

Here are three examples of these structures:

  • Trie
  • Bloom Filter
  • Spatial Data

Trie

No, this isn’t a typo. “Trie” is derived from “retrieval,” so you can guess its purpose. A trie stores data which you can represent as graphs. It consists of nodes and edges, and every node contains a character that comes after the word formed by the parent node. This means that a key’s value is carried across the entire trie.

Bloom Filter

A bloom filter is a probabilistic data structure. You use it to analyze a set and investigate the presence of a specific element. In this case, “probabilistic” means that the filter can determine the absence but can result in false positives.

Spatial Data Structures

These structures organize data objects by position. As such, they have a key role in geographic systems, robotics, and computer graphics.

Choosing the Right Data Structure

Data structures can have many benefits, but only if you choose the right type for your needs. Here’s what to consider when selecting a data structure:

  • Data size and complexity – Some data structures can’t handle large and/or complex data.
  • Access patterns and frequency – Different structures have different ways of accessing data.
  • Required data structure operations and their efficiency – Do you want to search, insert, sort, or delete data?
  • Memory usage and constraints – Data structures have varying memory usages. Plus, every structure has limitations you’ll need to get acquainted with before selecting it.

Jump on the Data Structure Train

Data structures allow you to organize information and help you store and manage it. The mechanisms behind data structures make handling vast amounts of data much easier. Whether you want to visualize a real-world challenge or use structures in game development, image viewing, or computer sciences, they can be useful in various spheres.

As the data industry is evolving rapidly, if you want to stay in the loop with the latest trends, you need to be persistent and invest in your knowledge continuously.

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How Regenerative Business Models Are Redefining Innovation and Sustainability
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Aug 18, 2025 6 min read

Open Institute of Technology (OPIT) masterclasses bring students face-to-face with real-world business challenges. In OPIT’s July masterclass, OPIT Professor Francesco Derchi and Ph.D. candidate Robert Mario de Stefano explained the principles of regenerative businesses and how regeneration goes hand in hand with growth.

Regenerative Business Models

Professor Derchi began by explaining what exactly is meant by regenerative business models, clearly differentiating them from sustainable or circular models.

Many companies pursue sustainable business models in which they offset their negative impact by investing elsewhere. For example, businesses that are big carbon consumers will support nature regeneration projects. Circular business models are similar but are more focused on their own product chain, aiming to minimize waste by keeping products in use as long as possible through recycling. Both models essentially aim to have a “net-zero” negative impact on the environment.

Regenerative models are different because they actively aim to have a “net-positive” impact on the environment, not just offsetting their own use but actively regenerating the planet.

Massive Transformative Purpose

While regenerative business models are often associated with philanthropic endeavors, Professor Derchi explained that they do not have to be, and that investment in regeneration can be a driver of growth.

He discussed the importance of corporate purpose in the modern business space. Having a strong and clearly stated corporate purpose is considered essential to drive business decision-making, encourage employee buy-in, and promote customer loyalty.

But today, simple corporate missions, such as “make good shoes,” don’t go far enough. People are looking for a Massive Transformational Purpose (MTP) that can take the business to the next level.

Take, for example, Ben & Jerry’s. The business’s initial corporate purpose may have been to make great ice cream and serve it up in a way that people will enjoy. But the business really began to grow when they embraced an MTP. As they announced in their mission statement, “We believe that ice cream can change the world.” Their business activities also have the aim of advancing human rights and dignity, supporting social and economic justice, and protecting and restoring the Earth’s natural systems. While these aims are philanthropic, they have also helped the business grow.

RePlanet

Professor Derchi next talked about RePlanet, a business he recently worked to develop their MTP. Founded in 2015, RePlanet designs and implements customized renewable energy solutions for businesses and projects. The company already operates in the renewable energy field and ranked as the 21st fastest-growing business in Italy in 2023. So while they were already enjoying great success, Derchi worked with them to see if actively embracing a regenerative business model could unlock additional growth.

Working together, RePlanet moved towards an MTP of building a greener future based on today’s choices, ensuring a cleaner world for generations. Meeting this goal started with the energy products that RePlanet sells, such as energy systems that recover heat from dairy farms. But as the business’s MTP, it goes beyond that. RePlanet doesn’t just engage suppliers; it chooses partners that share its specific values. It also influences the projects they choose to work on – they prioritize high-impact social projects, such as recently installing photovoltaic energy systems at a local hospital in Nigeria – and how RePlanet treats its talent, acknowledging that people are the true energy of the company.

Regenerative Business Strategies

Based on work with RePlanet and other businesses, Derchi has identified six archetypal regenerative business strategies for businesses that want to have both a regenerative impact and drive growth:

  • Regenerative Leadership – Laying the foundation for regeneration in a broader sense throughout the company
  • Nature Regeneration – Strategies to improve the health of the natural world
  • Social Regeneration – Regenerating human ecosystems through things such as fair-trade practices
  • Responsible Sourcing – Empowering and strengthening suppliers and their communities
  • Health & Well-being – Creating products and services that have a positive effect on customers
  • Employee Focus – Improve work conditions, lives, and well-being of employees.

Case Studies

Building on the concept of regenerative business models, Roberto Mario de Stefano shared other case studies of businesses that are having a positive impact and enjoying growth thanks to regenerative business models and strategies.

Biorfarm

Biorfarm is a digital platform that supports small-scale agriculture by creating a direct link between small farmers and consumers. Cutting out the middleman in modern supply chains means that farmers earn about 50% more for their produce. They set consumers up as “digital farmers” who actively support and learn about farming activities to promote more conscious food consumption.

Their vision is to create a food economy in which those who produce food and those who consume it are connected. This moves consumers from passive cash cows for large corporations that prioritize profits over the well-being of farmers to actively supporting natural production and a more sustainable system.

Rifo Lab

Rifo Lab is a circular clothing brand with the vision of addressing the problem of overproduction in the clothing industry. Established in Prato, Italy, a traditional textile-producing area, the company produces clothes made from textile waste and biodegradable materials. There are no physical stores, and all orders must be placed online; everything is made to order, reducing excess production.

With an eye on social regeneration, all production takes place within 30 kilometers of their offices, allowing the business to support ethical and local production. They also work with companies that actively integrate migrants into the local community, sharing their local artisan crafts with future generations.

Ogyre

Ogyre is a digital platform that allows you to pay fishermen to fish for waste. When fishermen are out conducting their livelihood, they also collect a significant amount of waste from the ocean, especially plastic waste. Ogyre arranges for fishermen to get paid for collecting that waste, which in turn supports the local fishing communities, and then transforms the waste collected into new sustainable products.

Moving Towards a Regenerative Future

The masterclass concluded with a Q&A session, where it explained that working in regenerative businesses requires the same skills as any other business. But it also requires you to embrace a mindset where value comes from giving and that growth is about working together for a better future, and not just competition.

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Addressing the Skills Gap: OPIT Prepares Students for the Modern Job Market
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Aug 18, 2025 5 min read

Riccardo Ocleppo’s vision for the Open Institute of Technology (OPIT) started when he realized that his own university-level training had not properly prepared him for the modern workplace. Technological innovation is moving quickly and changing the nature of work, while university curricula evolve slowly, in part due to systems in place designed to preserve the quality of courses.

Ocleppo was determined to create a higher learning institution that filled the gap between the two realities – delivering high-quality education while preparing professionals to work in dynamic environments that keep pace with technology. Thus, OPIT opened enrolments in 2023 with a curriculum that created a unique bridge between the present and the future.

This is the story of one student, Ania Jaca, whose time at OPIT gave her the skills to connect her knowledge of product design to full system deployment.

Meet Ania

Ania is an example of an active professional who was able to identify what was missing in her own skills that would be needed if she wanted to advance her career in the direction she desired.

Ania is a highly skilled professional who was working on product and industrial design at Deloitte. She has an MA in product design, speaks five languages, studied in China, and is an avid boxer. She had the intelligence and the temperament to succeed in her career, but felt that she lacked the skills to advance and move from determining how products look to how systems really work, scale, and evolve.

Ania taught herself skills such as Python, artificial intelligence (AI), and cloud infrastructure, but soon realized that she needed a more structured education to go deeper. Thus, the search for her next steps began, and her introduction to OPIT.

OPIT appealed to Ania because it offered a fully EU-accredited MSc that she could pursue at her own pace, thanks to remote delivery and flexible hours. But more than that, it filled exactly the knowledge gap she was looking to build upon, teaching her technical foundations, but always with a focus on applications in the real world. Part of the appeal was the faculty, which includes professionals who are leaders in their field and who deal with current professional challenges on a daily basis, which they can bring into the classroom.

Ania enrolled in OPIT’s MSc in Applied Data Science & AI.

MSc in Applied Data Science and AI

This is OPIT’s first master’s program, which also launched in 2023, and is now one of four on offer. The course is designed for graduates like Ania who want a career at the intersection of management and technology. It is attractive to professionals who are already working in this area but lack the technical training to step into certain roles. OPIT requires no computer science prerequisites, so it accepted Ania with her MA in product design.

It is an intensive program that starts with foundational application courses in business, data science, machine learning, artificial intelligence, and problem-solving. The program then moves towards applying data science and AI methodologies and tools to real-life business problems.

The course combines theoretical study with a capstone project that lets students apply what they learn in the real world, either at their existing company or through internship programs. Many of the projects developed by students go on to become fundamental to the businesses they work with.

Ania’s Path Forward

Ania is working on her capstone project with Neperia Group, an Italian-based IT systems development company that works mostly with financial, insurance, and industrial companies. They specialize in developing analysis tools for existing software to enhance insight, streamline management, minimize the impact of corrective and evolutionary interventions, and boost performance.

Ania is specifically working on tools for assessing vulnerabilities in codebases as an advanced cybersecurity tool.

Ania credits her studies at OPIT for helping her build solid foundations in data science, machine learning, and cloud workflows, giving her a thorough understanding of digital products from end to end. She feels this has prepared her for roles at the intersection between infrastructure, security, and deployment, which is exactly where she wants to be. OPIT is excited to see where Ania’s career takes her in the coming years.

Preparing for the Future of Work

Overall, studying at OPIT has helped Ania and others like her prepare for the future of work. According to the Visual Capitalist, the fastest-growing jobs between 2025 and 2030 will be in big data (up by 110%), Fintech engineers (up by 95%), AI and machine learning specialists (up by 85%), software application developers (up by 60%), and security management specialists (up by 55%).

However, while these industries are growing, entry-level opportunities are declining in areas such as software development and IT. This is because AI now performs many of the tasks associated with those roles. Instead, companies are looking for experienced professionals to take on roles that involve more strategic oversight and innovative problem-solving. But how do recent graduates leapfrog past experienced professionals when there is a lack of entry-level positions to make the transition?

This is another challenge that OPIT addresses in its course design. Students don’t just learn the theory, OPIT actively encourages them to focus on applications, allowing them to build experience while studying. The capstone project consolidates this, enabling students to demonstrate to future employers their expertise at deploying technology to solve problems.

OPIT also has a dynamic Career Services department that specifically works with students to prepare them for the types of roles they want. This focus on not only learning but building a career is one of the elements that makes OPIT stand out in preparing graduates for the workplace.

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