

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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Source:
- Metro, published on October 09th, 2025
After ChatGPT came on the scene in 2022, the tech industry quickly began comparing the arrival of AI to the dawn of the internet in the 1990s.
Back then, dot-com whizzes were minting easy millions only for the bubble to burst in 2000 when interest rates were hiked. Investors sold off their holdings, companies went bust and people lost their jobs.
Now central bank officials are worried that the AI industry may see a similar boom and bust.
A record of the Financial Policy Committee’s October 2 meeting shows officials saying financial market evaluations of AI ‘appear stretched’.
‘This, when combined with increasing concentration within market indices, leaves equity markets particularly exposed should expectations around the impact of AI become less optimistic,’ they added.
AI-focused stocks are mainly in US markets but as so many investors across the world have bought into it, a fallout would be felt globally.
ChatGPT creator OpenAI, chip-maker Nvidia and cloud service firm Oracle are among the AI poster companies being priced big this year.
Earnings are ‘comparable to the peak of the dot-com bubble’, committee members said.
Factors like limited resources – think power-hungry data centres, utilities and software that companies are spending billions on – and the unpredictability of the world’s politics could lead to a drop in stock prices, called a ‘correction’.
In other words, the committee said, investors may be ignoring how risky AI technology is.
Metro spoke with nearly a dozen financial analysts, AI experts and stock researchers about whether AI will suffer a similar fate. There were mixed feelings.
‘Every bubble starts with a story people want to believe,’ says Dat Ngo, of the trading guide, Vetted Prop Firms.
‘In the late 90s, it was the internet. Today, it’s artificial intelligence. The parallels are hard to ignore: skyrocketing stock prices, endless hype and companies investing billions before fully proving their business models.
‘The Bank of England’s warning isn’t alarmist – it’s realistic. When too much capital chases the same dream, expectations outpace results and corrections follow.’
Dr Alessia Paccagnini, an associate Professor from the University College Dublin’s Michael Smurfit Graduate Business School, says that companies are spending £300billion annually on AI infrastructure, while shoppers are spending $12billion. That’s a big difference.
Tech firms listed in the US now represent 30% of New York’s stock index, S&P 500 Index, the highest proportion in 50 years.
‘As a worst-case scenario, if the bubble does burst, the immediate consequences would be severe – a sharp market correction could wipe trillions from stock valuations, hitting retirement accounts and pension funds hard,’ Dr Paccagnini adds.
‘In my opinion, we should be worried, but being prepared could help us avoid the worst outcomes.’
One reason a correction would be so bad is because of how tangled-up the AI world is, says George Sweeney, an investing expert at the personal finance website site Finder.
‘If it fails to meet the lofty expectations, we could see an almighty unravelling of the AI hype that spooks markets, leading to a serious correction,’ he says.
Despite scepticism, AI feels like it’s everywhere these days, from dog bowls and fridges to toothbrushes and bird feeders.
And it might continue that way for a while, even if not as enthusiastically as before, says Professor Filip Bialy, who specialises in computer science and AI ethics at the at Open Institute of Technology.
‘TAI hype – an overly optimistic view of the technological and economic potential of the current paradigm of AI – contributes to the growth of the bubble,’ he says.
‘However, the hype may end not with the burst of the bubble but rather with a more mature understanding of the technology.’
Some stock researchers worry that the AI boom could lose steam when the companies spending billions on the tech see profits dip.
The AI analytic company Qlik found that only one in 10 business say their AI initiatives are seeing sizeable returns.
Qlik’s chief strategy officer, James Fisher, says this doesn’t show that the hype for AI is bursting, ‘but how businesses look at AI is changing’.

OPIT – Open Institute of Technology offers an innovative and exciting way to learn about technology. It offers a range of bachelor’s and master’s programs, plus a Foundation Year program for those taking the first steps towards higher education. Through its blend of instruction-based and independent learning, it empowers ambitious minds with the skills and knowledge needed to succeed.
This guide covers all you need to know to join OPIT and start your educational journey.
Introducing the Open Institute of Technology
Before we dig into the nitty-gritty of the OPIT application process, here’s a brief introduction to OPIT.
OPIT is a fully accredited Higher Education Institution under the European Qualification Framework (EQF) and the MFHEA Authority. It offers exclusively online education in English to an international community of students. With a winning team of top professors and a specific focus on computer science, it trains the technology leaders of tomorrow.
Some of the unique elements that characterize OPIT’s approach include:
- No final exams. Instead, students undergo progressive assessments over time
- A job-oriented, practical focus on the courses
- 24/7 support, including AI assistance and student communities, so everyone feels supported
- A strong network of company connections, unlocking doors for graduates
Reasons to Join OPIT
There are many reasons for ambitious students and aspiring tech professionals to study with OPIT.
Firstly, since all the study takes place online, it’s a very flexible and pleasant way to learn. Students don’t feel the usual pressures or suffer the same constraints they would at a physical college or university. They can attend from anywhere, including their own homes, and study at a pace that suits them.
OPIT is also a specialist in the technology field. It only offers courses focused on tech and computer science, with a team of professors and tutors who lead the way in these topics. This ensures that students get high-caliber learning opportunities in this specific sector.
Learning at OPIT is also hands-on and applicable to real-world situations, despite taking place online. Students are not just taught core skills and knowledge, but are also shown how to apply those skills and knowledge in their future careers.
In addition, OPIT strives to make technology education as accessible, inclusive, and affordable as possible. Entry requirements are relatively relaxed, fees are fair, and students from around the world are welcome here.
What You Need to Know About Joining OPIT
Now you know why it’s worth joining OPIT, let’s take a closer look at how to go about it. The following sections will cover how to apply to OPIT, entry requirements, and fees.
The OPIT Application Process
Unsurprisingly for an online-only institution, the application process for OPIT is all online, too. Users can submit the relevant documents and information on their computers from the comfort of their homes.
- Visit the official OPIT site and click the “Apply now” button to get started, filling out the relevant forms.
- Upload your supporting documents. These can include your CV, as well as certificates to prove your past educational accomplishments and level of English.
- Take part in an interview. This should last no more than 30 minutes. It’s a chance for you to talk about your ambitions and background, and to ask questions you might have about OPIT.
That’s it. Once you complete the above steps, you will be admitted to your chosen course and can start enjoying OPIT education once the first term begins. You’ll need to sign your admissions contract and pay the relevant fees, then begin classes.
Entry Requirements for OPIT Courses
OPIT offers a small curated collection of courses, each with its own requirements. You can consult the relevant pages on the official OPIT site to find out the exact details.
For the Foundation Program, for example, you simply need an MQF/EQF Level 3 or equivalent qualification. You also need to demonstrate a minimum B2 level of English comprehension.
For the BSc in Digital Business, applicants should have a higher secondary school leaving certificate, plus B2-level English comprehension. You can also support your application with a credit transfer from previous studies or relevant work experience.
Overall, the requirements are simple, and it’s most important for applicants to be ambitious and eager to build successful careers in the world of technology. Those who are driven and committed will get the best from OPIT’s instruction.
Fees and Flexible Payments at OPIT
As mentioned above, OPIT makes technological education accessible and affordable for all. Its tuition fees cover all relevant teaching materials, and there are no hidden costs or extras. The institute also offers flexible payment options for those with different budgets.
Again, exact fees vary depending on which course you want to take, so it’s important to consult the specific info for each one. You can pay in advance to enjoy 10% off the final cost, or refer a friend to also obtain a discount.
In addition to this, OPIT offers need-based and merit-based scholarships. Successful candidates can obtain discounts of up to 40% on bachelor’s and master’s tuition fees. This can substantially bring the term cost of each program down, making OPIT education even more accessible.
Credit Transfers and Experience
Those who are entering OPIT with pre-existing work experience or relevant academic achievements can benefit from the credit transfer program. This allows you to potentially skip certain modules or even entire semesters if you already have relevant experience in those fields.
OPIT is flexible and fair in terms of recognizing prior learning. So, as long as you can prove your credentials and experience, this could be a beneficial option for you. The easiest way to find out more and get started is to email the OPIT team directly.
Join OPIT Today
Overall, the process to join OPIT is designed to be as easy and stress-free as possible. Everything from the initial application forms to the interview and admission process is straightforward. Requirements and fees are flexible, so people in different situations and from different backgrounds can get the education they want. Reach out to OPIT today to take your first steps to tech success.
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