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 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.)
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 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 (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 aims to accurately represent data using the smallest storage amount possible. But without data structures, there wouldn’t be data compression algorithms.
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 (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.
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 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.
Soon, we will be launching four new Degrees for AY24-25 at OPIT – Open Institute of Technology
I want to offer a behind-the-scenes look at the Product Definition process that has shaped these upcoming programs.
🚀 Phase 1: Discovery (Late May – End of July)
Our journey began with intensive brainstorming sessions with OPIT’s Academic Board (Francesco Profumo, Lorenzo Livi, Alexiei Dingli, Andrea Pescino, Rosario Maccarrone) . We also conducted 50+ interviews with tech and digital entrepreneurs (both from startups and established firms), academics and students. Finally, we deep-dived into the “Future of Jobs 2023” report by the World Economic Forum and other valuable research.
🔍 Phase 2: Selection – Crafting Our Roadmap (July – August)
Our focus? Introducing new degrees addressing critical workforce shortages and upskilling/reskilling needs for the next 5-10 years, promising significant societal impact and a broad market reach.
Our decision? To channel our energies on full BScs and MScs, and steer away from shorter courses or corporate-focused offerings. This aligns perfectly with our core mission.
💡 Focus Areas Unveiled!
We’re thrilled to concentrate on pivotal fields like:
- Advanced AI
- Digital Business
- Metaverse & Gaming
- Cloud Computing (less “glamorous”, but market demand is undeniable).
🎓 Phase 3: Definition – Shaping the Degrees (August – November)
With an expert in each of the above fields, and with the strong collaboration of our Academic Director, Prof. Lorenzo Livi , we embarked on a rigorous “drill-down process”. Our goal? To meld modern theoretical knowledge with cutting-edge competencies and skills. This phase included interviewing over 60+ top academics, industry professionals, and students and get valuable, program-specific, insights from our Marketing department.
🌟 Phase 4: Accreditation and Launch – The Final Stretch
We’re currently in the accreditation process, gearing up for the launch. The focus is now shifting towards marketing, working closely with Greta Maiocchi and her Marketing and Admissions team. Together, we’re translating our new academic offering into a compelling value proposition for the market.
Stay tuned for more updates!
Far from being a temporary educational measure that came into its own during the pandemic, online education is providing students from all over the world with new ways to learn. That’s proven by statistics from Oxford Learning College, which point out that over 100 million students are now enrolled in some form of online course.
The demand for these types of courses clearly exists.
In fact, the same organization indicates that educational facilities that introduce online learning see a 42% increase in income – on average – suggesting that the demand is there.
Enter the Open Institute of Technology (OPIT).
Delivering three online courses – a Bachelor’s degree in computer science and two Master’s degrees – with more to come, OPIT is positioning itself as a leader in the online education space. But why is that? After all, many institutions are making the jump to e-learning, so what separates OPIT from the pack?
Here, you’ll discover the answers as you delve into the five reasons why you should trust OPIT for your online education.
Reason 1 – A Practical Approach
OPIT focuses on computer science education – a field in which theory often dominates the educational landscape. The organization’s Rector, Professor Francesco Profumo, makes this clear in a press release from June 2023. He points to a misalignment between what educators are teaching computer science students and what the labor market actually needs from those students as a key problem.
“The starting point is the awareness of the misalignment,” he says when talking about how OPIT structures its online courses. “That so-called mismatch is generated by too much theory and too little practical approach.” In other words, students in many classes spend far too much time learning the “hows” and “whys” behind computerized systems without actually getting their hands dirty with real work that gives them practical experience in using those systems.
OPIT takes a different approach.
It has developed a didactic approach that focuses far more on the practical element than other courses. That approach is delivered through a combination of classroom sessions – such as live lessons and masterclasses – and practical work offered through quizzes and exercises that mimic real-world situations.
An OPIT student doesn’t simply learn how computers work. They put their skills into practice through direct programming and application, equipping them with skills that are extremely attractive to major employers in the tech field and beyond.
Reason 2 – Flexibility Combined With Support
Flexibility in how you study is one of the main benefits of any online course.
You control when you learn and how you do it, creating an environment that’s beneficial to your education rather than being forced into a classroom setting with which you may not feel comfortable. This is hardly new ground. Any online educational platform can claim that it offers “flexibility” simply because it provides courses via the web.
Where OPIT differs is that it combines that flexibility with unparalleled support bolstered by the experiences of teachers employed from all over the world. The founder and director of OPIT, Riccardo Ocleppo, sheds more light on this difference in approach when he says, “We believe that education, even if it takes place physically at a distance, must guarantee closeness on all other aspects.” That closeness starts with the support offered to students throughout their entire study period.
Tutors are accessible to students at all times. Plus, every participant benefits from weekly professor interactions, ensuring they aren’t left feeling stuck on an educational “island” and have to rely solely on themselves for their education. OPIT further counters the potential isolation that comes with online learning with a Student Support team to guide students through any difficulties they may have with their courses.
In this focus on support, OPIT showcases one of its main differences from other online platforms.
You don’t simply receive course material before being told to “get on with it.” You have the flexibility to learn at your own pace while also having a support structure that serves as a foundation for that learning.
Reason 3 – OPIT Can Adapt to Change Quickly
The field of computer science is constantly evolving.
In the 2020s alone, we’ve seen the rise of generative AI – spurred on by the explosive success of services like ChatGPT – and how those new technologies have changed the way that people use computers.
Riccardo Ocleppo has seen the impact that these constant evolutions have had on students. Before founding OPIT, he was an entrepreneur who received first-hand experience of the fact that many traditional educational institutions struggle to adapt to change.
“Traditional educational institutions are very slow to adapt to this wave of new technologies and trends within the educational sector,” he says. He points to computer science as a particular issue, highlighting the example of a board in Italy of which he is a member. That board – packed with some of the country’s most prestigious tech universities – spent three years eventually deciding to add just two modules on new and emerging technologies to their study programs.
That left Ocleppo feeling frustrated.
When he founded OPIT, he did so intending to make it an adaptable institution in which courses were informed by what the industry needs. Every member of its faculty is not only a superb teacher but also somebody with experience working in industry. Speaking of industry, OPIT collaborates with major companies in the tech field to ensure its courses deliver the skills that those organizations expect from new candidates.
This confronts frustration on both sides. For companies, an OPIT graduate is one for which they don’t need to bridge a “skill gap” between what they’ve learned and what the company needs. For you, as a student, it means that you’re developing skills that make you a more desirable prospect once you have your degree.
Reason 4 – OPIT Delivers Tier One Education
Despite their popularity, online courses can still carry a stigma of not being “legitimate” in the face of more traditional degrees. Ocleppo is acutely aware of this fact, which is why he’s quick to point out that OPIT always aims to deliver a Tier One education in the computer science field.
“That means putting together the best professors who create superb learning material, all brought together with a teaching methodology that leverages the advancements made in online teaching,” he says.
OPIT’s degrees are all accredited by the European Union to support this approach, ensuring they carry as much weight as any other European degree. It’s accredited by both the European Qualification Framework (EQF) and the Malta Qualification Framework (MQF), with all of its courses having full legal value throughout Europe.
It’s also here where we see OPIT’s approach to practicality come into play via its course structuring.
Take its Bachelor’s degree in computer science as an example.
Yes, that course starts with a focus on theoretical and foundational knowledge. Building a computer and understanding how the device processes instructions is vital information from a programming perspective. But once those foundations are in place, OPIT delivers on its promises of covering the most current topics in the field.
Machine learning, cloud computing, data science, artificial intelligence, and cybersecurity – all valuable to employers – are taught at the undergraduate level. Students benefit from a broader approach to computer science than most institutions are capable of, rather than bogging them down in theory that serves little practical purpose.
Reason 5 – The Learning Experience
Let’s wrap up by honing in on what it’s actually like for students to learn with OPIT.
After all, as Ocleppo points out, one of the main challenges with online education is that students rarely have defined checkpoints to follow. They can start feeling lost in the process, confronted with a metaphorical ocean of information they need to learn, all in service of one big exam at the end.
Alternatively, some students may feel the temptation to not work through the materials thoroughly, focusing instead on passing a final exam. The result is that those students may pass, but they do so without a full grasp of what they’ve learned – a nightmare for employers who already have skill gaps to handle.
OPIT confronts both challenges by focusing on a continuous learning methodology. Assessments – primarily practical – take place throughout the course, serving as much-needed checkpoints for evaluating progress. When combined with the previously mentioned support that OPIT offers, this approach has led to courses that are created from scratch in service of the student’s actual needs.
Choose OPIT for Your Computer Science Education
At OPIT, the focus lies as much on helping students to achieve their dream careers as it does on teaching them. All courses are built collaboratively. With a dedicated faculty combined with major industry players, such as Google and Microsoft, it delivers materials that bridge the skill gap seen in the computer science field today.
There’s also more to come.
Beyond the three degrees OPIT offers, the institution plans to add more. Game development, data science, and cloud computing, to name a few, will receive dedicated degrees in the coming months, accentuating OPIT’s dedication to adapting to the continuous evolution of the computer science industry. Discover OPIT today – your journey into computing starts with the best online education institution available.