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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Il Sole 24 Ore: 100 thousand IT professionals missing
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
May 14, 2024 6 min read

Written on April 24th 2024

Source here: Il Sole 24 Ore (full article in Italian)


Open Institute of Technology: 100 thousand IT professionals missing

Eurostat data processed and disseminated by OPIT. Stem disciplines: the share of graduates in Italy between the ages of 20 and 29 is 18.3%, compared to the European 21.9%

Today, only 29% of young Italians between 25 and 34 have a degree. Not only that: compared to other European countries, the comparison is unequal given that the average in the Old Continent is 46%, bringing Italy to the penultimate place in this ranking, ahead only of Romania. The gap is evident even if the comparison is limited to STEM disciplines (science, technology, engineering and mathematics) where the share of graduates in Italy between the ages of 20 and 29 is 18.3%, compared to the European 21.9%, with peaks of virtuosity which in the case of France that reaches 29.2%. Added to this is the continuing problem of the mismatch between job supply and demand, so much so that 62.8% of companies struggle to find professionals in the technological and IT fields.

The data

The Eurostat data was processed and disseminated by OPIT – Open Institute of Technology. an academic institution accredited at European level, active in the university level education market with online Bachelor’s and Master’s degrees in the technological and digital fields. We are therefore witnessing a phenomenon with worrying implications on the future of the job market in Italy and on the potential loss of competitiveness of our companies at a global level, especially if inserted in a context in which the macroeconomic scenario in the coming years will undergo a profound discontinuity linked to the arrival of “exponential” technologies such as Artificial Intelligence and robotics, but also to the growing threats related to cybersecurity.

Requirements and updates

According to European House Ambrosetti, over 2,000,000 professionals will have to update their skills in the Digital and IT area by 2026, also to take advantage of the current 100,000 vacant IT positions, as estimated by Frank Recruitment Group. But not only that: the Italian context, which is unfavorable for providing the job market with graduates and skills, also has its roots in the chronic birth rate that characterizes our country: according to ISTAT data, in recent years the number of newborns has fallen by 28%, bringing Italy’s birth rate to 1.24, among the lowest in Europe, where the average is 1.46.

Profumo: “Structural deficiency”

“The chronic problem of the absence of IT professionals is structural and of a dual nature: on one hand the number of newborns – therefore, potential “professionals of the future” – is constantly decreasing; on the other hand, the percentage of young people who acquires degrees are firmly among the lowest in Europe”, declared Francesco Profumo, former Minister of Education and rector of OPIT – Open Institute of Technology. “The reasons are varied: from the cost of education (especially if undertaken off-site), to a university offering that is poorly aligned with changes in society, to a lack of awareness and orientation towards STEM subjects, which guarantee the highest employment rates. Change necessarily involves strong investments in the university system (and, in general, in the education system) at the level of the country, starting from the awareness that a functioning education system is the main driver of growth and development in the medium to long term. It is a debated and discussed topic on which, however, a clear and ambitious position is never taken.”

Stagnant context and educational offer

In this stagnant context, the educational offer that comes from online universities increasingly meets the needs of flexibility, quality and cost of recently graduated students, university students looking for specialization and workers interested in updating themselves with innovative skills. According to data from the Ministry of University and Research, enrollments in accredited online universities in Italy have grown by over 141 thousand units in ten years (since 2011), equal to 293.9%. Added to these are the academic institutions accredited at European level, such as OPIT, whose educational offering is overall capable of opening the doors to hundreds of thousands of students, with affordable costs and extremely innovative and updated degree paths.

Analyzing the figures

An analysis of Eurostat statistics relating to the year 2021 highlights that 27% of Europeans aged between 16 and 74 have attended an entirely digital course. The highest share is recorded in Ireland (46%), Finland and Sweden (45%) and the Netherlands (44%). The lowest in Romania (10%), Bulgaria (12%) and Croatia (18%). Italy is at 20%. “With OPIT” – adds Riccardo Ocleppo, founder and director – “we have created a new model of online academic institution, oriented towards new technologies, with innovative programs, a strong practical focus, and an international approach, with professors and students from 38 countries around the world, and teaching in English. We intend to train Italian students not only on current and updated skills, but to prepare them for an increasingly dynamic and global job market. Our young people must be able to face the challenges of the future like those who study at Stanford or Oxford: with solid skills, but also with relational and attitudinal skills that lead them to create global companies and startups or work in multinationals like their international colleagues. The increasing online teaching offer, if well structured and with quality, represents an incredible form of democratization of education, making it accessible at low costs and with methods that adapt to the flexibility needs of many working students.”

Point of reference

With two degrees already starting in September 2023 – a three-year degree (BSc) in Modern Computer Science and a specialization (MSc) in Applied Data Science & AI – and 4 starting in September 2024: a three-year degree (BSc) in Digital Business, and the specializations (MSc) in Enterprise Cybersecurity, Applied Digital Business and Responsible Artificial Intelligence (AI), OPIT is an academic institution of reference for those who intend to respond to the demands of a job market increasingly oriented towards the field of artificial intelligence. Added to this are a high-profile international teaching staff and an exclusively online educational offer focused on the technological and digital fields.

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Times of India: The 600,000 IT job shortage in India and how to solve it
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
May 2, 2024 3 min read

Written on April 25th 2024

Source here: Times of India 


The job market has never been a straightforward path. Ask anyone who has ever looked for a job, certainly within the last decade, and they can tell you as much. But with the rapid development of AI and machine learning, concerns are growing for people about their career options, with a report from Randstad finding that 7 in 10 people in India are concerned about their job being eliminated by AI.

 Employers have their own share of concerns. According to The World Economic Forum, 97 million new AI-related jobs will be created by 2025 and the share of jobs requiring AI skills will increase by 58%. The IT industry in India is experiencing a tremendous surge in demand for skilled professionals on disruptive technologies like artificial intelligence, machine learning, blockchain, cybersecurity and, according to Nasscom, this is leading to a shortage of 600,000 profiles.

 So how do we fill those gaps? Can we democratize access to top-tier higher education in technology?

These are the questions that Riccardo Ocleppo, the engineer who founded a hugely successful ed-tech platform connecting international students with global Universities, Docsity, asked himself for years. Until he took action and launched the Open Institute of Technology (OPIT), together with the Former Minister of Education of Italy, Prof. Francesco Profumo, to help people take control of their future careers.

OPIT offers BSc and MSc degrees in Computer Science, AI, Data Science, Cybersecurity, and Digital Business, attracting students from over 38 countries worldwide. Through innovative learning experiences and affordable tuition fees starting at €4,050 per year, OPIT empowers students to pursue their educational goals without the financial and personal burden of relocating.

The curriculum, delivered through a mix of live and pre-recorded lectures, equips students with the latest technology skills, as well as business and strategic acumen necessary for careers in their chosen fields. Moreover, OPIT’s EU-accredited degrees enable graduates to pursue employment opportunities in Europe, with recognition by WES facilitating transferability to the US and Canada.

OPIT’s commitment to student success extends beyond academics, with a full-fledged career services department led by Mike McCulloch. Remote students benefit from OPIT’s “digital campus,” fostering connections through vibrant discussion forums, online events, and networking opportunities with leading experts and professors.

Faculty at OPIT, hailing from prestigious institutions and industry giants like Amazon and Microsoft, bring a wealth of academic and practical experience to the table. With a hands-on, practical teaching approach, OPIT prepares students for the dynamic challenges of the modern job market.

In conclusion, OPIT stands as a beacon of hope for individuals seeking to future-proof their careers in technology. By democratizing access to high-quality education and fostering a global learning community, OPIT empowers students to seize control of their futures and thrive in the ever-evolving tech landscape.

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