The larger your database, the higher the possibility of data repetition and inaccuracies that compromise the results you pull from the database. Normalization in DBMS exists to counteract those problems by helping you to create more uniform databases in which redundancies are less likely to occur.
Mastering normalization is a key skill in DBMS for the simple fact that an error-strewn database is of no use to an organization. For example, a retailer that has to deal with a database that has multiple entries for phone numbers and email addresses is a retailer that can’t see as effectively as one that has a simple route to the customer. Let’s look at normalization in DBMS and how it helps you to create a more organized database.
The Concept of Normalization
Grab a pack of playing cards and throw them onto the floor. Now, pick up the “Jack of Hearts.” It’s a tough task because the cards are strewn all over the place. Some are facing down and there’s no rhyme, reason, or pattern to how the cards lie, meaning you’re going to have to check every card individually to find the one you want.
That little experiment shows you how critical organization is, even with a small set of “data.” It also highlights the importance of normalization in DBMS. Through normalization, you implement organizational controls using a set of principles designed to achieve the following:
- Eliminate redundancy – Lower (or eliminate) occurrences of data repeating across different tables, or inside individual tables, in your DBMS.
- Minimize data anomalies – Better organization makes it easier to spot datasets that don’t fit the “norm,” meaning fewer anomalies.
- Improve data integrity – More accurate data comes from normalization controls. Database users can feel more confident in their results because they know that the controls ensure integrity.
The Process of Normalization
If normalization in DBMS is all about organization, it stands to reason that they would be a set process to follow when normalizing your tables and database:
- Decompose your tables – Break every table down into its various parts, which may lead to you creating several tables out of one. Through decomposition, you separate different datasets, eliminate inconsistencies, and set the stage for creating relationships and dependencies between tables.
- Identify functional dependencies – An attribute in one table may be dependent on another to exist. For example, a “Customer ID” number in a retailer’s “Customer” table is functionally dependent on the “Customer Name” field because the ID can’t exist without the customer. Identifying these types of dependencies ensures you don’t end up with empty records (such as a record with a “Customer ID” and no customer attached to it).
- Apply normalization rules – Once you’re broken down your table and identified the functional dependencies, you apply relevant normalization rules. You’ll use Normal Forms to do this, with the six highlighted below each having its own rules, structures, and use cases.
Normal Forms in DBMS
There isn’t a “single” way to achieve normalization in DBMS because every database (and the tables it contains) is different. Instead, there are six normal forms you may use, with each having its own rules that you need to understand to figure out which to apply.
First Normal Form (1NF)
If a relation can’t contain multiple values, it’s in 1NF. In other words, each attribute in the table can only contain a single (called “atomic”) value.
Example
If a retailer wants to store the details of its customers, it may have attributes in its table like “Customer Name,” “Phone Number,” and “Email Address.” By applying 1NF to this table, you ensure that the attributes that could contain multiple entries (“Phone Number” and “Email Address”) only contain one, making contacting that customer much simpler.
Second Normal Form (2NF)
A table that’s in 2NF is in 1NF, with the additional condition that none of its non-prime attributes depend on a subset of candidate keys within the table.
Example
Let’s say an employer wants to create a table that contains information about an employee, the skills they have, and their age. An employee may have multiple skills, leading to multiple records for the same employee in the table, with each denoting a skill while the ID number and age of the employee repeat for each record.
In this table, you’ve achieved 1NF because each attribute has an atomic value. However, the employee’s age is dependent on the employee ID number. To achieve 2NF, you’d break this table down into two tables. The first will contain the employee’s ID number and age, with that ID number linking to a second table that lists each of the skills associated with the employee.
Third Normal Form (3NF)
In 3NF, the table you have must already be in 2NF form, with the added rule of removing the transitive functional dependency of the non-prime attribute of any super key. Transitive functional dependency occurs if the dependency is the result of a pair of functional dependencies. For example, the relationship between A and C is a transitive dependency if A depends on B, B depends on C, but B doesn’t depend on A.
Example
Let’s say a school creates a “Students” table with the following attributes:
- Student ID
- Name
- Zip Code
- State
- City
- District
In this case, the “State,” “District,” and “City” attributes all depend on the “Zip Code” attribute. That “Zip” attribute depends on the “Student ID” attribute, making “State,” “District,” and “City” all transitively depending on “Student ID.”
To resolve this problem, you’d create a pair of tables – “Student” and “Student Zip.” The “Student” table contains the “Student ID,” “Name,” and “Zip Code” attributes, with that “Zip Code” attribute being the primary key of a “Student Zip” table that contains the rest of the attributes and links to the “Student” table.
Boyce-Codd Normal Form (BCNF)
Often referred to as 3.5NF, BCNF is a stricter version of 3NF. So, this normalization in DBMS rule occurs if your table is in 3NF, and for every functional dependence between two fields (i.e., A -> B), A is the super key of your table.
Example
Sticking with the school example, every student in a school has multiple classes. The school has a table with the following fields:
- Student ID
- Nationality
- Class
- Class Type
- Number of Students in Class
You have several functional dependencies here:
- Student ID -> Nationality
- Class -> Number of Students in Class, Class Type
As a result, both the “Student ID” and “Class” attributes are candidate keys but can’t serve as keys alone. To achieve BCNF normalization, you’d break the above table into three – “Student Nationality,” “Student Class,” and “Class Mapping,” allowing “Student ID” and “Class” to serve as primary keys in their own tables.
Fourth Normal Form (4NF)
In 4NF, the database must meet the requirements of BCNF, in addition to containing no more than a single multivalued dependency. It’s often used in academic circles, as there’s little use for 4NF elsewhere.
Example
Let’s say a college has a table containing the following fields:
- College Course
- Lecturer
- Recommended Book
Each of these attributes is independent of the others, meaning each can change without affecting the others. For example, the college could change the lecturer of a course without altering the recommended reading or the course’s name. As such, the existence of the course depends on both the “Lecturer” and “Recommended Book” attributes, creating a multivalued dependency. If a DBMS has more than one of these types of dependencies, it’s a candidate for 4NF normalization.
Fifth Normal Form (5NF)
If your table is in 4NF, has no join dependencies, and all joining is lossless, it’s in 5NF. Think of this as the final form when it comes to normalization in DBMS, as you’ve broken your table down so much that you’ve made redundancy impossible.
Example
A college may have a table that tells them which lecturers teach certain subjects during which semesters, creating the following attributes:
- Subject
- Lecturer Name
- Semester
Let’s say one of the lecturers teaches both “Physics” and “Math” for “Semester 1,” but doesn’t teach “Math” for Semester 2. That means you need to combine all of the fields in this table to get an accurate dataset, leading to redundancy. Add a third semester to the mix, especially if that semester has no defined courses or lecturers, and you have to join dependencies.
The 5NF solution is to break this table down into three tables:
- Table 1 – Contains the “Semester” and “Subject” attributes to show which subjects are taught in each semester.
- Table 2 – Contains the “Subject” and “Lecturer Name” attributes to show which lecturers teach a subject.
- Table 3 – Contains the “Semester” and “Lecturer Name” attributes so you can see which lecturers teach during which semesters.
Benefits of Normalization in DBMS
With normalization in DBMS being so much work, you need to know the following benefits to show that it’s worth your effort:
- Improved database efficiency
- Better data consistency
- Easier database maintenance
- Simpler query processing
- Better access controls, resulting in superior security
Limitations and Trade-Offs of Normalization
Normalization in DBMS does have some drawbacks, though these are trade-offs that you accept for the above benefits:
- The larger your database gets, the more demands it places on system performance.
- Breaking tables down leads to complexity.
- You have to find a balance between normalization and denormalization to ensure your tables make sense.
Practical Tips for Mastering Normalization Techniques
Getting normalization in DBMS is hard, especially when you start feeling like you’re dividing tables into so many small tables that you’re losing track of the database. These tips help you apply normalization correctly:
- Understand the database requirements – Your database exists for you to extract data from it, so knowing what you’ll need to extract indicates whether you need to normalize tables or not.
- Document all functional dependencies – Every functional dependence that exists in your database makes the table in which it exists a candidate for normalization. Identify each dependency and document it so you know whether you need to break the table down.
- Use software and tools – You’re not alone when poring through your database. There are plenty of tools available that help you to identify functional dependencies. Many make normalization suggestions, with some even being able to carry out those suggestions for you.
- Review and refine – Every database evolves alongside its users, so continued refining is needed to identify new functional dependencies (and opportunities for normalization).
- Collaborate with other professionals – A different set of eyes on a database may reveal dependencies and normalization opportunities that you don’t see.
Make Normalization Your New Norm
Normalization may seem needlessly complex, but it serves the crucial role of making the data you extract from your database more refined, accurate, and free of repetition. Mastering normalization in DBMS puts you in the perfect position to create the complex databases many organizations need in a Big Data world. Experiment with the different “normal forms” described in this article as each application of the techniques (even for simple tables) helps you get to grips with normalization.
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Read the articleAs one of the world’s fastest-growing industries, with a predicted compound annual growth rate of 16.43% anticipated between 2022 and 2030, data science is the ideal choice for your career. Jobs will be plentiful. Opportunities for career advancement will come thick and fast. And even at the most junior level, you’ll enjoy a salary that comfortably sits in the mid-five figures.
Studying for a career in this field involves learning the basics (and then the complexities) of programming languages including C+, Java, and Python. The latter is particularly important, both due to its popularity among programmers and the versatility that Python brings to the table. Here, we explore the importance of Python for data science and how you’re likely to use it in the real world.
Why Python for Data Science?
We can distill the reasons for learning Python for data science into the following five benefits.
Popularity and Community Support
Statista’s survey of the most widely-used programming languages in 2022 tells us that 48.07% of programmers use Python to some degree. Leftronic digs deeper into those numbers, telling us that there are 8.2 million Python developers in the world. As a prospective developer yourself, these numbers tell you two things – Python is in demand and there’s a huge community of fellow developers who can support you as you build your skills.
Easy to Learn and Use
You can think of Python as a primer for almost any other programming language, as it takes the fundamental concepts of programming and turns them into something practical. Getting to grips with concepts like functions and variables is simpler in Python than in many other languages. Python eventually opens up from its simplistic use cases to demonstrate enough complexity for use in many areas of data science.
Extensive Libraries and Tools
Given that Python was first introduced in 1991, it has over 30 years of support behind it. That, combined with its continued popularity, means that novice programmers can access a huge number of tools and libraries for their work. Libraries are especially important, as they act like repositories of functions and modules that save time by allowing you to benefit from other people’s work.
Integration With Other Programming Languages
The entire script for Python is written in C, meaning support for C is built into the language. While that enables easy integration between these particular languages, solutions exist to link Python with the likes of C++ and Java, with Python often being capable of serving as the “glue” that binds different languages together.
Versatility and Flexibility
If you can think it, you can usually do it in Python. Its clever modular structure, which allows you to define functions, modules, and entire scripts in different files to call as needed, makes Python one of the most flexible programming languages around.
Setting Up Python for Data Science
Installing Python onto your system of choice is simple enough. You can download the language from the Python.org website, with options available for everything from major operating systems (Windows, macOS, and Linux) to more obscure devices.
However, you need an integrated development environment (IDE) installed to start coding in Python. The following are three IDEs that are popular with those who use Python for data science:
- Jupyter Notebook – As a web-based application, Jupyter easily allows you to code, configure your workflows, and even access various libraries that can enhance your Python code. Think of it like a one-stop shop for your Python needs, with extensions being available to extend its functionality. It’s also free, which is never a bad thing.
- PyCharm – Where Jupyter is an open-source IDE for several languages, PyCharm is for Python only. Beyond serving as a coding tool, it offers automated code checking and completion, allowing you to quickly catch errors and write common code.
- Visual Studio Code – Though Visual Studio Code alone isn’t compatible with Python, it has an extension that allows you to edit Python code on any operating system. Its “Linting” feature is great for catching errors in your code, and it comes with an integrated debugger that allows you to test executables without physically running them.
Setting up your Python virtual environment is as simple as downloading and installing Python itself, and then choosing an IDE in which to work. Think of Python as the materials you use to build a house, with your IDE being both the blueprint and the tools you’ll need to patch those materials together.
Essential Python Libraries for Data Science
Just as you’ll go to a real-world library to check out books, you can use Python libraries to “check out” code that you can use in your own programs. It’s actually better than that because you don’t need to return libraries when you’re done with them. You get to keep them, along with all of their built-in modules and functions, to call upon whenever you need them. In Python for data science, the following are some essential libraries:
- NumPy – We spoke about integration earlier, and NumPy is ideal for that. It brings concepts of functionality from Fortran and C into Python. By expanding Python with powerful array and numerical computing tools, it helps transform it into a data science powerhouse.
- pandas – Manipulating and analyzing data lies at the heart of data sciences, and pandas give you a library full of tools to allow both. It offers modules for cleaning data, plotting, finding correlations, and simply reading CSV and JSON files.
- Matplotlib – Some people can look at reams of data and see patterns form within the numbers. Others need visualization tools, which is where Matplotlib excels. It helps you create interactive visual representations of your data for use in presentations or if you simply prefer to “see” your data rather than read it.
- Scikit-learn – The emerging (some would say “exploding) field of machine learning is critical to the AI-driven future we’re seemingly heading toward. Scikit-learn is a library that offers tools for predictive data analysis, built on what’s available in the NumPy and Matplotlib libraries.
- TensorFlow and Keras – Much like Scikit-learn, both TensorFlow and Keras offer rich libraries of tools related to machine learning. They’re essential if your data science projects take you into the realms of neural networks and deep learning.
Data Science Workflow in Python
A Python programmer without a workflow is like a ship’s captain without a compass. You can sail blindly onward, and you may even get lucky and reach your destination, but the odds are you’re going to get lost in the vastness of the programming sea. For those who want to use Python for data science, the following workflow brings structure and direction to your efforts.
Step 1 – Data Collection and Preprocessing
You need to collect, organize, and import your data into Python (as well as clean it) before you can draw any conclusions from it. That’s why the first step in any data science workflow is to prepare the data for use (hint – the pandas library is perfect for this task).
Step 2 – Exploratory Data Analysis (EDA)
Just because you have clean data, that doesn’t mean you’re ready to investigate what that data tells you. It’s like washing ingredients before you make a dish – you need to have a “recipe” that tells you how to put everything together. Data scientists use EDA as this recipe, allowing them to combine data visualization (remember – the Matplotlib library) with descriptive statistics that show them what they’re looking at.
Step 3 – Feature Engineering
This is where you dig into the “whats” and “hows” of your Python program. You’ll select features for the code, which define what it does with the data you import and how it’ll deliver outcomes. Scaling is a key part of this process, with scope creep (i.e., constantly adding features as you get deeper into a project) being the key thing to avoid.
Step 4 – Model Selection and Training
Decision trees, linear regression, logistic regression, neural networks, and support vector machines. These are all models (with their own algorithms) you can use for your data science project. This step is all about selecting the right model for the job (your intended features are important here) and training that model so it produces accurate outputs.
Step 5 – Model Evaluation and Optimization
Like a puppy that hasn’t been house trained, an unevaluated model isn’t ready for release into the real world. Classification metrics, such as a confusion matrix and classification report, help you to evaluate your model’s predictions against real-world results. You also need to tune the hyperparameters built into your model, similar to how a mechanic may tune the nuts and bolts in a car, to get everything working as efficiently as possible.
Step 6 – Deployment and Maintenance
You’ve officially deployed your Python for data science model when you release it into the wild and let it start predicting outcomes. But the work doesn’t end at deployment, as constant monitoring of what your model does, outputs, and predicts is needed to tell you if you need to make tweaks or if the model is going off the rails.
Real-World Data Science Projects in Python
There are many examples of Python for data science in the real world, some of which are simple while others delve into some pretty complex datasets. For instance, you can use a simple Python program to scrap live stock prices from a source like Yahoo! Finance, allowing you to create a virtual ticker of stock price changes for investors.
Alternatively, why not create a chatbot that uses natural language processing to classify and respond to text? For that project, you’ll tokenize sentences, essentially breaking them down into constituent words called “tokens,” and tag those tokens with meanings that you could use to prompt your program toward specific responses.
There are plenty of ideas to play around with, and Python is versatile enough to enable most, so consider what you’d like to do with your program and then go on the hunt for datasets. Great (and free) resources include The Boston House Price Dataset, ImageNet, and IMDB’s movie review database.
Try Python for Data Science Projects
By combining its own versatility with integrations and an ease of use that makes it welcoming to beginners, Python has become one of the world’s most popular programming languages. In this introduction to data science in Python, you’ve discovered some of the libraries that can help you to apply Python for data science. Plus, you have a workflow that lends structure to your efforts, as well as some ideas for projects to try. Experiment, play, and tweak models. Every minute you spend applying Python to data science is a minute spent learning a popular programming language in the context of a rapidly-growing industry.
Read the articleToday’s tech-driven world is governed by data – so much so that nearly 98% of all organizations are increasing investment in data.
However, company owners can’t put their feet up after improving their data capabilities. They also need a database management system (DBMS) – a program specifically designed for storing and organizing information efficiently.
When analyzing a DBMS, you need to be thorough like a detective investigating a crime. One of the elements you want to consider is DBMS architecture. It describes the structure of your database and how individual bits of information are related to each other. The importance of DBMS architecture is enormous, as it helps IT experts design and maintain fully functional databases.
But what exactly does a DBMS architecture involve? You’ll find out in this entry. Coming up is an in-depth discussion of database system concepts and architecture.
Overview of DBMS Architecture
Suppose you’re assembling your PC. You can opt for several configurations, such as those with three RAM slots and dual-fan coolers. The same principle applies to DBMS architectures.
Two of the most common architectures are three-level and two-level architectures.
Three-Level Architecture
Three-level architecture is like teacher-parent communication. More often than not, a teacher communicates with parents through children, asking them to convey certain information. In other words, there are layers between the two that don’t allow direct communication.
The same holds for three-level architecture. But instead of just one layer, there are two layers between the database and user: application client and application server.
And as the name suggests, a three-level DBMS architecture has three levels:
- External level – Also known as the view level, this section concerns the part of your database that’s relevant to the user. Everything else is hidden.
- Conceptual level – Put yourself in the position of a scuba diver exploring the ocean layer by layer. Once you reach the external level, you go one segment lower and find the conceptual level. It describes information conceptually and tells you how data segments interact with one another.
- Internal level – Another name for the internal level is the physical level. But what does it deal with? It mainly focuses on how data is stored in your system (e.g., using folders and files).
Two-Level Architecture
When you insert a USB into your PC, you can see the information on your interface. However, the source of the data is on the USB, meaning they’re separated.
Two-level architecture takes the same approach to separating data interface and data structure. Here are the two levels in this DBMS architecture:
- User level – Any application and interface in your database are stored on the user level in a two-level DBMS architecture.
- System level – The system level (aka server level) performs transaction management and other essential processes.
Comparison of the Two Architectures
Determining which architecture works best for your database is like buying a car. You need to consider how easy it is to use and the level of performance you can expect.
On the one hand, the biggest advantage of two-level architectures is that they’re relatively easy to set up. There’s just one layer between the database and the user, resulting in easier database management.
On the other hand, developing a three-level DBMS architecture may take a while since you need to include two layers between the database and the user. That said, three-level architectures are normally superior to two-level architectures due to higher flexibility and the ability to incorporate information from various sources.
Components of DBMS Architecture
You’ve scratched the surface of database system concepts and architecture, but don’t stop there. It’s time to move on to the basics to the most important elements of a DBMS architecture:
Data Storage
The fact that DBMS architectures have data storage solutions is carved in stone. What exactly are those solutions? The most common ones are as follows:
- Data files – How many files do you have on your PC? If it’s a lot, you’re doing exactly what administrators of DBMS architectures are doing. A large number of them store data in files, and each file is categorized into blocks.
- Indexes – You want your database operations to be like lightning bolts, i.e. super-fast. You can incorporate indexes to accomplish this goal. They point to data columns for quick retrieval.
- Data dictionary – Also known as system logs, data dictionaries contain metadata – information about your data.
Data Manipulation
A large number of companies still utilize manual data management methods. But using this format is like shooting yourself in the foot when there are advanced data manipulation methods are available. These allow you to process and retrieve data within seconds through different techniques:
- Query processor – Query processing refers to extracting data from your DBMS architecture. It operates like any other multi-stage process. It involves parsing, translation, optimization, and evaluation.
- Query optimizer – A DBMS architecture administrator can perform various query optimization tasks to achieve desired results faster.
- Execution engine – Whenever you want your architecture to do something, you send requests. But something needs to process the requests – that something is the execution engine.
Data Control
We’re continuing our journey through an average DBMS architecture. Our next stop is data control, which is comprised of these key elements:
- Transaction management – When carrying out multiple transactions, how does the system prioritize one over another? The answer lies in transaction management, which is also about processing multiple transactions side by side.
- Concurrency control – Database architecture is like an ocean teeming with life. Countless operations take place simultaneously. As a result, the system needs concurrency control to manage these concurrent tasks.
- Recovery management – What if your DBMS architecture fails? Do you give up on your project? No – the system has robust recovery management tools to retrieve your information and reduce downtime.
Database System Concepts
To give you a better understanding of a DBMS architecture, let’s describe the most important concepts regarding this topic.
Data Models
Data models do to information what your folders do to files – organize them. There are four major types of data models:
- Hierarchical model – Top-down and bottom-up storage solutions are known as hierarchical models. They’re characterized by tree-like structures.
- Network model – Hierarchical models are generally used for basic data relationships. If you want to analyze complex relationships, you need to kick things up a notch with network models. They enable you to represent huge quantities of complex information without a hitch.
- Relational model – Relations are merely tables with values. A relational model is a collection of these relations, indicating how data is connected to other data.
- Object-oriented model – Programming languages regularly use objects. An object-oriented model stores information as models and is usually more complex than other models.
Database Schema and Instances
Another concept you should familiarize yourself with is schemas and instances.
- Definition of schema and instance – Schemas are like summaries, providing a basic description of databases. Instances tell you what information is stored in a database.
- Importance of schema in DBMS architecture – Schemas are essential because they help organize data by providing a clear outline.
Data Independence
The ability of other pieces of information to remain unaffected after you change one bit of data is known as data independence. What are the different types of data independence, and what makes them so important?
- Logical data independence – If you can modify logical schemas without altering the rest of the system, your logical data is independent.
- Physical data independence – Physical data is independent if it remains unaffected when changing your hardware, such as SSD disks.
- Significance of data independence in DBMS architecture – Independent data is crucial for saving time in database management because it reduces the amount of information that needs to be processed.
Efficient Database Management Systems
Database management systems have a lot in common with other tech-based systems. For example, you won’t ignore problems that arise on your PC, be they CPU or graphics card issues. You’ll take action to optimize the performance of the device and solve those issues.
That’s exactly what 75% of developers and administrators of database management systems do. They go the extra mile to enhance the performance, scalability, flexibility, security, and integrity of their architecture.
Performance Optimization Techniques
- Indexing – By pointing to certain data in tables, indexes speed up database management.
- Query optimization – This process is about finding the most efficient method of executing queries.
- Caching – Frequently accessed information is cached to accelerate retrieval.
Scalability and Flexibility
- Horizontal scaling – Horizontal scaling involves increasing the number of servers.
- Vertical scaling – An administrator can boost the performance of the server to make the system more scalable.
- Distributed databases – Databases are like smartphones in that they can easily overload. Pressure can be alleviated with distributed databases, which store information in multiple locations.
Security and Integrity
- Access control – Restricting access is key to preventing cyber security attacks.
- Data encryption – Administrators often encrypt their DBMS architecture to protect sensitive information.
- Backup and recovery – A robust backup plan helps IT experts recover from shutdowns and other unforeseen problems.
Preparing for the Future Is Critical
DBMS architecture is the underlying structure of a database management system. It consists of several elements, all of which work together to create a fully functional data infrastructure.
Understanding the basic elements of DBMS architecture is vital for IT professionals who want to be well-prepared for future changes, such as hybrid environments. As the old saying goes – success depends upon preparation.
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Bring talented tech experts together, set them a challenge, and give them a deadline. Then, let them loose and watch the magic happen. That, in a nutshell, is what hackathons are all about. They’re proven to be among the most productive tech events when it comes to solving problems and accelerating innovation.
What Is a Hackathon?
Put simply, a hackathon is a short-term event – often lasting just a couple of days, or sometimes even only a matter of hours – where tech experts come together to solve a specific problem or come up with ideas based on a central theme or topic. As an example, teams might be tasked with discovering a new way to use AI in marketing or to create an app aimed at improving student life.
The term combines the words “hack” and “marathon,” due to how participants (hackers or programmers) are encouraged to work around-the-clock to create a prototype, proof-of-concept, or new solution. It’s similar to how marathon runners are encouraged to keep running, putting their skills and endurance to the test in a race to the finish line.
The Benefits of Hackathons
Hackathons provide value both for the companies that organize them and the people who take part. Companies can use them to quickly discover new ideas or overcome challenges, for example, while participants can enjoy testing their skills, innovating, networking, and working either alone or as part of a larger team.
Benefits for Companies and Sponsors
Many of the world’s biggest brands have come to rely on hackathons as ways to drive innovation and uncover new products, services, and opportunities. Meta, for example, the brand behind Facebook, has organized dozens of hackathons, some of which have led to the development of well-known Facebook features, like the “Like” button. Here’s how hackathons help companies:
- Accelerate Innovation: In fast-moving fields like technology, companies can’t always afford to spend months or years working on new products or features. They need to be able to solve problems quickly, and hackathons create the necessary conditions to deliver rapid success.
- Employee Development: Leading companies like Meta have started to use annual hackathons as a way to not only test their workforce’s skills but to give employees opportunities to push themselves and broaden their skill sets.
- Internal Networking: Hackathons also double up as networking events. They give employees from different teams, departments, or branches the chance to work with and learn from one another. This, in turn, can promote or reinforce team-oriented work cultures.
- Talent Spotting: Talents sometimes go unnoticed, but hackathons give your workforce’s hidden gems a chance to shine. They’re terrific opportunities to see who your best problem solvers and most creative thinkers at.
- Improving Reputation: Organizing regular hackathons helps set companies apart from their competitors, demonstrating their commitment to innovation and their willingness to embrace new ideas. If you want your brand to seem more forward-thinking and innovative, embracing hackathons is a great way to go about it.
Benefits for Participants
The hackers, developers, students, engineers, and other people who take part in hackathons arguably enjoy even bigger and better benefits than the businesses behind them. These events are often invaluable when it comes to upskilling, networking, and growing, both personally and professionally. Here are some of the main benefits for participants, explained:
- Learning and Improvement: Hackathons are golden opportunities for participants to gain knowledge and skills. They essentially force people to work together, sharing ideas, contributing to the collective, and pushing their own boundaries in pursuit of a common goal.
- Networking: While some hackathons are purely internal, others bring together different teams or groups of people from different schools, businesses, and places around the world. This can be wonderful for forming connections with like-minded individuals.
- Sense of Pride: Everyone feels a sense of pride after accomplishing a project or achieving a goal, but this often comes at the end of weeks or months of effort. With hackathons, participants can enjoy that same satisfying feeling after just a few hours or a couple of days of hard work.
- Testing Oneself: A hackathon is an amazing chance to put one’s skills to the test and see what one is truly capable of when given a set goal to aim for and a deadline to meet. Many participants are surprised to see how well they respond to these conditions.
- Boosting Skills: Hackathons provide the necessary conditions to hone and improve a range of core soft skills, such as teamwork, communication, problem-solving, organization, and punctuality. By the end, participants often emerge with more confidence in their abilities.
Hackathons at OPIT
The Open Institute of Technology (OPIT) understands the unique value of hackathons and has played its part in sponsoring these kinds of events in the past. OPIT was one of the sponsors behind ESCPHackathon 6, for example, which involved 120 students given AI-related tasks, with mentorship and guidance from senior professionals and developers from established brands along the way.
Marco Fediuc, one of the participants, summed up the mood in his comments:
“The hackathon was a truly rewarding experience. I had the pleasure of meeting OPIT classmates and staff and getting to know them better, the chance to collaborate with brilliant minds, and the opportunity to take part in an exciting and fun event.
“Participating turned out to be very useful because I had the chance to work in a fast-paced, competitive environment, and it taught me what it means to stay calm and perform under pressure… To prospective Computer Science students, should a similar opportunity arise, I can clearly say: Don’t underestimate yourselves!”
The new year will also see the arrival of OPIT Hackathon 2026, giving more students the chance to test their skills, broaden their networks, and enjoy the one-of-a-kind experiences that these events never fail to deliver. This event is scheduled to be held February 13-15, 2026, and is open to all OPIT Bachelor’s and Master’s students, along with recent graduates. Interested parties have until February 1 to register.
The Open Institute of Technology (OPIT) recently held its first-ever career fair to showcase its wide array of career education options and services. Representatives from numerous high-profile international companies were in attendance, and students enjoyed unprecedented opportunities to connect with business leaders, expand their professional networks, and pave the way for success in their future careers.
Here’s a look back at the event and how it ties into OPIT’s diverse scope of career services.
Introducing OPIT
For those who aren’t yet familiar, OPIT is an EU-accredited Higher Education Institution, offering online degrees in technological fields such as computer science, data science, artificial intelligence, cybersecurity, and digital business. Aimed at making high-level tech education accessible to all, OPIT has assembled a stellar team of tutors and experts to train the tech leaders of tomorrow.
The First OPIT Career Fair
OPIT’s first career fair was held on November 19 and 20. And as with OPIT’s lectures, it was an exclusively online event, which ensured that every attendee had equal access to key lectures and information. Interested potential students from all over the world were able to enjoy the same great experience, demonstrating a core principle that OPIT has championed from the very start – the principles of accessibility and the power of virtual learning.
More than a dozen leading international companies took part in the event, with the full guest list including representatives from:
- Deloitte
- Dylog Hitech
- EDIST Engineering Srl
- Tinexta Cyber
- Datapizza
- RWS Group
- WE GRELE FRANCE
- Avatar Investments
- Planet Farms
- Coolshop
- Hoist Finance Italia
- Gruppo Buffetti S.p.A
- Nesperia Group
- Fusion AI Labs
- Intesi Group
- Reply
- Mindsight Ventures
This was a fascinating mix of established enterprises and emerging players. Deloitte, for example, is one of the largest professional services networks in the world in terms of both revenue and number of employees. Mindsight Ventures, meanwhile, is a newer but rapidly emerging name in the fields of AI and business intelligence.
The Response
The first OPIT career fair was a success, with many students in attendance expressing their joy at being able to connect with such a strong lineup of prospective employers.
OPIT Founder and Director Riccardo Ocleppo had this to say:
“I often say internally that our connection with companies – through masterclasses, thesis and capstone projects, and career opportunities – is the ‘cherry on the cake’ of the OPIT experience!
“It’s also a core part of our mission: making higher education more practical, more connected, and more aligned with what happens in the real world.
“Our first Career Fair says a lot about our commitment to building an end-to-end learning and professional growth experience for our community of students.
“Thank you to the Student and Career Services team, and to Stefania Tabi for making this possible.”
Representatives from some of the companies that attended also shared positive impressions of the event. A representative from Nesperia Group, for example, said:
“Nesperia Group would like to thank OPIT for the warm welcome we received during the OPIT Career Day. We were pleased to be part of the event because we met many talented young professionals. Their curiosity and their professional attitude really impressed us, and it’s clear that OPIT is doing an excellent job supporting their growth. We really believe that events like these are important because they can create a strong connection between companies and future professionals.”
The Future
Given the enormous success of the first OPIT career fair, it’s highly likely that students will be able to enjoy more events like this in the years to come. OPIT is clearly committed to making the most of its strong business connections and remarkable network to provide opportunities for growth, development, and employment, bringing students and businesses together.
Future events will continue to allow students to connect with some of the biggest businesses in the world, along with emerging names in the most exciting and innovative tech fields. This should allow OPIT graduates to enter the working world with strong networks and firm connections already established. That, in turn, should make it easier for them to access and enjoy a wealth of beneficial professional opportunities.
Given that OPIT also has partnerships in place with numerous other leading organizations, like Hype, AWS, and Accenture, the number and variety of the companies potentially making appearances at career fairs in the future should no doubt increase dramatically.
Other Career Services at OPIT
The career fair is just one of many ways in which OPIT leverages its company connections and offers professional opportunities and career support to its students. Other key career services include:
- Career Coaching: Students are able to schedule one-on-one sessions with their own mentors and career advisors. They can receive feedback on their resumes, practice and improve their interview skills, or work on clear action plans that align with their exact professional goals.
- Resource Hub: The OPIT Resource Hub is jam-packed with helpful guides and other resources to help students plan out and take smart steps in their professional endeavors. With detailed insights and practical tips, it can help tech graduates get off to the best possible start.
- Career Events: The career fair is only one of several planned career-related events organized by OPIT. Other events are planned to give students the chance to learn from and engage with industry experts and leading tech firms, with workshops, career skills days, and more.
- Internships: OPIT continues to support students after graduation, offering internship opportunities with leading tech firms around the world. These internships are invaluable for gaining experience and forging connections, setting graduates up for future success.
- Peer Mentoring: OPIT also offers a peer mentoring program in which existing students can team up with OPIT alumni to enjoy the benefits of their experience and unique insights.
These services – combined with the recent career day – clearly demonstrate OPIT’s commitment to not merely educating the tech leaders of the future, but also to supporting their personal and professional development beyond the field of education, making it easier for them to enter the working world with strong connections and unrivaled opportunities.
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