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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From the same author
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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During the Open Institute of Technology’s (OPIT) 2025 graduation day, the OPIT team interviewed graduating student Irene about her experience with the MSc in Applied Data Science and AI. The interview focused on how Irene juggled working full-time with her study commitments and the value of the final Capstone project, which is part of all OPIT’s master’s programs.
Irene, a senior developer at ReActive, said she chose to study at OPIT to update her skills for the current and future job market.
OPIT’s MSc in Applied Data Science and AI
In her interview, Irene said she appreciated how OPIT’s course did not focus purely on the hard mathematics behind technologies such as AI and cloud computing, but also on how these technologies can be applied to real business challenges.
She said she appreciated how the course gave her the skills to explain to stakeholders with limited technical knowledge how technology can be leveraged to solve business problems, but it also equipped her to engage with technical teams using their language and jargon. These skills help graduates bridge the gap between management and technology to drive innovation and transformation.
Irene chose to continue working full-time while studying and appreciated how her course advisor helped her plan her study workload around her work commitments “down to the minute” so that she never missed a deadline or was overcome by excessive stress.
She said she would recommend the program to people at any stage in their career who want to adapt to the current job market. She also praised the international nature of the program, in terms of both the faculty and the cohort, as working beyond borders promises to be another major business trend in the coming years.
Capstone Project
Irene described the most fulfilling part of the program as the final Capstone project, which allowed her to apply what she had learned to a real-life challenge.
The Capstone Project and Dissertation, also called the MSc Thesis, is a significant project aimed at consolidating skills acquired during the program through a long-term research project.
Students, with the help of an OPIT supervisor, develop and realize a project proposal as part of the final term of their master’s journey, investigating methodological and practical aspects in program domains. Internships with industrial partners to deliver the project are encouraged and facilitated by OPIT’s staff.
The Capstone project allows students to demonstrate their mastery of their field and the skills they’ve learned when talking to employers as part of the hiring process.
Capstone Project: AI Meets Art
Irene’s Capstone project, “Call Me VasarAI: An AI-Powered Framework for Artwork Recognition and Storytelling,” focused on using AI to bridge the gap between art and artificial intelligence over time, enhancing meaning through contextualization. She developed an AI-powered platform that allows users to upload a work of art and discover the style (e.g. Expressionism), the name of the artist, and a description of the artwork within an art historical context.
Irene commented on how her supervisor helped her fine-tune her ideas into a stronger project and offered continuous guidance throughout the process with weekly progress updates. After defending her thesis in January, she noted how the examiners did not just assess her work but guided her on what could be next.
Other Example Capstone Projects
Irene’s success is just one example of a completed OPIT Capstone project. Below are further examples of both successful projects and projects currently underway.
Elina delivered her Capstone project on predictive modeling of natural disasters using data science and machine learning techniques to analyze global trends in natural disasters and their relationships with climate change-related and socio-economic factors.
According to Elina: “This hands-on experience has reinforced my theoretical and practical abilities in data science and AI. I appreciate the versatility of these skills, which are valuable across many domains. This project has been challenging yet rewarding, showcasing the real-world impact of my academic learning and the interdisciplinary nature of data science and AI.”
For his Capstone project, Musa worked on finding the optimal pipeline to fine-tune a language learning model (LLM) based on the specific language and model, considering EU laws on technological topics such as GDPR, DSA, DME, and the AI Act, which are translated into several languages.
Musa stated: “This Capstone project topic aligns perfectly with my initial interests when applying to OPIT. I am deeply committed to developing a pipeline in the field of EU law, an area that has not been extensively explored yet.”
Tamas worked with industry partner Solergy on his Capstone project, working with generative AI to supercharge lead generation, boost SEO performance, and deliver data-driven marketing insights in the realm of renewable energy.
OPIT’s Master’s Courses
All of OPIT’s master’s courses include a final Capstone project to be completed over one 13-week term in the 90 ECTS program and over two terms in the 120 ECTS program.
The MSc in Digital Business and Innovation is designed for professionals who want to drive digital innovation in both established companies and new digital-native contexts. It covers digital business foundations and the applications of new technologies in business contexts. It emphasizes the use of AI to drive innovation and covers digital entrepreneurship, digital product management, and growth hacking.
The MSc in Responsible Artificial Intelligence combines technical expertise with a focus on the ethical implications of modern AI. It focuses on real-world applications in areas like natural language processing and industry automation, with a focus on sustainable AI systems and environmental impact.
The MSc in Enterprise Cybersecurity prepares students to fulfill the market need for versatile cybersecurity solutions, emphasizing hands-on experience and soft-skills development.
The MSc in Applied Data Science and AI focuses on the intersection between management and technology. It covers the underlying fundamentals, methodologies and tools needed to solve real-life business problems that can be approached using data science and AI.
In May 2025, Greta Maiocchi, Head of Marketing and Administration at the Open Institute of Technology (OPIT), went online with Stefania Tabi, OPIT Career Services Counselor, to discuss how OPIT helps students translate their studies into a career.
You can access OPIT Career Services throughout your course of study to help with making the transition from student to professional. Stefania specifically discussed what companies and businesses are looking for and how OPIT Career Services can help you stand out and find a desirable career with your degree.
What Companies Want
OPIT degrees are tailored to a wide range of individuals, with bachelor’s degrees for those looking to establish a career and master’s degrees for experienced professionals hoping to elevate their skills to meet the current market demand.
OPIT’s degrees establish the foundation of the key technological skills that are set to reshape industries shortly, in particular artificial intelligence (AI), big data, cloud computing, and cybersecurity.
Stefania shared how companies recruiting tech talent are looking for three types of skills:
- Builders – These are the superstars of the industry today, capable of developing the technologies that will transform the industry. These roles include AI engineers, cloud architects, and web developers.
- Protectors – Cybercrime is expected to cost the world $10.5 trillion by the end of 2025, which means companies place a high value on cybersecurity professionals capable of protecting their investment, data, and intellectual property (IP).
- Decoders – Industry is producing more data than ever before, with global data storage projected to exceed 200 zettabytes this year. Businesses seek professionals who can extract value from that data, such as data scientists and data strategists.
Growing Demand
Stefania also shared statistics about the growing demand for these roles. According to the World Economic Forum, there will be a 30-35% greater demand for roles such as data analysts and scientists, big data specialists, business intelligence analysts, data engineers, and database and network professionals by 2027.
The U.S. Bureau of Labor Statistics, meanwhile, predicts that by 2032, the demand for information security will increase by 33.8%, by 21.5% for software developers, by 10.4% for computer network architects, and by 9.9% for computer system analysts. Finally, the McKinsey Global Institute predicts a similar 15-25% increase in demand for technology professionals in the business services sector.
How Career Support Makes a Difference
Next, Stefania explained that while learning essential skills is vital to accessing this growing job market, high demand does not guarantee entry. Today, professionals looking for jobs in the technology field must stand out from the hundreds of applicants for each position with high-level skills.
Applicants demonstrate technical expertise in relevant fields by completing OPIT’s courses. They also need to prove that they can deliver results, demonstrating not just what they know but how they have applied what they know to transform or benefit a business. Professionals also need adaptability, adaptive problem-solving skills, and a commitment to continuous learning. OPIT’s final Capstone projects can be an excellent way to demonstrate the value of newly acquired skills.
Each OPIT program prepares students for future careers by providing dedicated support and academic guidance at every step.
What Kind of Support Does Career Services Offer?
Career Services is specifically focused on assisting students in making the transition to the job market, and you can make an appointment with them at any time during your studies. Stefania gave some specific examples of how Career Services can support students on their journey into the career market.
Stefania said she begins by talking with students and discussing what they truly value to help them discover the type of career that aligns with their strengths. With students who are still undecided on how to start to build their careers, she helps them craft a tailored job and internship search plan.
Stefania has also worked with students who want to stand out during the job application process among the hundreds of applicants. This includes hands-on help in reframing resumes, tailoring LinkedIn profiles, and developing cover letters that tell a unique story.
Finally, Stefania has assisted students in preparing for interviews, helping them research the company, develop intelligent questions about the role to ask the interviewer and engage in mock interviews with an experienced recruiter.
Connecting With Employers
OPIT Career Services also offers students exposure to a wide range of employers and the opportunity to build relationships through masterclasses, career talks, and industry roundtables. The office also helps students build career-ready skills through interactive, hands-on workshops and hosts virtual career fairs with top recruiters.
Career Services also plays an integral role in connecting students with companies for their Capstone project in the final phase of their master’s program. So far, students have worked with companies including Sintica, Cosmica, Cisco, PayPal, Morgan Stanley, AWS, Dylog, and Accenture. Projects have included developing predictive modeling for natural disasters and fine-tuning AI to answer questions about EU tech laws in multiple languages.
What Kinds of Jobs Have OPIT Graduates Secured?
Stefania capped off her talk by sharing some of the positions that OPIT graduates have now fulfilled, including:
- Chief Information Security Officer at MOMO for MTN mobile services in Nigeria
- Data Analyst at ISX Financial in Cyprus
- Head of Sustainability Office at Banca Popolare di Sondrio in Italy
- Data Analyst at Numisma Group in Cyprus
- Senior Software Engineer at Neaform in Italy
OPIT Courses
OPIT offers both foundational bachelor’s degrees and advanced master’s courses, which are both accessible with any bachelor’s degree (it does not have to be in the field of computer science).
Choose between a BSc in Modern Computer Science for a strong technical base or a BSc in Digital Business to focus on applications.
Meanwhile, courses that involve a final Capstone project include an MSc in Applied Data Science and AI, Digital Business and Innovation, Enterprise Cybersecurity, and Responsible Artificial Intelligence.
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