In a database, you have entities (which have attributes), and relationships between those entities. Managing them is key to preventing chaos from engulfing your database, which is where the concept of keys comes in. These unique identifiers enable you to pick specific rows in an entity set, as well as define their relationships to rows in other entity sets, allowing your database to handle complex computations.
Let’s explore keys in DBMS (database management systems) in more detail, before digging into everything you need to know about the most important keys – primary keys.
Understanding Keys in DBMS
Keys in DBMS are attributes that you use to identify specific rows inside a table, in addition to finding the relation between two tables. For example, let’s say you have a table for students, with that table recording each student’s “ID Number,” “Name,” “Address,” and “Teacher” as attributes. If you want to identify a specific student in the table, you’ll need to use one of these attributes as a key that allows you to pull the student’s record from your database. In this case “ID Number” is likely the best choice because it’s a unique attribute that only applies to a single student.
Types of Keys in DBMS
Beyond the basics of serving as unique identifiers for rows in a database, keys in DBMS can take several forms:
- Primary Keys – An attribute that is present in the table for all of the records it contains, with each instance of that attribute being unique to the record. The previously-mentioned “ID Number” for students is a great example, as no student can have the same number as another student.
- Foreign Key – Foreign keys allow you to define and establish relationships between a pair of tables. If Table A needs to refer to the primary key in Table B, you’ll use a foreign key in Table A so you have values in that table to match those in Table B.
- Unique Key – These are very similar to primary keys in that both contain unique identifiers for the records in a table. The only difference is that a unique key can contain a null value, whereas a primary key can’t.
- Candidate Key – Though you may have picked a unique attribute to serve as your primary key, there may be other candidates within a table. Coming back to the student example, you may record the phone numbers and email addresses of your students, which can be as unique as the student ID assigned to the individual. These candidate keys are also unique identifiers, allowing them to be used in tandem with a primary key to identify a specific row in a table.
- Composite Key – If you have attributes that wouldn’t be unique when taken alone, but can be combined to form a unique identifier for a record, you have a composite key.
- Super Key – This term refers to the collection of attributes that uniquely identify a record, meaning it’s a combination of candidate keys. Just like an employer sifting through job candidates to find the perfect person, you’ll sift through your super key set to choose the ideal primary key amongst your candidate keys.
So, why are keys in DBMS so important?
Keys ensure you maintain data integrity across all of the tables that make up your database. Without them, the relationships between each table become messy hodgepodges, creating the potential for duplicate records and errors that deliver inaccurate reports from the database. Having unique identifiers (in the form of keys) allows you to be certain that any record you pull, and the relationships that apply to that record, are accurate and unrepeated.
Primary Key Essentials
As mentioned, any unique attribute in a table can serve as a primary key, though this doesn’t mean that every unique attribute is a great choice. The following characteristics help you to define the perfect primary key.
Uniqueness
If your primary key is repeatable across records, it can’t serve as a unique identifier for a single record. For example, our student table may have multiple people named “John,” so you can’t use the “Name” attribute to find a specific student. You need something unique to that student, such as the previously mentioned ID number.
Non-Null Values
Primary keys must always contain a value, else you risk losing records in a table because you have no way of calling upon them. This need for non-null values can be used to eliminate some candidates from primary key content. For instance, it’s feasible (though unlikely) that a student won’t have an email address, creating the potential for null values that mean the email address attribute can’t be a primary key.
Immutability
A primary key that can change over time is a key that can cause confusion. Immutability is the term used for any attribute that’s unchanging to the point where it’s an evergreen attribute that you can use to identify a specific record forever.
Minimal
Ideally, one table should have one attribute that serves as its primary key, which is where the term “minimal” comes in. It’s possible for a table to have a composite or super key set, though both create the possibility of confusion and data integrity issues.
The Importance of a Primary Key in DBMS
We can distill the reason why having a primary key in DBMS for each of your tables is important into the following reasons:
- You can use a primary key to identify each unique record in a table, meaning no multi-result returns to your database searches.
- Having a primary key means a record can’t be repeated in the table.
- Primary keys make data retrieval more efficient because you can use a single attribute for searches rather than multiple.
Functions of Primary Keys
Primary keys in DBMS serve several functions, each of which is critical to your DBMS.
Data Identification
Imagine walking into a crowded room and shouting out a name. The odds are that several people (all of whom have the same name) will turn their heads to look at you. That’s basically what you’re doing if you try to pull records from a table without using a primary key.
A primary key in DBMS serves as a unique identifier that you can use to pull specific records. Coming back to the student example mentioned earlier, a “Student ID” is only applicable to a single student, making it a unique identifier you can use to find that student in your database.
Ensure Data Integrity
Primary keys protect data integrity in two ways.
First, they prevent duplicate records from building up inside a single table, ensuring you don’t get multiple instances of the same record. Second, they ensure referential integrity, which is the term used to describe what happens when one table in your database needs to refer to the records stored in another table.
For example, let’s say you have tables for “Students” and “Teachers” in your database. The primary keys assigned to your students and teachers allow you to pull individual records as needed from each table. But every “Teacher” has multiple “Students” in their class. So, your primary key from the “Students” table is used as a foreign key in the “Teachers” table, allowing you to denote the one-to-many relationship between a teacher and their class of students. That foreign key also ensures referential integrity because it contains the unique identifiers for students, which you can look up in your “Students” table.
Data Retrieval
If you need to pull a specific record from a table, you can’t rely on attributes that can repeat across several records in that table. Again, the “Name” example highlights the problem here, as several people could have the same name. You need a unique identifier for each record so you can retrieve a single record from a huge set without having to pore through hundreds (or even thousands) of records.
Best Practices for Primary Key Selection
Now that you understand how primary keys in DBMS work, here are some best practices for selecting the right primary key for your table:
- Choose Appropriate Attributes as Candidates – If the attribute isn’t unique to each record, or it can contain a null value (as is the case with email addresses and phone numbers), it’s not a good candidate for a primary key.
- Avoid Using Sensitive Information – Using personal or sensitive information as a primary key creates a security risk because anybody who cracks your database could use that information for other purposes. Make your primary keys unique, and only applicable, to your database, which allows you to encrypt any sensitive information stored in your tables.
- Consider Surrogate Keys – Some tables don’t have natural attributes that you can use as primary keys. In these cases, you can create a primary key out of thin air and assign it to each record. The “Student ID” referenced earlier is a great example, as students entering a school don’t come with their own ID numbers. Those numbers are given to the student (or simply used in the database that collects their data), making them surrogate keys.
- Ensure Primary Key Stability – Any attribute that can change isn’t suitable for use as a primary key because it causes stability issues. Names, email addresses, phone numbers, and even bank account details are all things that can change, making them unsuitable. Evergreen and unchanging is the way to go with primary keys.
Choose the Right Keys for Your Database
You need to understand the importance of a primary key in DBMS (or multiple primary keys when you have several tables) so you can define the relationships between tables and identify unique records inside your tables. Without primary keys, you’ll find it much harder to run reports because you won’t feel confident in the accuracy of the data returned. Each search may pull up duplicate or incorrect records because of a lack of unique identifiers.
Thankfully, many of the tables you create will have attributes that lend themselves well to primary key status. And even when that isn’t the case, you can use surrogate keys in DBMS to assign primary keys to your tables. Experiment with your databases, testing different potential primary keys to see what works best for you.
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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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More and more students are turning to careers in computer science, and it’s easy to see why. The world is becoming increasingly reliant on and powered by modern technology with every passing day.
Emerging innovations like artificial intelligence (AI) and cloud computing are set to change the face of countless industries. This means qualifications in computer science unlock the doors to countless job opportunities.
However, this is also a dynamic and rapidly changing field – a field that demands a distinct set of core skills to not only achieve success, but also future-proof your career for years to come.
The Importance and Value of Computer Science and Cloud Computing Qualifications
Official statistics show that the European cloud computing market is set to experience an astonishing compound annual growth rate (CAGR) of almost 23% in the coming years, moving from a value of €110 billion in 2024 to a lower-end estimate €250 billion by 2028, and it won’t stop there.
The same sort of growth is being seen across the full spectrum of computer science fields and industries, with emerging technologies like AI, automation, and edge computing all spearheading the surging value and importance of these sectors.
The increasing shift away from conventional computing towards cloud computing shows just how quickly the technological world changes and evolves. It also demonstrates the need for professionals with a wide and varied range of skills, plus the ability to adapt to rapidly changing situations.
Those who can bring these skills to the table, supported by a comprehensive education and effective computer science qualifications, can enjoy:
- Entry into job markets that have exceptionally high levels of demand.
- Diverse career opportunities in fields that range from AI and machine learning to computer engineering and software development.
- Competitive salaries, with consistently high rates offered all around the world.
- Job security and stability, given the ever-increasing significance of technology in the modern world.
Core Skills to Master for Long Careers in Computer Science
Next, let’s look into the actual specific skills that computer science students and aspiring leaders will need to master to secure their futures.
Programming Languages
Being able to work with code is the bedrock of almost any career in computer science. Aspiring scientists will need to master one or several programming languages, including Java for building business and web apps, Python for web development, and C++ for system programming.
Networks and Architectures
Computer scientists also need a strong core knowledge of the fundamentals of the computing world, like how networks function, the various protocols and architectures that control the flow of data from device to device, etc.
Algorithms and Data
Algorithms play a fundamental role in fields like AI and machine learning, so computer scientists must understand how different algorithms and data structures drive computing processes, problem-solving, and development.
Software Development
Computer scientists may spend much of their time working on development projects post-graduation, which may include apps for the web, business, mobile, and more. A strong understanding of the stages of the software development lifecycle and processes involved is, therefore, essential.
Cloud Computing
With cloud computing growing in importance, unlocking whole new fields, like software as a service (SaaS) and desktop as a service (DaaS), the computer scientists of tomorrow will also need to have intricate knowledge of how cloud computing works and the potential it holds.
Soft Skills
Along with the many technical skills listed above, computer scientists will also benefit from vital soft skills that will help them when collaborating with others, presenting their ideas, driving innovation in their chosen fields, leading teams, and managing projects. These skills include:
- Being able to communicate effectively, both with fellow computer scientists and non-technical audiences as well
- Analytical thinking and problem-solving skills that help scientists understand and respond to challenges
- Adaptability, so that scientists can keep on learning, evolving their skill sets, and embracing new tech and ideas as they emerge
- The ability to work as part of a team, often fundamental for large-scale computing projects
- Attention to detail that allows you to spot that single line of code that needs fixing, or for tasks like debugging and data interpretation
Get the Best Start With a BSc in Computer Science at OPIT
We’ve seen the skills you need to succeed in computer science and cloud computing for years to come, but one question remains: where is the best place to gain these skills and acquire the necessary knowledge for genuine long-term success?
The answer: the Open Institute of Technology (OPIT).
An exclusively online learning platform specializing in computer science and digital business, OPIT offers a curated collection of online degrees for tomorrow’s technology leaders, including a Foundation Program for beginners, bachelor’s programs, and master’s degrees for the most experienced students.
Why Study at OPIT?
Advantages of studying with OPIT include:
- Accessible and flexible online learning to suit your needs and pace
- A focus on real-world applications of the skills and knowledge imparted
- A leading team of top tutors and professors from diverse fields and nations
- An international community of students with which to collaborate
- Progressive assessment of skills and knowledge, as opposed to exam-heavy schedules
Those planning pathways into computer science and cloud computing are best served by OPIT’s BSc in Computer Science program.
Lasting six terms and worth 180 ECTS credits, this fully remote, online, and accredited undergraduate degree was developed with insights from industry leaders, academics, and employers within the IT industry.
It equips graduates with both the fundamental theoretical knowledge they need to succeed in technology, as well as the practical skills required to enter the world of work and immediately innovate in fields like programming, cybersecurity, data science, AI, and cloud computing.
Like other courses and programs available at OPIT, this BSc in Computer Science provides students with a flexible learning program involving a mixture of recorded and engaging live content, progressive assessment, fast-track options for those who study at a faster pace, and round-the-clock accessibility to tutors and fellow learners.
If you feel that computer science or cloud computing is the right path for you, and you want to secure the foundational skills you need to succeed, consider the OPIT BSc in Computer Science to help you achieve your dreams.
On September 8, 2025, the Open Institute of Technology (OPIT) officially inaugurated its 2025/26 academic year, welcoming a whole host of new students into the institute, ready to gain the skills and knowledge they need to be the tech industry leaders of tomorrow.
Faculty members, guest speakers from around the world, and hundreds of students from over 100 nations were all in attendance at the online inauguration event. Among them was one new student on the MSc in Responsible AI program: Rem Lampa, also known by his online alias, Kuya Dev.
A Filipino podcaster, speaker, and developer, Kuya Dev documented the inauguration experience in a YouTube video, sharing his thoughts on the event. Here’s a detailed look at his experience, showing the welcoming and effective way in which new OPIT students are brought into the institute from the first day.
An Overview of the Inauguration Event
The latest OPIT inauguration event was one to remember, with the institute celebrating several notable milestones. These included the first graduation of the MSc in Applied Data Science and AI program, and the unveiling of new industry connections and corporate partners offering opportunities for graduates.
Various guest speakers were in attendance, including the former Italian Minister of Education and current OPIT Rector, Professor Francesco Profumo, plus the CEO of Malta’s accreditation authority MFHEA, Dr. Rose Anne Cuschieri.
OPIT founder, Riccardo Ocleppo, also took to the stage to highlight the transformative role of education and the unique possibilities offered by the online-only OPIT degrees, stating:
“When we launched OPIT in 2022, we wanted to prove that an online degree could be rigorous, international, deeply connected to the world of work, and engaging. Three years later, we can already see results. This year alone, 300 new students are joining us, representing 98 nationalities.”
Those hundreds of new students were also, naturally, in attendance for their big inauguration day, welcomed into the institute’s various BSc and MSc programs in specialties like Computer Science, Digital Business, Digital Business and Innovation, and Cybersecurity.
Introducing Rem ‘Kuya Dev’ Lampa
As mentioned, one of the new students joining was vlogger and podcaster, Kuya Dev.
Already boasting extensive experience and technical expertise in the computing world, Kuya Dev has founded several tech communities, including Tech Career Shifter Philippines and Server-Side JavaScript.
He describes himself as an advocate for responsible AI and tech practices, and once gave a powerful TEDx talk about the negative potential impacts of artificial intelligence (AI), including its large carbon footprint and involving exploitation of workers in emerging nations. So, it was only fitting that he signed up for OPIT’s MSc in Responsible AI.
The MSc in Responsible AI Program
A postgraduate program lasting three or four terms (between 12 and 24 months, total), OPIT’s MSc in Responsible AI is an accredited program that combines technical skills with a core focus on the ethical implications of AI technology.
It explores real-world use cases of AI and ways in which this exciting and powerful technology can be used responsibly and sustainably, emphasizing its potential for both positive and negative change in areas such as the environment and society as a whole.
Students of this program enjoy all the usual benefits that come with studying at OPIT, like flexible-paced learning, tutors available on a daily basis, a dynamic and engaging online learning environment, and fair, progressive assessments over time.
Those who graduate can move into careers in AI Application Development, AI Research, and AI Business Consulting.
Highlights and Impressions of Kuya Dev’s Inauguration Experience
Inauguration is always a special moment in any student’s academic journey as they take their first tentative yet exciting steps towards new knowledge and skills that can unlock their potential and accelerate their development.
It can even be a “make-or-break” moment in which students can either feel welcomed, empowered, and eager to begin their studies, or, conversely, experience feelings of worry, doubt, or regret if the inauguration lacks the spark they were hoping to feel.
Fortunately, Kuya Dev and his many fellow new OPIT students had a very positive experience at the inauguration event, with the vlogger calling it both “very fruitful” and “very insightful.” Some of the specific highlights he mentioned included:
An International Community
Early in the inauguration event, Kuya Dev heard from Professor Francesco Profumo about how OPIT was welcoming students from over 90 countries.
This, for Kuya Dev and for so many other students, is one of the big perks of studying with OPIT. It allows students to meet, engage, and exchange with people from dozens of different backgrounds, cultures, and communities, learning new ways of thinking and collaborating.
Real World Applications
The OPIT inauguration also focused on another of the university’s key features: a focus not merely on imparting knowledge to students or making them memorize facts, figures, and theorems, but actually teaching them how to apply that knowledge and ideas to real-world uses.
Kuya Dev commented on how other universities that he had considered attending appeared more conventional and limited in their approach, and also shared his excitement and enthusiasm at OPIT’s unique approach, focusing more on genuine industry collaboration and real-life scenarios.
Inspiring Speakers
The vlogger and podcaster also commented on the various key speakers who hosted the event. He was pleased to see that they were not only academically oriented but also all had real-world experience and a diverse range of working backgrounds in fields like politics.
This, too, is part of what makes OPIT different. It features a winning team of international tutors, professors, and experts from diverse fields, including the aforementioned former Italian Education Minister, education executives, research chairs, digital content creation specialists, and more.
Graduate Experiences
Finally, Kuya Dev noted how several OPIT graduates spoke to those about to follow the same path they did, including students from nations as diverse as the U.S., Italy, and Nigeria.
They all shared their experiences and detailed what they enjoyed most about studying with OPIT, including key factors such as professors who took the time to get to know their students, as well as the depth of the topics and knowledge covered in each course.
A Successful First Step Into OPIT Education
All in all, the recent inauguration event was a major success for all involved, giving students like Kuya Dev the ideal introduction to life and study with OPIT. He, like so many of his new fellow students, left the event eager and excited to expand their knowledge and broaden their horizons in unique ways that only OPIT can provide.
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