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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- Metro, published on October 09th, 2025
After ChatGPT came on the scene in 2022, the tech industry quickly began comparing the arrival of AI to the dawn of the internet in the 1990s.
Back then, dot-com whizzes were minting easy millions only for the bubble to burst in 2000 when interest rates were hiked. Investors sold off their holdings, companies went bust and people lost their jobs.
Now central bank officials are worried that the AI industry may see a similar boom and bust.
A record of the Financial Policy Committee’s October 2 meeting shows officials saying financial market evaluations of AI ‘appear stretched’.
‘This, when combined with increasing concentration within market indices, leaves equity markets particularly exposed should expectations around the impact of AI become less optimistic,’ they added.
AI-focused stocks are mainly in US markets but as so many investors across the world have bought into it, a fallout would be felt globally.
ChatGPT creator OpenAI, chip-maker Nvidia and cloud service firm Oracle are among the AI poster companies being priced big this year.
Earnings are ‘comparable to the peak of the dot-com bubble’, committee members said.
Factors like limited resources – think power-hungry data centres, utilities and software that companies are spending billions on – and the unpredictability of the world’s politics could lead to a drop in stock prices, called a ‘correction’.
In other words, the committee said, investors may be ignoring how risky AI technology is.
Metro spoke with nearly a dozen financial analysts, AI experts and stock researchers about whether AI will suffer a similar fate. There were mixed feelings.
‘Every bubble starts with a story people want to believe,’ says Dat Ngo, of the trading guide, Vetted Prop Firms.
‘In the late 90s, it was the internet. Today, it’s artificial intelligence. The parallels are hard to ignore: skyrocketing stock prices, endless hype and companies investing billions before fully proving their business models.
‘The Bank of England’s warning isn’t alarmist – it’s realistic. When too much capital chases the same dream, expectations outpace results and corrections follow.’
Dr Alessia Paccagnini, an associate Professor from the University College Dublin’s Michael Smurfit Graduate Business School, says that companies are spending £300billion annually on AI infrastructure, while shoppers are spending $12billion. That’s a big difference.
Tech firms listed in the US now represent 30% of New York’s stock index, S&P 500 Index, the highest proportion in 50 years.
‘As a worst-case scenario, if the bubble does burst, the immediate consequences would be severe – a sharp market correction could wipe trillions from stock valuations, hitting retirement accounts and pension funds hard,’ Dr Paccagnini adds.
‘In my opinion, we should be worried, but being prepared could help us avoid the worst outcomes.’
One reason a correction would be so bad is because of how tangled-up the AI world is, says George Sweeney, an investing expert at the personal finance website site Finder.
‘If it fails to meet the lofty expectations, we could see an almighty unravelling of the AI hype that spooks markets, leading to a serious correction,’ he says.
Despite scepticism, AI feels like it’s everywhere these days, from dog bowls and fridges to toothbrushes and bird feeders.
And it might continue that way for a while, even if not as enthusiastically as before, says Professor Filip Bialy, who specialises in computer science and AI ethics at the at Open Institute of Technology.
‘TAI hype – an overly optimistic view of the technological and economic potential of the current paradigm of AI – contributes to the growth of the bubble,’ he says.
‘However, the hype may end not with the burst of the bubble but rather with a more mature understanding of the technology.’
Some stock researchers worry that the AI boom could lose steam when the companies spending billions on the tech see profits dip.
The AI analytic company Qlik found that only one in 10 business say their AI initiatives are seeing sizeable returns.
Qlik’s chief strategy officer, James Fisher, says this doesn’t show that the hype for AI is bursting, ‘but how businesses look at AI is changing’.
OPIT – Open Institute of Technology offers an innovative and exciting way to learn about technology. It offers a range of bachelor’s and master’s programs, plus a Foundation Year program for those taking the first steps towards higher education. Through its blend of instruction-based and independent learning, it empowers ambitious minds with the skills and knowledge needed to succeed.
This guide covers all you need to know to join OPIT and start your educational journey.
Introducing the Open Institute of Technology
Before we dig into the nitty-gritty of the OPIT application process, here’s a brief introduction to OPIT.
OPIT is a fully accredited Higher Education Institution under the European Qualification Framework (EQF) and the MFHEA Authority. It offers exclusively online education in English to an international community of students. With a winning team of top professors and a specific focus on computer science, it trains the technology leaders of tomorrow.
Some of the unique elements that characterize OPIT’s approach include:
- No final exams. Instead, students undergo progressive assessments over time
- A job-oriented, practical focus on the courses
- 24/7 support, including AI assistance and student communities, so everyone feels supported
- A strong network of company connections, unlocking doors for graduates
Reasons to Join OPIT
There are many reasons for ambitious students and aspiring tech professionals to study with OPIT.
Firstly, since all the study takes place online, it’s a very flexible and pleasant way to learn. Students don’t feel the usual pressures or suffer the same constraints they would at a physical college or university. They can attend from anywhere, including their own homes, and study at a pace that suits them.
OPIT is also a specialist in the technology field. It only offers courses focused on tech and computer science, with a team of professors and tutors who lead the way in these topics. This ensures that students get high-caliber learning opportunities in this specific sector.
Learning at OPIT is also hands-on and applicable to real-world situations, despite taking place online. Students are not just taught core skills and knowledge, but are also shown how to apply those skills and knowledge in their future careers.
In addition, OPIT strives to make technology education as accessible, inclusive, and affordable as possible. Entry requirements are relatively relaxed, fees are fair, and students from around the world are welcome here.
What You Need to Know About Joining OPIT
Now you know why it’s worth joining OPIT, let’s take a closer look at how to go about it. The following sections will cover how to apply to OPIT, entry requirements, and fees.
The OPIT Application Process
Unsurprisingly for an online-only institution, the application process for OPIT is all online, too. Users can submit the relevant documents and information on their computers from the comfort of their homes.
- Visit the official OPIT site and click the “Apply now” button to get started, filling out the relevant forms.
- Upload your supporting documents. These can include your CV, as well as certificates to prove your past educational accomplishments and level of English.
- Take part in an interview. This should last no more than 30 minutes. It’s a chance for you to talk about your ambitions and background, and to ask questions you might have about OPIT.
That’s it. Once you complete the above steps, you will be admitted to your chosen course and can start enjoying OPIT education once the first term begins. You’ll need to sign your admissions contract and pay the relevant fees, then begin classes.
Entry Requirements for OPIT Courses
OPIT offers a small curated collection of courses, each with its own requirements. You can consult the relevant pages on the official OPIT site to find out the exact details.
For the Foundation Program, for example, you simply need an MQF/EQF Level 3 or equivalent qualification. You also need to demonstrate a minimum B2 level of English comprehension.
For the BSc in Digital Business, applicants should have a higher secondary school leaving certificate, plus B2-level English comprehension. You can also support your application with a credit transfer from previous studies or relevant work experience.
Overall, the requirements are simple, and it’s most important for applicants to be ambitious and eager to build successful careers in the world of technology. Those who are driven and committed will get the best from OPIT’s instruction.
Fees and Flexible Payments at OPIT
As mentioned above, OPIT makes technological education accessible and affordable for all. Its tuition fees cover all relevant teaching materials, and there are no hidden costs or extras. The institute also offers flexible payment options for those with different budgets.
Again, exact fees vary depending on which course you want to take, so it’s important to consult the specific info for each one. You can pay in advance to enjoy 10% off the final cost, or refer a friend to also obtain a discount.
In addition to this, OPIT offers need-based and merit-based scholarships. Successful candidates can obtain discounts of up to 40% on bachelor’s and master’s tuition fees. This can substantially bring the term cost of each program down, making OPIT education even more accessible.
Credit Transfers and Experience
Those who are entering OPIT with pre-existing work experience or relevant academic achievements can benefit from the credit transfer program. This allows you to potentially skip certain modules or even entire semesters if you already have relevant experience in those fields.
OPIT is flexible and fair in terms of recognizing prior learning. So, as long as you can prove your credentials and experience, this could be a beneficial option for you. The easiest way to find out more and get started is to email the OPIT team directly.
Join OPIT Today
Overall, the process to join OPIT is designed to be as easy and stress-free as possible. Everything from the initial application forms to the interview and admission process is straightforward. Requirements and fees are flexible, so people in different situations and from different backgrounds can get the education they want. Reach out to OPIT today to take your first steps to tech success.
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