Think for a second about employees in diamond mines. Their job can often seem like trying to find a needle in a haystack. But once they find what they’re looking for, the feeling of accomplishment is overwhelming.

The situation is similar with data mining. Granted, you’re not on the hunt for diamonds (although that wouldn’t be so bad). The concept’s name may suggest otherwise, but data mining isn’t about extracting data. What you’re mining are patterns; you analyze datasets and try to see whether there’s a trend.

Data mining doesn’t involve you reading thousands of pages. This process is automatic (or at least semi-automatic). The patterns discovered with data mining are often seen as input data, meaning it’s used for further analysis and research. Data mining has become a vital part of machine learning and artificial intelligence as a whole. If you think this is too abstract and complex, you should know that data mining has found its purpose for every company. Investigating trends, prices, sales, and customer behavior is important for any business that sells products or services.

In this article, we’ll cover different data mining techniques and explain the entire process in more detail.

Data Mining Techniques

Here are the most popular data mining techniques.

Classification

As you can assume, this technique classifies something (datasets). Through classification, you can organize vast datasets into clear categories and turn them into classifiers (models) for further analysis.

Clustering

In this case, data is divided into clusters according to a certain criterion. Each cluster should contain similar data points that differ from data points in other clusters.

If we look at clustering from the perspective of artificial intelligence, we say it’s an unsupervised algorithm. This means that human involvement isn’t necessary for the algorithm to discover common features and group data points according to them.

Association Rule Learning

This technique discovers interesting connections and associations in large datasets. It’s pretty common in sales, where companies use it to explore customers’ behaviors and relationships between different products.

Regression

This technique is based on the principle that the past can help you understand the future. It explores patterns in past data to make assumptions about the future and make new observations.

Anomaly Detection

This is pretty self-explanatory. Here, datasets are analyzed to identify “ugly ducklings,” i.e., unusual patterns or patterns that deviate from the standard.

Sequential Pattern Mining

With this technique, you’re also on the hunt for patterns. The “sequential” indicates that you’re analyzing data where the values are in a sequence.

Text Mining

Text mining involves analyzing unstructured text, turning it into a structured format, and checking for patterns.

Sentiment Analysis

This data mining technique is also called opinion mining, and it’s very different from the methods discussed above. This complex technique involves natural language processing, linguistics, and speech analysis and wants to discover the emotional tone in a text.

Data Mining Process

Regardless of the technique you’re using, the data process consists of several stages that ensure accuracy, efficiency, and reliability.

Data Collection

As mentioned, data mining isn’t actually about identifying data but about exploring patterns within the data. To do that, you obviously need a dataset you want to analyze. The data needs to be relevant, otherwise you won’t get accurate results.

Data Preprocessing

Whether you’re analyzing a small or large dataset, the data within it could be in different formats or have inconsistencies or errors. If you want to analyze it properly, you need to ensure the data is uniform and organized, meaning you need to preprocess it.

This stage involves several processes:

• Data cleaning
• Data transformation
• Data reduction

Once you complete them, your data will be prepared for analysis.

Data Analysis

You’ve come to the “main” part of the data mining process, which consists of two elements:

• Model building
• Model evaluation

Model building represents determining the most efficient ways to analyze the data and identify patterns. Think of it this way: you’re asking questions, and the model should be able to provide the correct answers.

The next step is model evaluation, where you’ll step back and think about the model. Is it the right fit for your data, and does it meet your criteria?

Interpretation and Visualization

The journey doesn’t end after the analysis. Now it’s time to review the results and come to relevant conclusions. You’ll also need to present these conclusions in the best way possible, especially if you conducted the analysis for someone else. You want to ensure that the end-user understands what was done and what was discovered in the process.

Deployment and Integration

You’ve conducted the analysis, interpreted the results, and now you understand what needs to be changed. You’ll use the knowledge you’ve gained to elicit changes.

For example, you’ve analyzed your customers’ behaviors to understand why the sales of a specific product dropped. The results showed that people under the age of 30 don’t buy it as often as they used to. Now, you face two choices: You can either advertise the product and focus on the particular age group or attract even more people over the age of 30 if that makes more sense.

Applications of Data Mining

The concept of data mining may sound too abstract. However, it’s all around us. The process has proven invaluable in many spheres, from sales to healthcare and finance.

Here are the most common applications of data mining.

Customer Relationship Management

Your customers are the most important part of your business. After all, if it weren’t for them, your company wouldn’t have anyone to sell the products/services to. Yes, the quality of your products is one way to attract and keep your customers. But quality won’t be enough if you don’t value your customers.

Whether they’re buying a product for the first or the 100th time, your customers want to know you want to keep them. Some ways to do so are discounts, sales, and loyalty programs. Coming up with the best strategy can be challenging to say the least, especially if you have many customers belonging to different age groups, gender, and spending habits. With data mining, you can group your customers according to specific criteria and offer them deals that suit them perfectly.

Fraud Detection

In this case, you analyze data not to find patterns but to find something that stands out. This is what banks do to ensure no unwanted guests are accessing your account. But you can also see this fraud detection in the business world. Many companies use it to identify and remove fake accounts.

Market Basket Analysis

With data mining, you can get answers to an important question: “Which items are often bought together?” If this is on your mind, data mining can help. You can perform the association technique to discover the patterns (for example, milk and cereal) and use this valuable intel to offer your customers top-notch recommendations.

Healthcare and Medical Research

The healthcare industry has benefited immensely from data mining. The process is used to improve decision-making, generate conclusions, and check whether a treatment is working. Thanks to data mining, diagnoses have become more precise, and patients get more quality services.

As medical research and drug testing are large parts of moving the entire industry forward, data mining found its role here, too. It’s used to keep track of and reduce the risk of side effects of different medications and assist in administration.

Social Media Analysis

This is definitely one of the most lucrative applications. Social media platforms rely on it to pick up more information about their users to offer them relevant content. Thanks to this, people who use the same network will often see completely different posts. Let’s say you love dogs and often watch videos about them. The social network you’re on will recognize this and offer you even more dog videos. If you’re a cat person and avoid dog videos at all costs, the algorithm will “understand” this and offer you more videos starring cats.

Finance and Banking

Data mining analyzes markets to discover hidden patterns and make accurate predictions. The process is also used to check a company’s health and see what can be improved.

In banking, data mining is used to detect unusual transactions and prevent unauthorized access and theft. It can analyze clients and determine whether they’re suitable for loans (whether they can pay them back).

Challenges and Ethical Considerations of Data Mining

While it has many benefits, data mining faces different challenges:

• Privacy concerns – During the data mining process, sensitive and private information about users can come to light, thus jeopardizing their privacy.
• Data security – The world’s hungry for knowledge, and more and more data is getting collected and analyzed. There’s always a risk of data breaches that could affect millions of people worldwide.
• Bias and discrimination – Like humans, algorithms can be biased, but only if the sample data leads them toward such behavior. You can prevent this with precise data collection and preprocessing.
• Legal and regulatory compliance – Data mining needs to be conducted according to the letter of the law. If that’s not the case, the users’ privacy and your company’s reputation are at stake.

Track Trends With Data Mining

If you feel lost and have no idea what your next step should be, data mining can be your life support. With it, you can make informed decisions that will drive your company forward.

Considering its benefits, data mining will continue to be an invaluable tool in many niches.

When you’re faced with a task, you often wish you had the help of a friend. As they say, two heads are better than one, and collaboration can be the key to solving a problem or overcoming a challenge. With computer networks, we can say two nodes are better than one. These unique environments consist of at least two interconnected nodes that share and exchange data and resources, for which they use specific rules called “communications protocols.” Every node has its position within the network and a name and address to identify it.

The possibilities of computer networks are difficult to grasp. They make transferring files and communicating with others on the same network a breeze. The networks also boost storage capacity and provide you with more leeway to meet your goals.

One node can be powerful, but a computer network with several nodes can be like a super-computer capable of completing challenging tasks in record times.

In this introduction to computer networks, we’ll discuss the different types in detail. We’ll also tackle their applications and components and talk more about network topologies, protocols, and security.

Components of a Computer Network

Let’s start with computer network basics. A computer network is comprised of components that it can’t function without. These components can be divided into hardware and software. The easiest way to remember the difference between the two is to know that software is something “invisible,” i.e., stored inside a device. Hardware components are physical objects we can touch.

Hardware Components

• Network interface cards (NICs) – This is the magic part that connects a computer to a network or another computer. There are wired and wireless NICs. Wired NICs are inside the motherboard and connect to cables to transfer data, while wireless NICs have an antenna that connects to a network.
• Switches – A switch is a type of mediator. It’s the component that connects several devices to a network. This is what you’ll use to send a direct message to a specific device instead of the entire network.
• Routers – This is the device that uses an internet connection to connect to a local area network (LAN). It’s like a traffic officer who controls and directs data packets to networks.
• Hubs – This handy component divides a network connection into multiple computers. This is the distribution center that receives information requests from a computer and places the information to the entire network.
• Cables and connectors – Different types of cables and connectors are required to keep the network operating.

Software Components

• Network operating system (NOS) – A NOS is usually installed on the server. It creates an adequate environment for sharing and transmitting files, applications, and databases between computers.
• Network protocols – Computers interpret network protocols as guidelines for data communication.
• Network services – They serve as bridges that connect users to the apps or data on a specific network.

Types of Computer Networks

Local Area Network (LAN)

This is a small, limited-capacity network you’ll typically see in small companies, schools, labs, or homes. LANs can also be used as test networks for troubleshooting or modeling.

The main advantage of a local area network is convenience. Besides being easy to set up, a LAN is affordable and offers decent speed. The obvious drawback is its limited size.

Wide Area Network (WAN)

In many aspects, a WAN is similar to a LAN. The crucial difference is the size. As its name indicates, a WAN can cover a large space and can “accept” more users. If you have a large company and want to connect your in-office and remote employees, data centers, and suppliers, you need a WAN.

These networks cover huge areas and stretch across the globe. We can say that the internet is a type of a WAN, which gives you a good idea of how much space it covers.

The bigger size comes at a cost. Wide area networks are more complex to set up and manage and cost more money to operate.

Metropolitan Area Network (MAN)

A metropolitan area network is just like a local area network but on a much bigger scale. This network covers entire cities. A MAN is the golden middle; it’s bigger than a LAN but smaller than a WAN. Cable TV networks are the perfect representatives of metropolitan area networks.

A MAN has a decent size and good security and provides the perfect foundation for a larger network. It’s efficient, cost-effective, and relatively easy to work with.

As far as the drawbacks go, you should know that setting up the network can be complex and require the help of professional technicians. Plus, a MAN can suffer from slower speed, especially during peak hours.

Personal Area Network (PAN)

If you want to connect your technology devices and know nobody else will be using your network, a PAN is the way to go. This network is smaller than a LAN and can interconnect devices in your proximity (the average range is about 33 feet).

A PAN is simple to install and use and doesn’t have components that can take up extra space. Plus, the network is convenient, as you can move it around without losing connection. Some drawbacks are the limited range and slower data transfer.

These days, you encounter PANs on a daily basis: smartphones, gaming consoles, wireless keyboards, and TV remotes are well-known examples.

Network Topologies

Network topologies represent ways in which elements of a computer network are arranged and related to each other. Here are the five basic types:

• Bus topology – In this case, all network devices and computers connect to only one cable.
• Star topology – Here, all eyes are on the hub, as that is where all devices “meet.” In this topology, you don’t have a direct connection between the devices; the hub acts as a mediator.
• Ring topology – Device connections create a ring; the last device is connected to the first, thus forming a circle.
• Mesh topology – In this topology, all devices belonging to a network are interconnected, making data sharing a breeze.
• Hybrid topology – As you can assume, this is a mix of two or more topologies.

Network Protocols

Network protocols determine how a device connected to a network communicates and exchanges information. There are the five most common types:

• Transmission Control Protocol/Internet Protocol (TCP/IP) – A communication protocol that interconnects devices to a network and lets them send/receive data.
• Hypertext Transfer Protocol (HTTP) – This application layer protocol transfers hypertext and lets users communicate data across the World Wide Web (www).
• File Transfer Protocol (FTP) – It’s used for transferring files (documents, multimedia, texts, programs, etc.)
• Simple Mail Transfer Protocol (SMTP) – It transmits electronic mails (e-mails).
• Domain Name System (DNS) – It converts domain names to IP addresses through which computers and devices are identified on a network.

Network Security

Computer networks are often used to transfer and share sensitive data. Without adequate network security, this data could end up in the wrong hands, not to mention that numerous threats could jeopardize the network’s health.

Here are the types of threats you should be on the lookout for:

• Viruses and malware – These can make your network “sick.” When they penetrate a system, viruses and malware replicate themselves, eliminating the “good” code.
• Unauthorized access – These are guests who want to come into your house, but you don’t want to let them in.
• Denial of service attacks – These dangerous attacks have only one goal: making the network inaccessible to the users (you). If you’re running a business, these attacks will also prevent your customers from accessing the website, which can harm your company’s reputation and revenue.

What can you do to keep your network safe? These are the best security measures:

• Firewalls – A firewall acts as your network’s surveillance system. It uses specific security rules as guidelines for monitoring the traffic and spotting untrusted networks.
• Intrusion detection systems – These systems also monitor your network and report suspicious activity to the administrator or collect the information centrally.
• Encryption – This is the process of converting regular text to ciphertext. Such text is virtually unusable to everyone except authorized personnel who have the key to access the original data.
• Virtual private networks (VPNs) – These networks are like magical portals that guarantee safe and private connections thanks to encrypted tunnels. They mask your IP address, meaning nobody can tell your real location.
• Regular updates and patches – These add top-notch security features to your network and remove outdated features at the same time. By not updating your network, you make it more vulnerable to threats.

Reap the Benefits of Computer Networks

Whether you need a network for a few personal devices or want to connect with hundreds of employees and suppliers, computer networks have many uses and benefits. They take data sharing, efficiency, and accessibility to a new level.

If you want your computer network to function flawlessly, you need to take good care of it, no matter its size. This means staying in the loop about the latest industry trends. We can expect to see more AI in computer networking, as it will only make them even more beneficial.

Data is the heartbeat of the digital realm. And when something is so important, you want to ensure you deal with it properly. That’s where data structures come into play.

But what is data structure exactly?

In the simplest terms, a data structure is a way of organizing data on a computing machine so that you can access and update it as quickly and efficiently as possible. For those looking for a more detailed data structure definition, we must add processing, retrieving, and storing data to the purposes of this specialized format.

With this in mind, the importance of data structures becomes quite clear. Neither humans nor machines could access or use digital data without these structures.

But using data structures isn’t enough on its own. You must also use the right data structure for your needs.

This article will guide you through the most common types of data structures, explain the relationship between data structures and algorithms, and showcase some real-world applications of these structures.

Armed with this invaluable knowledge, choosing the right data structure will be a breeze.

Types of Data Structures

Like data, data structures have specific characteristics, features, and applications. These are the factors that primarily dictate which data structure should be used in which scenario. Below are the most common types of data structures and their applications.

Primitive Data Structures

Take one look at the name of this data type, and its structure won’t surprise you. Primitive data structures are to data what cells are to a human body – building blocks. As such, they hold a single value and are typically built into programming languages. Whether you check data structures in C or data structures in Java, these are the types of data structures you’ll find.

• Integer (signed or unsigned) – Representing whole numbers
• Float (floating-point numbers) – Representing real numbers with decimal precision
• Character – Representing integer values as symbols
• Boolean – Storing true or false logical values

Non-Primitive Data Structures

Combine primitive data structures, and you get non-primitive data structures. These structures can be further divided into two types.

Linear Data Structures

As the name implies, a linear data structure arranges the data elements linearly (sequentially). In this structure, each element is attached to its predecessor and successor.

The most commonly used linear data structures (and their real-life applications) include the following:

• In arrays, multiple elements of the same type are stored together in the same location. As a result, they can all be processed relatively quickly. (library management systems, ticket booking systems, mobile phone contacts, etc.)
• Linked lists. With linked lists, elements aren’t stored at adjacent memory locations. Instead, the elements are linked with pointers indicating the next element in the sequence. (music playlists, social media feeds, etc.)
• These data structures follow the Last-In-First-Out (LIFO) sequencing order. As a result, you can only enter or retrieve data from one stack end (browsing history, undo operations in word processors, etc.)
• Queues follow the First-In-First-Out (FIFO) sequencing order (website traffic, printer task scheduling, video queues, etc.)

Non-Linear Data Structures

A non-linear data structure also has a pretty self-explanatory name. The elements aren’t placed linearly. This also means you can’t traverse all of them in a single run.

• Trees are tree-like (no surprise there!) hierarchical data structures. These structures consist of nodes, each filled with specific data (routers in computer networks, database indexing, etc.)
• Combine vertices (or nodes) and edges, and you get a graph. These data structures are used to solve the most challenging programming problems (modeling, computation flow, etc.)

Advanced Data Structures

Venture beyond primitive data structures (building blocks for data structures) and basic non-primitive data structures (building blocks for more sophisticated applications), and you’ll reach advanced data structures.

• Hash tables. These advanced data structures use hash functions to store data associatively (through key-value pairs). Using the associated values, you can quickly access the desired data (dictionaries, browser searching, etc.)
• Heaps are specialized tree-like data structures that satisfy the heap property (every tree element is larger than its descendant.)
• Tries store strings that can be organized in a visual graph and retrieved when necessary (auto-complete function, spell checkers, etc.)

Algorithms for Data Structures

There is a common misconception that data structures and algorithms in Java and other programming languages are one and the same. In reality, algorithms are steps used to structure data and solve other problems. Check out our overview of some basic algorithms for data structures.

Searching Algorithms

Searching algorithms are used to locate specific elements within data structures. Whether you’re searching for specific data structures in C++ or another programming language, you can use two types of algorithms:

• Linear search: starts from one end and checks each sequential element until the desired element is located
• Binary search: looks for the desired element in the middle of a sorted list of items (If the elements aren’t sorted, you must do that before a binary search.)

Sorting Algorithms

Whenever you need to arrange elements in a specific order, you’ll need sorting algorithms.

• Bubble sort: Compares two adjacent elements and swaps them if they’re in the wrong order
• Selection sort: Sorts lists by identifying the smallest element and placing it at the beginning of the unsorted list
• Insertion sort: Inserts the unsorted element in the correct position straight away
• Merge sort: Divides unsorted lists into smaller sections and orders each separately (the so-called divide-and-conquer principle)
• Quick sort: Also relies on the divide-and-conquer principle but employs a pivot element to partition the list (elements smaller than the pivot element go back, while larger ones are kept on the right)

Tree Traversal Algorithms

To traverse a tree means to visit its every node. Since trees aren’t linear data structures, there’s more than one way to traverse them.

• Pre-order traversal: Visits the root node first (the topmost node in a tree), followed by the left and finally the right subtree
• In-order traversal: Starts with the left subtree, moves to the root node, and ends with the right subtree
• Post-order traversal: Visits the nodes in the following order: left subtree, right subtree, the root node

Graph Traversal Algorithms

Graph traversal algorithms traverse all the vertices (or nodes) and edges in a graph. You can choose between two:

• Depth-first search – Focuses on visiting all the vertices or nodes of a graph data structure located one above the other
• Breadth-first search – Traverses the adjacent nodes of a graph before moving outwards

Applications of Data Structures

Data structures are critical for managing data. So, no wonder their extensive list of applications keeps growing virtually every day. Check out some of the most popular applications data structures have nowadays.

Data Organization and Storage

With this application, data structures return to their roots: they’re used to arrange and store data most efficiently.

Database Management Systems

Database management systems are software programs used to define, store, manipulate, and protect data in a single location. These systems have several components, each relying on data structures to handle records to some extent.

Let’s take a library management system as an example. Data structures are used every step of the way, from indexing books (based on the author’s name, the book’s title, genre, etc.) to storing e-books.

File Systems

File systems use specific data structures to represent information, allocate it to the memory, and manage it afterward.

Data Retrieval and Processing

With data structures, data isn’t stored and then forgotten. It can also be retrieved and processed as necessary.

Search Engines

Search engines (Google, Bing, Yahoo, etc.) are arguably the most widely used applications of data structures. Thanks to structures like tries and hash tables, search engines can successfully index web pages and retrieve the information internet users seek.

Data Compression

Data compression aims to accurately represent data using the smallest storage amount possible. But without data structures, there wouldn’t be data compression algorithms.

Data Encryption

Data encryption is crucial for preserving data confidentiality. And do you know what’s crucial for supporting cryptography algorithms? That’s right, data structures. Once the data is encrypted, data structures like hash tables also aid with value key storage.

Problem Solving and Optimization

At their core, data structures are designed for optimizing data and solving specific problems (both simple and complex). Throw their composition into the mix, and you’ll understand why these structures have been embraced by fields that heavily rely on mathematics and algorithms for problem-solving.

Artificial Intelligence

Artificial intelligence (AI) is all about data. For machines to be able to use this data, it must be properly stored and organized. Enter data structures.

Arrays, linked lists, queues, graphs, and stacks are just some structures used to store data for AI purposes.

Machine Learning

Data structures used for machine learning (MI) are pretty similar to other computer science fields, including AI. In machine learning, data structures (both linear and non-linear) are used to solve complex mathematical problems, manipulate data, and implement ML models.

Network Routing

Network routing refers to establishing paths through one or more internet networks. Various routing algorithms are used for this purpose and most heavily rely on data structures to find the best patch for the incoming data packet.

Data Structures: The Backbone of Efficiency

Data structures are critical in our data-driven world. They allow straightforward data representation, access, and manipulation, even in giant databases. For this reason, learning about data structures and algorithms further can open up a world of possibilities for a career in data science and related fields.

You may have heard the catchy phrase “data is the new oil” floating around. The implication is that data in the 21st century is what oil was in the 20th – the biggest industry around. And it’s true, as the sheer amount of data each person generates when they use the web, try out an app, or even buy from a store is digital “oil” for the companies collecting that data.

It’s also the fuel that powers the current (and growing) wave of artificial intelligence (AI) tools emerging in the market. From ChatGPT to the wave of text-to-speech tech flooding the market, everything hinges on information, and people who can harness that data through algorithms and machine learning practices are in high demand.

That’s where you can come in. By taking a Master’s degree in artificial intelligence online, you position yourself as one of the people who can help the new “digital oil” barons capitalize on their finds.

Factors to Consider When Choosing an Online AI Master’s Program

When choosing an artificial intelligence online Master’s, you have to consider more than the simple accessibility the course offers. These factors help you to weed out the also-ran programs from the ones that help you to advance your career:

• Accreditation – Checks for accreditation come in two flavors. First, you need to check the program provider’s credentials to ensure the degree you get from your studies is worth the paper on which it’s printed. Second, you have to confirm the accreditation you receive is something that employers actually want to see.
• Curriculum – What does your artificial intelligence online Master degree actually teach you? Answer that question and you can determine if the program serves the career goals you’ve set for yourself.
• Faculty Expertise – On the ground level, you want tutors with plenty of teaching experience and their own degrees in AI-related subjects. But dig beyond that to also discover if they have direct experience working with AI in industry.
• Program Format – A self-study artificial intelligence Master’s program’s online nature means they offer some degree of flexibility. But the course format plays a role in your decision, given that some rely solely on self-learning whereas others include examinations and live remote lectures.
• Tuition and Financial Aid – A Master’s degree costs quite a bit depending on area (prices range from €1,000 to €20,000 per year), so you need to be in the appropriate financial position. Many universities offer financial aid, such as scholarships, grants, and payment programs, that may help here.
• Career Support – You’re likely not studying for Master of artificial intelligence online for the joy of having a piece of paper on your wall. You want to build a career. Look for institutions that have strong alumni networks, connections within industry, and dedicated careers offices or services.

Top Online AI Master’s Programs Ranked

In choosing the best Master’s in artificial intelligence online programs, we looked at the above factors in addition to the key features of each program. That examination results in three online courses, each offering something a little different, that give you a solid grounding in AI.

Master in Applied Data Science & AI (OPIT)

Flexibility is the name of the game with OPIT’s program, as it’s fully remote and you get a choice between an 18-month course and a fast-tracked 12-month variant. The latter contains the same content as the former, with the student simply dedicating themselves to more intensive course requirements.

The program comes from an online institution that is accredited under both the Malta Qualification Framework and European Qualification Framework. As for the course itself, it’s the focus on real-life challenges in data science and AI that makes it so attractive. You don’t just learn theory. You discover how to apply that theory to the practical problems you’ll face when you enter the workforce.

OPIT has an admissions team who’ll guide you through getting onto the course, though you’ll need a BSc degree (in any field) and the equivalent of B2-level English proficiency to apply. If English isn’t your strong suit, OPIT also offers an in-house certification that you can take to get on the course. Financial aid is available through scholarships and funding, which you may need given that the program can cost up to €6,500, though discounts are available for those who apply early.

Master in Big Data, Artificial Intelligence, and Disruptive Technologies (Digital Age University)

If data is the new oil, Digital Age University’s program teaches you how to harness that oil and pump it in a way that makes you an attractive proposition for any employer. Key areas of study include the concept and utilization of Big Data (data analytics plays a huge role here), as well as the Python programming skills needed to create AI tools. You’ll learn more about machine learning models and get to grips with how AI is the big disruptor in modern business.

Tuition costs are reasonable, too, with this one-year course only costing €2,600. Digital Age University runs a tuition installment plan that lets you spread your costs out without worrying about being charged interest. Plus, your previous credentials may put you in line for a grant or scholarship that covers at least part of the cost. All first-year students are eligible for the 10% merit-based scholarship again, dependent on prior education). There’s also a 20% Global Scholarship available to students from Asia, Africa, the Middle East, and Latin American countries.

Speaking of credentials, you can showcase yours via the online application process or by scheduling a one-on-one call with one of the institution’s professors. The latter option is great if you’re conducting research and want to get a taste of what the faculty has to offer.

Master in Artificial Intelligence (Three Points Digital Business School)

Three Points Digital Business School sets its stall out early by pointing out that 83% of companies say they’ll create new jobs due to AI in the coming years. That’s its way of telling you that its business-focused AI course is the right choice for getting one of those jobs. After teaching the fundamentals of AI, the course moves into showing you how to create AI and machine learning models and, crucially, how to apply those models in practical settings. By the end, you’ll know how to program chatbots, virtual assistants, and similar AI-driven tools.

It’s the most expensive program on this list, clocking in at €7,500 for a one-year course that delivers 60 ECTS credits. However, it’s a course targeted at mature students (half of the current students are 40 years old), and it’s very much career-minded. That’s exemplified by Three Points’ annual ThinkDigital Summit, which puts some of the leading minds in AI and digital innovation in front of students.

Admission is tougher than for many other Master’s in artificial intelligence online programs as you go through an interview process in addition to submitting qualifications. Every candidate is manually assessed via committee, with your experience and business know-how playing as much of a role as any technical qualifications you have.

Tips for Success in an Online AI Master’s Program

Let’s assume you’ve successfully applied to an artificial intelligence online Master’s program. That’s the first step in a long, often complex, journey. Here are some tips to keep in mind and set up for the future:

• Manage your time properly by scheduling your study, especially given that online courses rely on students having the discipline needed for self-learning.
• Build relationships with faculty and peers who may be able to connect you to job opportunities or have ideas for starting their own businesses.
• Stay up-to-date on what’s happening with AI because this high-paced industry can leave people who assume what they know is enough behind.
• Pursue real-world experience wherever you can, both through the practical assessments a program offers and internship programs that you can add to your CV.

Career Opportunities With a Master’s in Artificial Intelligence

You need to know what sorts of roles are available on the digital “oil rigs” of today and the future. Those who have an artificial intelligence online Master degree take roles as varied as data analyst, software engineer, data scientist, and research scientist.

Better yet, those roles are spread across almost all industries. Grand View Research tells us that we can expect the AI market to enjoy a 37.3% compound annual growth rate between 2023 and 2030, with that growth making AI-based roles available on a near-constant basis. Salary expectations are likely to increase along with that growth, with the current average of around €91,000 for an artificial intelligence engineer (figures based on Germany’s job market) likely to be a baseline for future growth.

Find the Right Artificial Intelligence Master’s Programs Online

We’ve highlighted three online Master’s programs with a focus on AI in this article, each offering something different. OPIT’s course leans heavily into data science, giving you a specialization to go along with the foundational knowledge you’ll gain. Digital Age University’s program places more of a focus on Big Data, with Three Points Digital Business School living up to its name by taking a more business-oriented approach.

Whatever program you choose (and it could be one other than the three listed here), you must research the course based on the factors like credentials, course content, and quality of the faculty. Put plenty of time into this research process and you’re sure to find a program that aligns with your goals.

Machines that can learn on their own have been a sci-fi dream for decades. Lately, that dream seems to be coming true thanks to advances in AI, machine learning, deep learning, and other cutting-edge technologies.

Have you used Google’s search engine recently or admired the capabilities of ChatGPT? That means you’ve seen machine learning in action. Besides those renowned apps, the technology is widespread across many industries, so much so that machine learning experts are in increasingly high demand worldwide.

Chances are there’s never been a better time to get involved in the IT industry than today. This is especially true if you enter the market as a machine learning specialist. Fortunately, getting proficient in this field no longer requires enlisting in a college – now you can finish a Master in machine learning online.

Let’s look at the best online Masters in machine learning and data science that you can start from the comfort of your home.

Top MSc Programs in Machine Learning Online

Finding the best MSc machine learning online programs required us to apply certain strict criteria in the search process. The following is a list of programs that passed our research with flying colors. But first, here’s what we looked for in machine learning MSc courses.

Our Criteria

The criteria we applied include:

• The quality and reputation of the institution providing the course
• International degree recognition
• Program structure and curriculum
• Duration
• Pricing

Luckily, numerous world-class universities and organizations have a machine learning MSc online. Their degrees are accepted around the world, and their curricula count among the finest in the market. Take a look at our selection.

Imperial College London – Machine Learning and Data Science

The Machine Learning and Data Science postgraduate program from the Imperial College in London provides comprehensive courses on models applicable to real-life scenarios. The program features hands-on projects and lessons in deep learning, data processing, analytics, and machine learning ethics.

The complete program is online-based and relies mostly on independent study. The curriculum consists of 13 modules. With a part-time commitment, this program will last for two years. The fee is the same for domestic and overseas students: £16,200

European School of Data Science & Technology – MSc Artificial Intelligence and Machine Learning

If you need a Master’s program that combines the best of AI and machine learning, the European School of Data Science & Technology has an excellent offer. The MSc Artificial Intelligence and Machine Learning program provides a sound foundation of the essential concepts in both disciplines.

During the courses, you’ll examine the details of reinforcement learning, search algorithms, optimization, clustering, and more. You’ll also get the opportunity to work with machine learning in the R language environment.

The program lasts for 18 months and is entirely online. Applicants must cover a registration fee of €1500 plus monthly fees of €490.

European University Cyprus – Artificial Intelligence Master

The European University in Cyprus is an award-winning institution that excels in student services and engagement, as well as online learning. The Artificial Intelligence Master program from this university treats artificial intelligence in a broader sense. However, machine learning is a considerable part of the curriculum, being taught alongside NLP, robotics, and big data.

The official site of the European University Cyprus states the price for all computer science Master’s degrees at €8,460. However, it’s worth noting that there’s a program for financial support and scholarships. The duration of the program is 18 months, after which you’ll get an MSc in artificial intelligence.

Udacity – Computer Vision Nanodegree

Udacity has profiled itself as a leading learning platform. Its Nanodegree programs provide detailed knowledge on numerous subjects, such as this Computer Vision Nanodegree. The course isn’t a genuine MSc program, but it offers specialization for a specific field of machine learning that may serve for career advancement.

This program includes lessons on the essentials of image processing and computer vision, deep learning, object tracking, and advanced computer vision applications. As with other Udacity courses, learners will enjoy support in real-time as well as career-specific services for professional development after finishing the course.

This Nanodegree has a flexible schedule, allowing you to set a personalized learning pace. The course lasts for three months and has a fee of €944. Scholarship options are also available for this program, and there are no limitations in terms of applying for the course or starting the program.

Lebanese American University – MS in Applied Artificial Intelligence

Lebanese American University curates the MS in Applied Artificial Intelligence study program, led by experienced faculty members. The course is completely online and focuses on practical applications of AI programming, machine learning, data learning, and data science. During the program, learners will have the opportunity to try out AI solutions for real-life issues.

This MS program has a duration of two years. During that time, you can take eight core courses and 10 elective courses, including subjects like Healthcare Analytics, Big Data Analytics, and AI for Biomedical Informatics.

The price of this program is €6,961 per year. It’s worth noting that there’s a set application deadline and starting date for the course. The first upcoming application date is in July, with the program starting in September.

Data Science Degrees: A Complementary Path

Machine learning can be viewed as a subcategory of data science. While the former focuses on methods of supervised and unsupervised AI learning, the latter is a broad field of research. Data science deals with everything from programming languages to AI development and robotics.

Naturally, there’s a considerable correlation between machine learning and data science. In fact, getting familiar with the principles of data science can be quite helpful when studying machine learning. That’s why we compiled a list of degree programs for data science that will complement your machine learning education perfectly.

Top Online Data Science Degree Programs

Purdue Global – Online Bachelor of Science Degree in Analytics

Data analytics represents one of the essential facets of data science. The Online Bachelor of Science Degree in Analytics program is an excellent choice to get familiar with data science skills. To that end, the program may complement your machine learning knowledge or serve as a starting point for a more focused pursuit of data science.

The curriculum includes nine different paths of professional specialization. Some of those concentrations include cloud computing, network administration, game development, and software development in various programming languages.

Studying full-time, you should be able to complete the program within four years. Each course has a limited term of 10 weeks. The program in total requires 180 credits, and the price of one credit is $371 or its equivalent in euros.

Berlin School of Business and Innovation – MSc Data Analytics

MSc Data Analytics is a postgraduate program from the Berlin School of Business and Innovation (BSBI). As an MSc curriculum, the program is relatively complex and demanding, but will be more than worthwhile for anyone wanting to gain a firm grasp of data analytics.

This is a traditional on-campus course that also has an online variant. The program focuses on data analysis and extraction and predictive modeling. While it could serve as a complementary degree to machine learning, it’s worth noting that this course may be the most useful for those pursuing a multidisciplinary approach.

This MSc course lasts for 18 months. Pricing differs between EU and non-EU students, with the former paying €8,000 and the latter €12,600.

Imperial College London – Machine Learning and Data Science

It’s apparent from the very name that this Imperial College London program represents an ideal mix. Machine Learning and Data Science combines the two disciplines, providing a thorough insight into their fundamentals and applications.

The two-year program is tailored for part-time learners. It consists of core modules like Programming for Data Science, Ethics in Data Science and Artificial Intelligence, Deep Learning, and Applicable Mathematics.

This British-based program costs £16,200 yearly, both for domestic and overseas students. Some of the methods include lectures, tutorials, exercises, and reading materials.

Thriving Career Opportunities With a Masters in Machine Learning Online

Jobs in machine learning require proper education. The chances of becoming a professional in the field without mastering the subject are small – the industry needs experts.

A Master’s degree in machine learning can open exciting and lucrative career paths. Some of the best careers in the field include:

• Data scientist
• Machine learning engineer
• Business intelligence developer
• NLP scientist
• Software engineer
• Machine learning designer
• Computational linguist
• Software developer

These professions pay quite well across the EU market. The median annual salary for a machine learning specialist is about €70,000 in Germany, €68,000 in the Netherlands, €46,000 in France, and €36,000 in Italy.

On the higher end, salaries in these countries can reach €98,000, €113,000, €72,000, and €65,000, respectively. To reach these more exclusive salaries, you’ll need to have a quality education in the field and a level of experience.

Become Proficient in Machine Learning Skills

Getting a Master’s degree in machine learning online is convenient, easily accessible, and represents a significant career milestone. With the pace at which the industry is growing today, it would be a wise choice.

Since the best programs offer a thorough education, great references, and a chance for networking, there’s no reason not to check out the courses on offer. Ideally, getting the degree could mark the start of a successful career in machine learning.

Take a sprinkling of math, add some statistical analysis, and coat with the advanced programming and analytics that enables people to pore through enormous batches of data and you have the recipe for a data scientist.

These professionals (and their data-based talents) are sought after in industries of all shapes and sizes. Every sector from healthcare, finance, and retail to communications and even the government can make use of the skills of data scientists to advance. That’s great news if you’re considering completing your Master’s degree in the subject, as your degree is the key that can unlock the door to a comfortable five-figure salary.

Here, we look at the Master’s in data science salary and explain what you can do to maximize your potential.

Masters in Data Science: An Overview

As a postgraduate degree course, a Masters in data science builds on some of the core skills you’ll learn in a computer science or information technology degree. Think of it as a specialization. You’ll expand on the programming and analytical skills you’ve already developed to learn how to extract actionable insights from massive datasets. In the world of Big Data (where companies generate more data than at any other point in history), those skills are more important than ever.

Speaking of skills, you’ll develop or hone the following when studying for your Master’s in data science:

• Data Analysis – The ability to analyze data (i.e., interpret what seemingly random datasets tell you) is one of the first skills you’ll pick up in your degree.
• Data Visualization – Where your analysis helps you to see what you’re looking at, data visualization is all about representing that data visually so that others see what you see.
• AI and Machine Learning – The nascent technologies involved in the artificial intelligence sector revolve around data, in addition to many modern AI technologies being helpful for analyzing data. You’ll learn both sides, developing the skills to both create and use AI.
• Software Engineering and Programming – Don’t assume the programming skills you have from your previous degree will go to waste, as you’ll need them for a data science Master’s. You’ll use plenty of new tools, in addition to picking up more skills in languages like Python, SQL, and R.
• Soft Skills – A Master’s in data science isn’t all technical. You’ll develop some soft skills that prove useful in the workplace, such as communication, basic teamwork, and management. Most data science courses also teach ethics so you can get to grips with the idea of doing the right thing with data.

The Top Universities for a Data Science Masters

According to the university rating website Collegedunia, there are more than 60 leading data sciences universities in the United States alone, each offering both Bachelor’s and Master’s degrees in the subject. It ranks the following as the top five institutions for getting your Master’s in data science:

• MIT – As the top data science university in the world (according to the QS Global Rankings), MIT is the first choice for any prospective student.
• Harvard University – The “Harvard” name carries weight regardless of the course you choose. Data scientists have their pick of a standard Master’s in data science or a course dedicated to health data science.
• Columbia University – Those who want to fast-track their studies may find that the intensive one-year data science Master’s that Columbia offers is a better choice than traditional two-year courses.
• John Hopkins University – Though it’s best known as one of America’s best medical schools, John Hopkins also has a strong data science department. It may be a great choice for those who want to use their data science skills to get into the medical field.
• Northwestern University – Ranking at 30 in the QS Global Rankings, Northwestern offers Master’s degrees in both data science and analytics, with the latter expanding on one of the core skills needed for data science.

Masters in Data Science Salary Potential

As great as the skills you’ll get will be, you want to know more about the Master’s in data science salary you can expect to earn.

The good news is that a strong salary isn’t just possible. It’s likely. According to Indeed, the average salary for a data scientist is £49,749 in the UK. Cult.Honeypot has interesting figures for Europe as a whole, noting that the average data scientist on the continent earns €60,815, which matches up well to general salary expectations of €60,000. You can also expect a position in this field to come with numerous benefits, including medical insurance (where relevant) and flexible working conditions.

Of course, there are several factors that influence your specific earning power:

• Geographic location
• The specific industry in which you work
• Your experience level
• The size of the company for which you work

For example, a brand-new graduate who takes a position at a start-up in a non-tech industry may find that they earn at the lower end of the scale, though they’ll develop experience that could serve them well later on.

Data scientists also tend to have higher salary prospects than those in comparable fields. For example, more data from Indeed shows us that data scientists in the UK earn more, on average, than software engineers (£49,409), computer scientists (£45,245), and computer engineers (£24,780). Furthermore, a Master’s in data science is wide-ranging enough that it’ll give you many of the skills you need for the above industries, assuming you’d want a career change or discover that data science isn’t for you.

Benefits of a Masters in Data Science for Earning Power

It’s clear that the Master’s in data science salary potential is strong, with mid-five-figure salaries being the standard (rather than the exception) for the industry. But there are benefits beyond potential earnings that make the Master’s course a good one to take.

More Job Opportunities

Data science is everywhere in modern industry because every company produces data. You can apply your skills in industries like healthcare, manufacturing, and retail, meaning you have plenty of job opportunities. The research backs this statement up, too, with figures from Polaris Market Research suggesting a 27.6% compound annual growth rate (CAGR) for the data science industry between 2022 and 2030.

Greater Job Security

The encroachment of AI into almost every aspect of our lives has many people worried about job security. Some even speculate that machines will take over many roles in the coming years. Data scientists don’t have to worry about that. Not only will you use AI to advance your research, but you may also be responsible for further developments in the AI and machine learning fields. All of which will make you crucial to the continuation of the AI trend.

Opportunities for Career Advancement

The salary figure quoted above (average salary of €60,815) is for a fairly standard data science role. Opportunities for career advancement exist, whether that be simply moving into a more senior position in a company or taking control of a team, thus adding management to your skill set. Those who prefer conducting research will also find that many universities and large companies have teams dedicated to using data science to create social and commercial change.

Tips for Maximizing Earnings With a Masters in Data Science

With the Master’s in data science salary potential already being attractive enough (six figures is a great start), you may not worry too much about maximizing your earning potential at the start of your career. But as you get deeper into your working life, the following tips will help you get more money in return for the skills you bring to the table.

1 – Choose the Right University and Program

Universities aren’t built equally, with some carrying more weight than others. For example, a data science Master’s degree from MIT holds huge weight because it’s one of America’s top universities for the subject. Employers know what the school is about, understand that those who study there undergo superb training, and will thus be more willing to both hire and offer more money to its graduates. The point is that where you go (and what you study in your course) influences how employers see you, which also influences your earning potential.

2 – Gain Relevant Work Experience

As with any career path, what you learn along the path is as valuable as the skills you pick up when studying. You can get a head start on other data science graduates if you take on internships or get involved in research projects while studying, giving you some work experience to add to your resume that could lead to higher initial salary offers.

3 – Leverage Networking and Connections

Meeting the right people at the right times can do wonders for your career. Studying for a Master’s in data science exposes you to professors (and even people who work in the industry) who can put you in touch with people who offer roles in the industry. Continuous building on these connections, from staying active in the industry to leveraging social media, offers more opportunities for advancement.

4 – Stay Up-to-Date With Industry Trends

Data science is a fast-moving sector, with constant advancements occurring at both the high level (the evolution of AI) and in terms of how we use data science in different industries. Keeping on top of these advancements means you stay “in the know” and can see potential career paths branching out before you.

5 – Pursue Additional Qualifications

Keeping with the theme of staying up-to-date, how you build on your skills via continuing education can influence your salary potential. A Master’s degree in data science is impressive. But a degree supplemented by specialized certifications, proof of bootcamp completion, and any other accolades puts you ahead of the pack of other graduates.

Turn Your Master’s in Data Science Into a Great Career

In addition to opening you up to an exciting career in a field that’s undergoing tremendous growth, a Master’s in data science comes with mid-five-figure salary potential. You can boost your Master’s in data science salary expectations through networking, specialization, and simply staying up-to-date with what’s happening in the industry.

Granted, there are time and monetary commitments involved. You usually dedicate two years of your life to getting your degree (though some universities offer one-year data science Master’s courses) and you’ll pay a five-figure sum for your education. But the benefits on the backend of that commitment are so vast that a Master’s in data science may be the key to unlocking huge earnings in the data industry.

Most of the modern world – work, private life, and entertainment – revolves around computers and IT in general. Naturally, this landscape creates a high demand for computer science jobs. As a result, BSc Computer Science positions are well-paid and offer excellent career opportunities.

With all these advantages considered, it’s no wonder that people from other professions pivot toward computer science. This includes biology students, too.

But can a biology student do BSc Computer Science? And, equally as important, should they?

The answer to the first question is relatively complex and will represent the bulk of this article. But the second answer is a resounding yes. Interdisciplinary education can be a massive advantage in today’s world, providing venues for innovation and greater career advances.

Let’s delve deeper into the question of can a biology student do BSc Computer Science.

Background on BSc Computer Science

A BSc degree is often a part of professional development for people interested in IT. The degree usually follows a core computer science course. After obtaining the BSc, you can move forward towards a specialization or pursue a PhD in the field.

As a biology student, your path to BSc Computer Science will be different. The first step on the way is to understand what computer science is, which areas it covers, and what core skills it requires. This section will explain just that, plus the career opportunities that come with BSc Computer Science.

Definition and Scope

Computer science deals with computer systems. If you’re (rightfully) wondering what that means precisely, the answer is: practically anything related to computers.

A computer scientist can work on the architecture and structure of a processor chip. On the other hand, their colleague could be engaged in supporting the structure of the internet. Both roles fall under the umbrella of computer science.

At its core, this branch of IT concerns with questions about the nature of computing. In that light, one of the computer scientist’s main tasks is to understand what a computer system is. Then, these professionals can move onto designing different systems for particular purposes.

Core Subjects and Skills

BSc Computer Science courses teach core subjects that provide the essential skills for the job. As you might presume, programming is the crucial skill of a computer scientist. This skill requires proficiency in programming languages and a deep understanding of data structures. In addition, knowing the ins and outs of algorithms is pivotal for programming.

Software development is another skill that computer scientists must have. Besides coding knowledge, this skill calls for high proficiency in the principles of software engineering. A good computer scientists should be able to perform the entire development process from coding to implementation.

Computer science calls for a good understanding of math basics like algebra and calculus. However, advanced techniques will also be necessary.

Finally, a computer scientist should have a firm grasp on data analysis and visualization. The former improves professional capabilities, while the latter helps communicate the data to the stakeholders.

Core subjects in BSc Computer Science courses that tackle these and other skills include:

• Programming principles
• Computer networks
• Computer architectures
• Foundational mathematics
• Data structures and Algorithms
• Web development
• Introduction to operating systems
• Cloud computing
• Programming paradigms

Job Prospects and Career Opportunities

Employment in the computer science sector is growing rapidly, following a trend that’s projected to continue throughout the decade. The U.S. Bureau of Labor Statistics expects a 15% growth in the computer science landscape, along with hundreds of thousands of new jobs.

As the IT sector keeps innovating, even more jobs may become available. After all, many of today’s most desired professions didn’t exist at the start of the century, and computer science is developing rapidly.

Some of the career opportunities in computer science are for programmers, systems analysts, support specialists, software and computer engineers, and data scientists.

Comparing Biology and Computer Science

The question of can a biology student do BSc Computer Science comes down to a few crucial considerations. One of the first things you might ask is: what do computer science and biology even have in common.

Surprisingly, there are considerable similarities between the two fields.

Similarities

The most obvious aspect that computer science and biology share is that both are scientific disciplines. This means that the scientific approach is a hard requirement for both fields.

Biology and computer science aim to solve problems following two crucial methods: data analysis and interpretation and the scientific principle. A computer scientist will follow the same path to a conclusion as a biologist:

• Observation
• Question
• Hypothesis
• Prediction
• Testing
• Iteration

Furthermore, both disciplines will utilize mathematical models, although computer science will lean into math more than biology. Lastly, living organisms can be thought about as systems, which is somewhat similar to a computer scientist’s understanding of computers and other IT technologies.

Differences

Of course, the differences between biology and computer science will be much more evident. The two fields employ completely different sets of skills and require knowledge specific to their subjects. Naturally, people specializing in biology and computer science will also have completely different career paths.

When it comes to the underlying principles behind the two sciences, other crucial differences come to mind:

• Computer scientists regularly build artificial systems while biologists explore natural ones.
• As a science, biology is more based on observation, unlike the often experimental computer science.
• Biology is often regarded as an applied field, while computer science may be viewed as more abstract.

Assessing the Feasibility of a Biology Student Pursuing BSc Computer Science

Now that we’ve seen what makes biology and computer science similar in some regards and different in others, let’s return to the original question:

Can a biology student do BSc Computer Science?

To answer that question, we’ll need to look at two aspects. Firstly, doing a BSc in Computer Science comes with certain prerequisites. And second, you as a biology student must be ready and willing to adapt to the new field.

Analyzing the Prerequisites

The essential skills that are required for a BSc in Computer Science include programming and mathematics. As a biology student, you’ll likely already have some courses in math, which will make that part of the equation easier.

However, programming definitely won’t be a part of the standard biology curriculum. The same goes for other computer science skills.

Yet, this mismatch doesn’t mean that a biology student can’t pivot towards computer science. The process will only require more effort than for someone with a computer science background.

To enroll in a BSc Computer Science program, you’ll need to have a good grasp of the mentioned skills. Since studying biology doesn’t offer knowledge on programming or computer science in general, you’ll need to acquire those skills in addition to your primary studies.

The good news is that you won’t need any other specific knowledge besides math and the basics of programming and computer science. If you’re seriously considering transitioning into computer science, fulfilling these prerequisites will be well worth your while.

Evaluating the Adaptability

Besides the necessary entry-level knowledge for a BSc Computer Science, another factor will determine your success: whether you can adapt to the new field of study.

The similarities between biology and computer science will play a massive role here.

You can lean into your understanding of the scientific principle and apply it to computer systems rather than biological organisms. The transition can be viewed as following the same general methods but using them on a different subject.

Also, data collection and analysis skills will be an excellent foundation for computer science. These skills are vital in biology. Luckily, they also represent an essential part of computer science, so you’ll be able to apply them to the new discipline relatively easy.

Granted, the usefulness of your prior knowledge and skills will reach a limit at a point. Then, you’ll need to show another crucial quality: the willingness to adopt new concepts and learn new subjects.

Your advantage will be in the foundational scientific skills that you’ll have as a biologist. Building on those skills with computer science-specific knowledge will make your transition smoother. The key consideration here will be that you’re ready to learn.

Options for Biology Students to Transition Into BSc Computer Science

The final part of answering the question of can a biology student do BSc Computer Science is the practical method of transitioning. You’ll have several options in that regard:

• Enroll in a bridge course or a preparatory program
• Complete an online course and get the appropriate certification
• Rather than biology alone, opt for an interdisciplinary degree or a dual-degree program
• Pursue a biology degree simultaneously with a computer science minor

Each of these options will help you gain the necessary knowledge for the BSc and prepare for a career in computer science.

Can a Biology Student Do BSc Computer Science? Absolutely!

As you’ve seen, the path from a biology student to BSc in Computer Science isn’t a straight one. However, it’s completely achievable if you have the motivation.

Getting interdisciplinary education will represent an excellent opportunity for professional growth. Better yet, it will open up your possibilities for personal development as well. Learning about a new discipline is always a benefit, even if you pursue a different career path later in life.

If computer science sounds like an interesting prospect, nothing stops you from following that line of study. Fortunately, the opportunities for just that are readily available. Enlist in a quality BSc course and start building your knowledge base and skills.

AI is already a massive industry – valued at $136.55 billion (approx. €124.82 billion) as of 2022 – and it’s only going to get bigger as we come to grips with what AI can do. As a student, you stand on the cusp of the AI tidal wave and you have an opportunity to ride that wave into a decades-long career.

But you need a starting point for that career – a BSc computer science with artificial intelligence. The three courses discussed in this article are the best for budding AI masters.

Factors to Consider When Choosing a BSc Computer Science With AI Program

Before choosing your BSc, you need to know what to look for in a good course:

• Institution Accreditation – Whoever provides the course should offer solid accreditation so that you know you can trust the institution and that potential future employers actually respect the qualification you have on your VC.
• An AI-Focused Curriculum – Not all computer science bachelor’s degrees are the same. The one you choose needs to offer a specific focus on AI or machine learning so you can build the foundations for later specialization.
• Faculty Expertise – A course led by instructors who don’t know much about AI is like the blind leading the blind. Every mentor, instructor, and lecturer needs to have provable knowledge and industry experience.
• Job Opportunities – Every chance you have to “get your hands dirty” with AI is going to look great on your CV. Look for courses that create pathways into internships and job programs. Associations with organizations like IBM are a great place to start.
• Financial Aid – It isn’t cheap to study a BSc artificial intelligence and machine learning. Degrees cost thousands of Euros per year (the average in Europe is about €3,000, though prices can go higher) so the availability of financial aid is a huge help.

Top BSc Computer Science With AI Programs

Studying from the best is how you become a leader in the AI field. The combination of expert tuition and the name recognition that comes from having a degree from one of the following institutions stands you in good stead for success in the AI industry. Here are the top three organizations (with degrees available to overseas students) in the world.

Course 1 – BSc Artificial Intelligence – The University of Edinburgh

Named as one of the top 10 AI courses in the world by Forbes, The University of Edinburgh’s offering has everything you need from a great BSc computer science with artificial intelligence. It’s a four-year full-time course that focuses on the applications of AI in the modern world, with students developing the skills to build intelligent systems capable of making human-like decisions. The course is taught by the university’s School of Informatics, led by National Robotarium academic co-lead Professor Helen Hastie.

The course starts simple, with the first year dedicated to learning the language of computers before the second year introduces students to software development and data science concepts. By the third year, you’ll be digging deep into machine learning and robotics. That year also comes with opportunities to study abroad.

As for career prospects, The University of Edinburgh has a Careers Service department that can put you in line for internships at multi-national businesses. Add to that the university’s huge alumni network (essentially a huge group of professionals willing to help students with their careers) and this is a course that offers a great route into the industry.

Course 2 – Artificial Intelligence Program – Carnegie Mellon University

Ranked as the top university in the world for AI courses by Edurank, Carnegie Mellon University is a tough nut to crack if you want to study its world-renowned program. You’ll face a ton of competition, as evidenced by the university’s 17% acceptance rate, and the program is directed by Reid Simmons. For those who don’t recognize the name, he’s been a frontrunner in leveraging AI for NASA and was the creator of the “Robotceptionist.”

As for the course, it blends foundational mathematical, statistical, and computer science concepts with a wide variety of AI modules. It’s robotics-focused (that’s no surprise given the director), though you’ll also learn how AI applies on a perceptive level. The use of AI in speech processing, search engines, and even photography are just some examples of the concepts this course teaches.

Carnegie Mellon takes an interesting approach to internships, as it offers both career and academic internships. Career internships are what you’d expect – placements with major companies where you get to put your skills into practice. An academic internship is different because you’ll be based in the university and will work alongside its faculty on research projects.

Course 3 – BSc in Artificial Intelligence and Decision Making – Massachusetts Institute of Technology (MIT)

It should come as no surprise that MIT makes it onto the list given the school’s engineering and tech focus. Like Carnegie Mellon’s AI course, it’s tough to get into the MIT course (only a 7% acceptance rate) but simply having MIT on your CV makes you attractive to employers.

The course takes in multiple foundational topics, such as programming in Python and introductions to machine learning algorithms, before moving into a robotics focus in its application modules. But it’s the opportunities for research that make this one stand out. MIT has departments dedicated to the use of AI in society, healthcare, communications, and speech processing, making this course ideal for those who wish to pursue a specialization.

Networking opportunities abound, too. MIT’s AI faculty has 92 members, all with different types of expertise, who can guide you on your path and potentially introduce you to career opportunities. Combine that with the fact you’ll be working with some of the world’s best and brightest and you have a course that’s built for your success in the AI industry.

Emerging BSc Computer Science With AI programs

Given that AI is clearly going to be enormously important to developing industry in the coming years, it’s no surprise that many institutions are creating their own BSc computer science with artificial intelligence courses. In the UK alone, the likes of Queen’s University Belfast and Cardiff University are quickly catching up to The University of Edinburgh, especially in the robotics field.

In North America, the University of Toronto is making waves with a course that’s ranked the best in Canada and fifth in North America by EduRank. Interestingly, that course is a little easier to get into than many comparable North American courses, given its 43% acceptance rate.

Back in the UK, the University of Oxford is also doing well with AI, though its current courses tend to be shorter and specialized in areas like utilizing AI in business. We’re also seeing Asian universities make great progress with their courses, as both Tsinghua University and Nanyang Technological University are establishing themselves as leaders in the space.

Importance of Hands-On Experience and Internships

As important as foundational and theoretical knowledge is, it’s when you get hands-on that you start to understand how much of an impact AI will have on business and society at large. Good universities recognize this and offer hands-on experience (either via research or internship programs) that offer three core benefits:

• Gain Practical Skills – Becoming a walking encyclopedia for the theory of AI is great if you intend on becoming a teacher. But for everybody else, working with hands-on practical experiments and examples is required to develop the practical skills that employers seek.
• Networking – A strong faculty (ideally with industry as well as academic connections) will take you a long way in your BSc computer science with artificial intelligence. The more people you encounter, the more connections you build and the better your prospects are when you complete your course.
• Enhanced Job Prospects – Getting hands-on with real-world examples, and having evidence of that work, shows employers that you know how to use the knowledge you have knocking around your head. The more practical a course gets, the better it enhances your job prospects.

Scholarships and Financial Aid Opportunities

Due to BSc artificial intelligence and machine learning courses being so expensive (remember – an average of €3,000 per year), financial aid is going to be important for many students. In the UK, that aid often comes in the form of student loans, which you don’t have to start repaying until you hit a certain earnings threshold.

When we take things Europe-wide, more scholarship and financial aid programs become available. The Erasmus program offers funding for master’s students (assuming they meet the criteria) and there are several scholarship portals, such as EURAXESS and Scholarshipportal designed to help with financial aid.

If this is something you’re interested in, the following tips may help you obtain funding:

• Excel academically in pre-university studies to demonstrate your potential
• Speak to the finance teams at your university of choice to see what’s currently available
• Apply for as many scholarship and aid programs as you can to boost your chances of success

Try the Top BSc Artificial Intelligence and Machine Learning Programs

The three BSc computer science with artificial intelligence programs discussed in this article are among the best in the world for many reasons. They combine intelligence course focuses with faculty who not only know how to teach AI but have practical experience that helps you learn and can serve useful networking purposes.

The latter will prove increasingly important as the AI industry grows and becomes more competitive. But as with any form of education, your own needs are paramount. Choose the best course for your needs (whether it’s one from this list or an online BSc) and focus your efforts on becoming the best you can be.