As 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.

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Cyber Threat Landscape 2024: Human-Centric Cyber Threats
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Apr 17, 2024 9 min read

Human-centric cyber threats have long posed a serious issue for organizations. After all, humans are often the weakest link in the cybersecurity chain. Unfortunately, when artificial intelligence came into the mix, it only made these threats even more dangerous.

So, what can be done about these cyber threats now?

That’s precisely what we asked Tom Vazdar, the chair of the Enterprise Cybersecurity Master’s program at the Open Institute of Technology (OPIT), and Venicia Solomons, aka the “Cyber Queen.”

They dedicated a significant portion of their “Cyber Threat Landscape 2024: Navigating New Risks” master class to AI-powered human-centric cyber threats. So, let’s see what these two experts have to say on the topic.

Human-Centric Cyber Threats 101

Before exploring how AI impacted human-centric cyber threats, let’s go back to the basics. What are human-centric cyber threats?

As you might conclude from the name, human-centric cyber threats are cybersecurity risks that exploit human behavior or vulnerabilities (e.g., fear). Even if you haven’t heard of the term “human-centric cyber threats,” you’ve probably heard of (or even experienced) the threats themselves.

The most common of these threats are phishing attacks, which rely on deceptive emails to trick users into revealing confidential information (or clicking on malicious links). The result? Stolen credentials, ransomware infections, and general IT chaos.

How Has AI Impacted Human-Centric Cyber Threats?

AI has infiltrated virtually every cybersecurity sector. Social engineering is no different.

As mentioned, AI has made human-centric cyber threats substantially more dangerous. How? By making them difficult to spot.

In Venicia’s words, AI has allowed “a more personalized and convincing social engineering attack.”

In terms of email phishing, malicious actors use AI to write “beautifully crafted emails,” as Tom puts it. These emails contain no grammatical errors and can mimic the sender’s writing style, making them appear more legitimate and harder to identify as fraudulent.

These highly targeted AI-powered phishing emails are no longer considered “regular” phishing attacks but spear phishing emails, which are significantly more likely to fool their targets.

Unfortunately, it doesn’t stop there.

As AI technology advances, its capabilities go far beyond crafting a simple email. Venicia warns that AI-powered voice technology can even create convincing voice messages or phone calls that sound exactly like a trusted individual, such as a colleague, supervisor, or even the CEO of the company. Obey the instructions from these phone calls, and you’ll likely put your organization in harm’s way.

How to Counter AI-Powered Human-Centric Cyber Threats

Given how advanced human-centric cyber threats have gotten, one logical question arises – how can organizations counter them? Luckily, there are several ways to do this. Some rely on technology to detect and mitigate threats. However, most of them strive to correct what caused the issue in the first place – human behavior.

Enhancing Email Security Measures

The first step in countering the most common human-centric cyber threats is a given for everyone, from individuals to organizations. You must enhance your email security measures.

Tom provides a brief overview of how you can do this.

No. 1 – you need a reliable filtering solution. For Gmail users, there’s already one such solution in place.

No. 2 – organizations should take full advantage of phishing filters. Before, only spam filters existed, so this is a major upgrade in email security.

And No. 3 – you should consider implementing DMARC (Domain-based Message Authentication, Reporting, and Conformance) to prevent email spoofing and phishing attacks.

Keeping Up With System Updates

Another “technical” move you can make to counter AI-powered human-centric cyber threats is to ensure all your systems are regularly updated. Fail to keep up with software updates and patches, and you’re looking at a strong possibility of facing zero-day attacks. Zero-day attacks are particularly dangerous because they exploit vulnerabilities that are unknown to the software vendor, making them difficult to defend against.

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Nurturing a Culture of Skepticism

The key component of the human-centric cyber threats is, in fact, humans. That’s why they should also be the key component in countering these threats.

At an organizational level, numerous steps are needed to minimize the risks of employees falling for these threats. But it all starts with what Tom refers to as a “culture of skepticism.”

Employees should constantly be suspicious of any unsolicited emails, messages, or requests for sensitive information.

They should always ask themselves – who is sending this, and why are they doing so?

This is especially important if the correspondence comes from a seemingly trusted source. As Tom puts it, “Don’t click immediately on a link that somebody sent you because you are familiar with the name.” He labels this as the “Rule No. 1” of cybersecurity awareness.

Growing the Cybersecurity Culture

The ultra-specific culture of skepticism will help create a more security-conscious workforce. But it’s far from enough to make a fundamental change in how employees perceive (and respond to) threats. For that, you need a strong cybersecurity culture.

Tom links this culture to the corporate culture. The organization’s mission, vision, statement of purpose, and values that shape the corporate culture should also be applicable to cybersecurity. Of course, this isn’t something companies can do overnight. They must grow and nurture this culture if they are to see any meaningful results.

According to Tom, it will probably take at least 18 months before these results start to show.

During this time, organizations must work on strengthening the relationships between every department, focusing on the human resources and security sectors. These two sectors should be the ones to primarily grow the cybersecurity culture within the company, as they’re well versed in the two pillars of this culture – human behavior and cybersecurity.

However, this strong interdepartmental relationship is important for another reason.

As Tom puts it, “[As humans], we cannot do anything by ourselves. But as a collective, with the help within the organization, we can.”

Staying Educated

The world of AI and cybersecurity have one thing in common – they never sleep. The only way to keep up with these ever-evolving worlds is to stay educated.

The best practice would be to gain a solid base by completing a comprehensive program, such as OPIT’s Enterprise Cybersecurity Master’s program. Then, it’s all about continuously learning about new developments, trends, and threats in AI and cybersecurity.

Conducting Regular Training

For most people, it’s not enough to just explain how human-centric cyber threats work. They must see them in action. Especially since many people believe that phishing attacks won’t happen to them or, if they do, they simply won’t fall for them. Unfortunately, neither of these are true.

Approximately 3.4 billion phishing emails are sent each day, and millions of them successfully bypass all email authentication methods. With such high figures, developing critical thinking among the employees is the No. 1 priority. After all, humans are the first line of defense against cyber threats.

But humans must be properly trained to counter these cyber threats. This training includes the organization’s security department sending fake phishing emails to employees to test their vigilance. Venicia calls employees who fall for these emails “clickers” and adds that no one wants to be a clicker. So, they do everything in their power to avoid falling for similar attacks in the future.

However, the key to successful employee training in this area also involves avoiding sending similar fake emails. If the company keeps trying to trick the employees in the same way, they’ll likely become desensitized and less likely to take real threats seriously.

So, Tom proposes including gamification in the training. This way, the training can be more engaging and interactive, encouraging employees to actively participate and learn. Interestingly, AI can be a powerful ally here, helping create realistic scenarios and personalized learning experiences based on employee responses.

Following in the Competitors’ Footsteps

When it comes to cybersecurity, it’s crucial to be proactive rather than reactive. Even if an organization hasn’t had issues with cyberattacks, it doesn’t mean it will stay this way. So, the best course of action is to monitor what competitors are doing in this field.

However, organizations shouldn’t stop with their competitors. They should also study other real-world social engineering incidents that might give them valuable insights into the tactics used by the malicious actors.

Tom advises visiting the many open-source databases reporting on these incidents and using the data to build an internal educational program. This gives organizations a chance to learn from other people’s mistakes and potentially prevent those mistakes from happening within their ecosystem.

Stay Vigilant

It’s perfectly natural for humans to feel curiosity when it comes to new information, anxiety regarding urgent-looking emails, and trust when seeing a familiar name pop up on the screen. But in the world of cybersecurity, these basic human emotions can cause a lot of trouble. That is, at least, when humans act on them.

So, organizations must work on correcting human behaviors, not suppressing basic human emotions. By doing so, they can help employees develop a more critical mindset when interacting with digital communications. The result? A cyber-aware workforce that’s well-equipped to recognize and respond to phishing attacks and other cyber threats appropriately.

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Cyber Threat Landscape 2024: The AI Revolution in Cybersecurity
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Apr 17, 2024 9 min read

There’s no doubt about it – artificial intelligence has revolutionized almost every aspect of modern life. Healthcare, finance, and manufacturing are just some of the sectors that have been virtually turned upside down by this powerful new force. Cybersecurity also ranks high on this list.

But as much as AI can benefit cybersecurity, it also presents new challenges. Or – to be more direct –new threats.

To understand just how serious these threats are, we’ve enlisted the help of two prominent figures in the cybersecurity world – Tom Vazdar and Venicia Solomons. Tom is the chair of the Master’s Degree in Enterprise Cybersecurity program at the Open Institute of Technology (OPIT). Venicia, better known as the “Cyber Queen,” runs a widely successful cybersecurity community looking to empower women to succeed in the industry.

Together, they held a master class titled “Cyber Threat Landscape 2024: Navigating New Risks.” In this article, you get the chance to hear all about the double-edged sword that is AI in cybersecurity.

How Can Organizations Benefit From Using AI in Cybersecurity?

As with any new invention, AI has primarily been developed to benefit people. In the case of AI, this mainly refers to enhancing efficiency, accuracy, and automation in tasks that would be challenging or impossible for people to perform alone.

However, as AI technology evolves, its potential for both positive and negative impacts becomes more apparent.

But just because the ugly side of AI has started to rear its head more dramatically, it doesn’t mean we should abandon the technology altogether. The key, according to Venicia, is in finding a balance. And according to Tom, this balance lies in treating AI the same way you would cybersecurity in general.

Keep reading to learn what this means.

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Implement a Governance Framework

In cybersecurity, there is a governance framework called ISO/IEC 27000, whose goal is to provide a systematic approach to managing sensitive company information, ensuring it remains secure. A similar framework has recently been created for AI— ISO/IEC 42001.

Now, the trouble lies in the fact that many organizations “don’t even have cybersecurity, not to speak artificial intelligence,” as Tom puts it. But the truth is that they need both if they want to have a chance at managing the risks and complexities associated with AI technology, thus only reaping its benefits.

Implement an Oversight Mechanism

Fearing the risks of AI in cybersecurity, many organizations chose to forbid the usage of this technology outright within their operations. But by doing so, they also miss out on the significant benefits AI can offer in enhancing cybersecurity defenses.

So, an all-out ban on AI isn’t a solution. A well-thought-out oversight mechanism is.

According to Tom, this control framework should dictate how and when an organization uses cybersecurity and AI and when these two fields are to come in contact. It should also answer the questions of how an organization governs AI and ensures transparency.

With both of these frameworks (governance and oversight), it’s not enough to simply implement new mechanisms. Employees should also be educated and regularly trained to uphold the principles outlined in these frameworks.

Control the AI (Not the Other Way Around!)

When it comes to relying on AI, one principle should be every organization’s guiding light. Control the AI; don’t let the AI control you.

Of course, this includes controlling how the company’s employees use AI when interacting with client data, business secrets, and other sensitive information.

Now, the thing is – people don’t like to be controlled.

But without control, things can go off the rails pretty quickly.

Tom gives just one example of this. In 2022, an improperly trained (and controlled) chatbot gave an Air Canada customer inaccurate information and a non-existing discount. As a result, the customer bought a full-price ticket. A lawsuit ensued, and in 2024, the court ruled in the customer’s favor, ordering Air Canada to pay compensation.

This case alone illustrates one thing perfectly – you must have your AI systems under control. Tom hypothesizes that the system was probably affordable and easy to implement, but it eventually cost Air Canada dearly in terms of financial and reputational damage.

How Can Organizations Protect Themselves Against AI-Driven Cyberthreats?

With well-thought-out measures in place, organizations can reap the full benefits of AI in cybersecurity without worrying about the threats. But this doesn’t make the threats disappear. Even worse, these threats are only going to get better at outsmarting the organization’s defenses.

So, what can the organizations do about these threats?

Here’s what Tom and Venicia suggest.

Fight Fire With Fire

So, AI is potentially attacking your organization’s security systems? If so, use AI to defend them. Implement your own AI-enhanced threat detection systems.

But beware – this isn’t a one-and-done solution. Tom emphasizes the importance of staying current with the latest cybersecurity threats. More importantly – make sure your systems are up to date with them.

Also, never rely on a single control system. According to our experts, “layered security measures” are the way to go.

Never Stop Learning (and Training)

When it comes to AI in cybersecurity, continuous learning and training are of utmost importance – learning for your employees and training for the AI models. It’s the only way to ensure all system aspects function properly and your employees know how to use each and every one of them.

This approach should also alleviate one of the biggest concerns regarding an increasing AI implementation. Namely, employees fear that they will lose their jobs due to AI. But the truth is, the AI systems need them just as much as they need those systems.

As Tom puts it, “You need to train the AI system so it can protect you.”

That’s why studying to be a cybersecurity professional is a smart career move.

However, you’ll want to find a program that understands the importance of AI in cybersecurity and equips you to handle it properly. Get a master’s degree in Enterprise Security from OPIT, and that’s exactly what you’ll get.

Join the Bigger Fight

When it comes to cybersecurity, transparency is key. If organizations fail to report cybersecurity incidents promptly and accurately, they not only jeopardize their own security but also that of other organizations and individuals. Transparency builds trust and allows for collaboration in addressing cybersecurity threats collectively.

So, our experts urge you to engage in information sharing and collaborative efforts with other organizations, industry groups, and governmental bodies to stay ahead of threats.

How Has AI Impacted Data Protection and Privacy?

Among the challenges presented by AI, one stands out the most – the potential impact on data privacy and protection. Why? Because there’s a growing fear that personal data might be used to train large AI models.

That’s why European policymakers sprang into action and introduced the Artificial Intelligence Act in March 2024.

This regulation, implemented by the European Parliament, aims to protect fundamental rights, democracy, the rule of law, and environmental sustainability from high-risk AI. The act is akin to the well-known General Data Protection Regulation (GDPR) passed in 2016 but exclusively targets the use of AI. The good news for those fearful of AI’s potential negative impact is that every requirement imposed by this act is backed up with heavy penalties.

But how can organizations ensure customers, clients, and partners that their data is fully protected?

According to our experts, the answer is simple – transparency, transparency, and some more transparency!

Any employed AI system must be designed in a way that doesn’t jeopardize anyone’s privacy and freedom. However, it’s not enough to just design the system in such a way. You must also ensure all the stakeholders understand this design and the system’s operation. This includes providing clear information about the data being collected, how it’s being used, and the measures in place to protect it.

Beyond their immediate group of stakeholders, organizations also must ensure that their data isn’t manipulated or used against people. Tom gives an example of what must be avoided at all costs. Let’s say a client applies for a loan in a financial institution. Under no circumstances should that institution use AI to track the client’s personal data and use it against them, resulting in a loan ban. This hypothetical scenario is a clear violation of privacy and trust.

And according to Tom, “privacy is more important than ever.” The same goes for internal ethical standards organizations must develop.

Keeping Up With Cybersecurity

Like most revolutions, AI has come in fast and left many people (and organizations) scrambling to keep up. However, those who recognize that AI isn’t going anywhere have taken steps to embrace it and fully benefit from it. They see AI for what it truly is – a fundamental shift in how we approach technology and cybersecurity.

Those individuals have also chosen to advance their knowledge in the field by completing highly specialized and comprehensive programs like OPIT’s Enterprise Cybersecurity Master’s program. Coincidentally, this is also the program where you get to hear more valuable insights from Tom Vazdar, as he has essentially developed this course.

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