Data science is all the rage these days. It plays a pivotal role in many organizations, as it makes raw data easily understandable for managers and owners. In turn, it provides stakeholders with better decision-making opportunities.

Considering the enormous importance of data science, it’s no surprise the industry has grown to a whopping $65 billion. It’s also no wonder why there are 150K+ data scientists in the U.S., either, with more people expected to flock to this realm. So, why not become one of them and set yourself up to earn more than $120,000 per year?

All it takes is to invest in high-quality education, and this article will point you in the right direction. Here’s an overview of the five best data science courses to help propel your career.

Factors to Consider When Choosing a Data Science Course

We’ll take a closer look at the best data science courses in 2023 shortly, but let’s put that on hold for a few moments. After all, you don’t want to end up enrolling in a module that doesn’t suit your needs and budget, do you?

Our data science course buyer’s guide has come to the rescue. Check out the factors you should consider when selecting your module.

Course Content and Curriculum

Becoming a data scientist is a lucrative but broad career path. Did you know that this field branches out into multiple sub-fields? These include data engineering, machine learning, and data analysis. There’s no one-size-fits-all solution when it comes to data science courses, which is why you should make sure the curriculum ties in with your goals.

For example, if you want to spearhead the next generation of machine learning developments, look for a course that focuses on machine learning. In other words, module content should be in line with your needs.

Course Duration and Flexibility

Course duration is another important consideration. If you only want to scratch the surface of data science, a so-called boot camp might be a good choice. It typically lasts two or three months and gives you a basic understanding of this topic.

But if you wish to become a data science mastermind, a BSc or MSc in data science is the right option. It takes at least four years, but it teaches you all you need to know about this area, including theoretical knowledge and practical skills.

Instructor’s Expertise and Experience

Experienced instructors should also be a priority. Just like Elon Musk leads the way in Tesla with his extensive programming expertise, your teachers should be your focal point with their data science knowledge. Check their credentials before hitting the “Enroll” button.

Course Fees and Return on Investment

While you can get a lot of value out of a free data science course, paid alternatives are the real deal. Still, be sure you can afford the module before starting your first lesson. Reliable providers should offer transparent pricing with no hidden fees.

Course Reviews and Ratings

One of the best ways to determine if a course is compatible with you is word of mouth. So, put your search engine to work and see what others are saying about different modules. You’ll be able to learn more about the instructors’ approach, pricing, and content.

Best Data Science Courses Available

Now that you have a sense of direction when looking for a data science course, let’s get to the brass tacks of this article. Completing one of the following modules can be your leg up, giving you an edge over other candidates during your job search.

1. Data Science Specialization by Coursera

Coursera is the repository of many courses, including those related to data science. Their Data Science Specialization course can be an excellent choice if you have some understanding of this field but want to expand your horizons.

If you sign up for the module, you’ll gain access to an array of valuable lessons. The list includes cleaning and analyzing data with R, managing different projects with GitHub, and applying data regression models.

Furthermore, the instructors come from established institutions, and you get a shareable certificate after completing the course. Keep in mind that some prior Python knowledge is recommended to take the module.

Pros:

  • Beginner-friendly
  • Reliable instructors
  • Shareable certificate

Cons:

  • Requires Python knowledge

Price: Free enrollment from May 30; $49 per month otherwise

Duration: Approx. 11 months

2. The Data Science Course: Complete Data Science Bootcamp by Udemy

Although this is technically a boot camp, it’s one of the most comprehensive data science courses online. It lifts the veil of mystery surrounding data science and offers detailed explanations of the key concepts in this area.

For instance, if you wish to apply deep learning principles in your work, you can learn how to do so with this course. Other useful skills you can pick up here include Python-based machine learning, data pre-processing, logistic and linear regression, and statistical analyses.

The biggest downside is that lesson quality is inconsistent. Unlike Coursera, Udemy doesn’t attract renowned data science professionals. Basically, anyone can teach on the platform, even if they don’t have credentials. The good news is that you get a certificate of completion for passing the course.

Pros:

  • Fairly detailed
  • Wide range of skills
  • Certificate of completion

Cons:

  • Inconsistent teaching quality

Price: $74.99

Duration: 31 hours of video materials

3. Python for Data Science and Machine Learning Bootcamp by Udemy

Udemy makes another appearance on our rundown with their Python for Data Science and Machine Learning course. As you’ve probably guessed, it’s geared toward budding data scientists who want to climb the career ladder with Python.

And admittedly, the course does a good job of teaching the basics of this programming language. It tackles a variety of topics, such as machine learning, Pandas, Seaborn, Sci-Kit, decision tree algorithms, and natural language processing. It comes with a certificate of completion and is relatively short, allowing you to grasp the fundamentals of Python in just a few weeks.

Again, the only drawback might be lesson quality. You may receive instructions from first-class teachers, but you may also have subpar instructors.

Pros:

  • Good representation of Python basics
  • Natural language processing module
  • Short and simple

Cons:

  • Inconsistent instructions

Price: $74.99

Duration: 25 hours of video materials

4. Master of Applied Data Science by University of Michigan

For some aspiring data scientists, courses provided by renowned universities are the only ones in play. If you have the same affinity, consider this Master of Applied Data Science at the University of Michigan.

What stands out about this course is that it’s fully online, despite coming from a top-rated school. Therefore, you don’t have to attend classes in person to make headway.

When it comes to the curriculum, it covers most (if not all) subjects you need to apply data science in real life. It delves deep into machine learning, natural language processing, data preparation, and network analysis. Plus, you get a hands-on experience with real data from several companies around the globe. Completing the module earns you an accredited diploma.

As for the instructors, you shouldn’t have issues with inconsistent lectures. Michigan professors are well-versed in data science and know how to transfer knowledge effectively.

Still, many people are put off the program due to the price. It also requires some previous knowledge of statistics and Python.

Pros:

  • Renowned institution
  • Fully online
  • Covers everything data science-related
  • Great instructors

Cons:

  • Pricey
  • Previous knowledge required

Price: $34,000-$46,000

Duration: 12-36 weeks

5. Online Master of Computer Science by Arizona State University

The University of Michigan can be an excellent choice, but it doesn’t blow other schools out of the water. Arizona State is a solid option, too, with its Online Master of Computer Science.

Practical teaching is the highlight of this course. The curriculum focuses on applied projects throughout its duration, enabling you to gain a better understanding of data science and related fields. Some of the skills you can acquire and polish here include machine learning, software security, and computer forensics.

On top of that, the course puts a heavy emphasis on blockchain-related data science. Hence, if you want to test the waters with this ever-growing industry, Arizona State has you covered.

Instructions are also high-quality. Even though it’s an online course, the professors devote the same attention to you as to your fellow students on campus.

As for the drawbacks, the course isn’t affordable for many people. You also need to meet strict admission and GPA criteria.

Pros:

  • In-depth course
  • Blockchain analysis
  • Top-rated professors

Cons:

  • On the expensive side
  • Stringent enrollment criteria

Price: $15,000

Duration: 18-36 weeks

Tips for Succeeding in a Data Science Course

Just because you choose an exceptional data science course doesn’t mean you’ll breeze through the curriculum. The following tips will help make your experience smoother.

  • Set clear goals and expectations — Determine whether you want a basic or advanced understanding of data science.
  • Dedicated time for learning and practice — Allocate as much time as necessary to learn and practice key skills.
  • Engage in online forums and communities — Visit forums and other online communities to find heaps of resources and course materials.
  • Work on real-world projects — Practice applying data science by manipulating real-life data.
  • Continuously update your skills — Always look for new learning opportunities to get a full picture of your curriculum.

A Remunerative Career Is Waiting

If you’re looking to master critical skills, the best data science course for you might be Master of Applied Data Science by the University of Michigan. It’s expensive, but it’s jam-packed with real-world knowledge. If you need something simpler that still offers some value, the courses by Coursera and Udemy may be a good fit.

So, make your pick carefully. By enrolling in a course that aligns with your needs, you’ll get a better learning experience and higher retention. And nothing paves the way for a lucrative career in data science like top-grade education.

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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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