More and more companies are employing data scientists. In fact, the number has nearly doubled in recent years, indicating the importance of this profession for the modern workplace.

Additionally, data science has become a highly lucrative career. Professionals easily make over $120,000 annually, which is why it’s one of the most popular occupations.

This article will cover all you need to know about data science. We’ll define the term, its main applications, and essential elements.

What Is Data Science?

Data science analyzes raw information to provide actionable insights. Data scientists who retrieve this data utilize cutting-edge equipment and algorithms. After the collection, they analyze and break down the findings to make them readable and understandable. This way, managers, owners, and stakeholders can make informed strategic decisions.

Data Science Meaning

Although most data science definitions are relatively straightforward, there’s a lot of confusion surrounding this topic. Some people believe the field is about developing and maintaining data storage structures, but that’s not the case. It’s about analyzing data storage solutions to solve business problems and anticipate trends.

Hence, it’s important to distinguish between data science projects and those related to other fields. You can do so by testing your projects for certain aspects.

For instance, one of the most significant differences between data engineering and data science is that data science requires programming. Data scientists typically rely on code. As such, they clean and reformat information to increase its visibility across all systems.

Furthermore, data science generally requires the use of math. Complex math operations enable professionals to process raw data and turn it into usable insights. For this reason, companies require their data scientists to have high mathematical expertise.

Finally, data science projects require interpretation. The most significant difference between data scientists and some other professionals is that they use their knowledge to visualize and interpret their findings. The most common interpretation techniques include charts and graphs.

Data Science Applications

Many questions arise when researching data science. In particular, what are the applications of data science? It can be implemented for a variety of purposes:

  • Enhancing the relevance of search results – Search engines used to take forever to provide results. The wait time is minimal nowadays. One of the biggest factors responsible for this improvement is data science.
  • Adding unique flair to your video games – All gaming areas can gain a lot from data science. High-end games based on data science can analyze your movements to anticipate and react to your decisions, making the experience more interactive.
  • Risk reduction – Several financial giants, such as Deloitte, hire data scientists to extract key information that lets them reduce business risks.
  • Driverless vehicles – Technology that powers self-driving vehicles identifies traffic jams, speed limits, and other information to make driving safer for all participants. Data science-based cars can also help you reach your destination sooner.
  • Ad targeting – Billboards and other forms of traditional marketing can be effective. But considering the number of online consumers is over 2.6 billion, organizations need to shift their promotion activities online. Data science is the answer. It lets organizations improve ad targeting by offering insights into consumer behaviors.
  • AR optimization – AR brands can take a number of approaches to refining their headsets. Data science is one of them. The algorithms involved in data science can improve AR machines, translating to a better user experience.
  • Premium recognition features – Siri might be the most famous tool developed through data science methods.

Learn Data Science

If you want to learn data science, understanding each stage of the process is an excellent starting point.

Data Collection

Data scientists typically start their day with data collection – gathering relevant information that helps them anticipate trends and solve problems. There are several methods associated with collecting data.

Data Mining

Data mining is great for anticipating outcomes. The procedure correlates different bits of information and enables you to detect discrepancies.

Web Scraping

Web scraping is the process of collecting data from web pages. There are different web scraping techniques, but most professionals utilize computer bots. This technique is faster and less prone to error than manual data discovery.

Remember that while screen scraping and web scraping are often used interchangeably, they’re not the same. The former merely copies screen pixels after recognizing them from various user interface components. The latter is a more extensive procedure that recovers the HTML code and any information stored within it.

Data Acquisition

Data acquisition is a form of data collection that garners information before storing it on your cloud-based servers or other solutions. Companies can collect information with specialized sensors and other devices. This equipment makes up their data acquisition systems.

Data Cleaning

You only need usable and original information in your system. Duplicate and redundant data can be a major obstacle, which is why you should use data cleaning. It removes contradictory information and helps you separate the wheat from the chaff.

Data Preprocessing

Data preprocessing prepares your data sets for other processes. Once it’s done, you can move on to information transformation, normalization, and analysis.

Data Transformation

Data transformation turns one version of information into another. It transforms raw data into usable information.

Data Normalization

You can’t start your data analysis without normalizing the information. Data normalization helps ensure that your information has uniform organization and appearance. It makes data sets more cohesive by removing illogical or unnecessary details.

Data Analysis

The next step in the data science lifecycle is data analysis. Effective data analysis provides more accurate data, improves customer insights and targeting, reduces operational costs, and more. Following are the main types of data analysis:

Exploratory Data Analysis

Exploratory data analysis is typically the first analysis performed in the data science lifecycle. The aim is to discover and summarize key features of the information you want to discuss.

Predictive Analysis

Predictive analysis comes in handy when you wish to forecast a trend. Your system uses historical information as a basis.

Statistical Analysis

Statistical analysis evaluates information to discover useful trends. It uses numbers to plan studies, create models, and interpret research.

Machine Learning

Machine learning plays a pivotal role in data analysis. It processes enormous chunks of data quickly with minimal human involvement. The technology can even mimic a human brain, making it incredibly accurate.

Data Visualization

Preparing and analyzing information is important, but a lot more goes into data science. More specifically, you need to visualize information using different methods. Data visualization is essential when presenting your findings to a general audience because it makes the information easily digestible.

Data Visualization Tools

Many tools can help you expedite your data visualization and create insightful dashboards.

Here are some of the best data visualization tools:

  • Zoho Analytics
  • Datawrapper
  • Tableau
  • Google Charts
  • Microsoft Excel

Data Visualization Techniques

The above tools contain a plethora of data visualization techniques:

  • Line chart
  • Histogram
  • Pie chart
  • Area plot
  • Scatter plot
  • Hexbin plots
  • Word clouds
  • Network diagrams
  • Highlight tables
  • Bullet graphs

Data Storytelling

You can’t have effective data presentation without next-level storytelling. It contextualizes your narrative and gives your audience a better understanding of the process. Data dashboards and other tools can be an excellent way to enhance your storytelling.

Data Interpretation

The success of your data science work depends on your ability to derive conclusions. That’s where data interpretation comes in. It features a variety of methods that let you review and categorize your information to solve critical problems.

Data Interpretation Tools

Rather than interpret data on your own, you can incorporate a host of data interpretation tools into your toolbox:

  • Layer – You can easily step up your data interpretation game with Layer. You can send well-designed spreadsheets to all stakeholders for improved visibility. Plus, you can integrate the app with other platforms you use to elevate productivity.
  • Power Bi – A vast majority of data scientists utilize Power BI. Its intuitive interface enables you to develop and set up customized interpretation tools, offering a tailored approach to data science.
  • Tableau – If you’re looking for another straightforward yet powerful platform, Tableau is a fantastic choice. It features robust dashboards with useful insights and synchronizes well with other applications.
  • R – Advanced users can develop exceptional data interpretation graphs with R. This programming language offers state-of-the-art interpretation tools to accelerate your projects and optimize your data architecture.

Data Interpretation Techniques

The two main data interpretation techniques are the qualitative method and the quantitative method.

The qualitative method helps you interpret qualitative information. You present your findings using text instead of figures.

By contrast, the quantitative method is a numerical data interpretation technique. It requires you to elaborate on your data with numbers.

Data Insights

The final phase of the data science process involves data insights. These give your organization a complete picture of the information you obtained and interpreted, allowing stakeholders to take action on company problems. That’s especially true with actionable insights, as they recommend solutions for increasing productivity and profits.

Climb the Data Science Career Ladder, Starting From the Basics

The first step to becoming a data scientist is understanding the essence of data science and its applications. We’ve given you the basics involved in this field – the rest is up to you. Master every stage of the data science lifecycle, and you’ll be ready for a rewarding career path.

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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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