Cyber Security vs. Data Science: Which Career is Best for You?

What is Cyber Security?

Cyber security is the discipline of protecting data, devices, and networks from unlawful use or unauthorized access while ensuring and maintaining the availability, confidentiality, and integrity of information.

Cyber security is typically broken down into five distinct fields: 

• Application security
• Cloud security
• Critical infrastructure security
• Internet of Things (IoT) security
• Network security 

Cyber security professional duties include:

• Implementing firewalls to system networks
• Determining and implementing required access authorizations
• Securing the organization’s Information Technology infrastructure
• Monitoring the network for signs of cyberattacks and other unauthorized access
• Eliminating potential threats or attempted breaches
• Identifying cyberattackers
• Help create the organization’s security policies and ensure the company’s employees are aware of them

What is Data Science?

Data science combines domain expertise, programming skills, and the knowledge of mathematics and statistics to derive actionable, meaningful insights from vast quantities of unstructured data, also known as Big Data.

Data scientists typically employ algorithms, processes, scientific methods, tools, techniques, and systems to carry out their data processing tasks, and apply the resulting insights across various domains. 

Data Scientist duties include:

• Assessing quality of data sets
• Building machine learning (ML) algorithms
• Cleansing unstructured data
• Collaborating with other teams in the organization
• Combining models through ensemble modeling
• Creating data visualizations to better present information
• Developing and maintaining all databases
• Designing data modeling processes
• Preparing reports for executives, stakeholders, and project teams
• Proposing solutions to the executive teams

How Are Cyber Security and Data Science Interrelated?

Cyber security and data science have a fundamental connection because the latter requires the defenses and protection that the former provides. Data scientists need clean, uncompromised data to get their findings and ensure that their resulting processed information is secured. So, the field of data science turns to cyber security to help protect the information no matter what shape it's in.

So, cyber security vs. data science may not be the best approach — this is also a symbiotic relationship, as we are about to see below! 

A Word About Cybersecurity Data Science

The term “cyber security data science” covers situations where data is collected from selected cyber security sources and is analyzed to provide the most timely and up-to-date data-driven patterns, providing more effective security solutions.

The concept of cyber security data science makes the computing process more intelligent and actionable, especially when compared to the more traditional approaches found in the cyber security domain.

So, while data science relies on cyber security for data integrity and protection, the field of cyber security relies on data science to gather meaningful, actionable information to help better secure systems, networks, and data.

And there’s an added wrinkle to this relationship. Advances in Artificial Intelligence and Machine Learning are redefining how cyber security works. Both AI and ML have enormous potential to derive new insights from data, adding an element of speed and accuracy that humans can’t duplicate. Additionally, AI and ML are better suited to handle the dull, repetitive data-gathering tasks, freeing up human brains to tackle more challenging and engaging problems.

What Are the Differences Between Cyber Security and Data Science?

Although we have seen how data science and cyber security share an affinity, there are significant differences between the two disciplines. Here is a breakdown of their differences, illustrated conveniently side by side in chart form.

Cyber Security vs. Data Science

The Significance and Uses of Cybersecurity

Cybercrime is rising, and the media is filled with stories about data breaches, identity theft, cyberattacks, and online fraud. Consequently, cyber security is a critical element across diverse sectors of the world, including government agencies, the military, corporations, financial institutions, and healthcare providers, to name a few. Whether within an organization or connected to the rest of the world, data-sharing networks need a robust cyber security system. This need has been one of the driving forces behind corporations' training of security professionals. Here are some of the more popular uses of cyber security:

• Data Security: Vast amounts of organizational data are stored and accessible online, making the information vulnerable to many threats. Implementing the best cyber security practices ensures that sensitive and confidential organizational data remains protected.

• Cyberattack Loss Mitigation: According to Cybercrime Magazine, global cybercrime costs will grow by 15 percent annually between 2020 and 2025, reaching $10.5 trillion by 2025, an increase from $3 trillion USD in 2015! This alarming figure, and similar data, lead to customers losing their trust and businesses losing their reputation. However, robust and effective cyber security measures coupled with strong network security are the only way to mitigate these losses. That's why organizations worldwide hire more security experts at more significant salaries.
• Digitization Support: Digitization has brought almost every customer online, which means anyone could theoretically access it. As a result, cyber security has become a greater priority for many businesses and organizations. Unfortunately, it's a tradeoff; companies have brought in digitization to make their customers' lives easier but have increased the risk of privacy loss. This situation has further boosted the need for better cyber security.

The Significance and Uses of Data Science

We live, work, and play in the Information Age; data is the main currency. However, we also live in a time when businesses are more competitive and constantly seek ways of cutting costs and mitigating risks to prosper and beat the competition.

That's why data science is so vital to today's commercial world. The informed choices are the best choices and the most excellent chances of success. Data science provides that information, putting it to use like:

• Analyzing Trends: Organizations use trend analysis to set realistic and appropriate goals. Data scientists use the data they process to recommend suitable courses of action and find practical solutions to business issues.
• Making Better Decisions: Data science better equips business and organizational leadership to make sound, data-driven business decisions. As a result, many organizations actively recruit data scientists, using the lure of excellent salaries and generous benefits.
• Identifying Opportunities: Data science helps companies identify potentially profitable opportunities by analyzing historical data about customers, products, services, and more. Data scientist contributions can help businesses explore new growth options.
• Understanding Consumer Behavior: Data science is the foundation of corporate marketing and product strategy. Data scientists study consumer-related data to gather insights about consumer behavior and use these observations to assist management in strategizing and making informed decisions.

Cyber Security vs. Data Science Career Paths and Growth

Cyber security and data science offer incredible opportunities for a career-minded individual who wants to get involved in two of the more exciting and active IT fields. Both fields have similar career paths.

Cyber security experts typically start with a bachelor’s degree in cyber security, computer science, information technology, or a related field. They should also have skills in core topics like programming, cloud computing, and networks and systems administration.

After graduation, the aspiring cyber security expert typically signs on with a company in an entry-level role. Once they have gained experience in the field, typically after a few years, it’s time to try for a senior position, which typically requires a master’s degree and certification in several different cybersecurity-related disciplines.

Cyber security experts typically follow career paths like: 

• Chief Information Security Officer
• IT Security Consultant
• Security Analyst
• Security Architect
• Penetration Tester
• Ethical Hacker 

As the earlier chart shows, data scientists require more formal education than cyber security experts. Cyber security experts don’t necessarily need a master’s or even a bachelor's degree, although those resources make matters easier. 

Most data science positions require a bachelor's degree in data science, computer science, or another related field. After several years in an entry-level position, the ambitious data scientist should pursue a master’s degree in Data Science, supplemented by a few appropriate certifications, and try for a senior data analyst position.

Data science experts typically follow career paths such as: 

• Data Engineer
• Machine Learning Engineer
• Marketing Manager
• Product Manager
• Data Leader

What Are Salary Expectations Like for Cyber Security vs. Data Science Professionals?

In the US:

1. Cybersecurity Professionals:

   • Average Salary: $100,000 - $130,000 annually.
   • Top Roles (e.g., Security Engineer, Cybersecurity Architect, CISO): $150,000 - $200,000+
   • Factors Influencing Salary: Experience, certifications (CISSP, CEH), company size, and location (higher salaries in tech hubs like San Francisco, New York).

2. Data Science Professionals:

   • Average Salary: $95,000 - $120,000 annually.
   • Top Roles (e.g., Data Scientist, Machine Learning Engineer, Data Engineer): $130,000 - $180,000+
   • Factors Influencing Salary: Experience, skills in machine learning, Python, and SQL, advanced degrees (PhD/Masters), and location.

In India:

1. Cybersecurity Professionals:

   • Average Salary: ₹6,00,000 - ₹12,00,000 annually.
   • Top Roles (e.g., Security Analyst, Network Security Engineer, CISO): ₹15,00,000 - ₹25,00,000+
   • Factors Influencing Salary: Experience, certifications (CISSP, CEH), job location (metros like Bangalore, Delhi), and organization type.

2. Data Science Professionals:

   • Average Salary: ₹8,00,000 - ₹15,00,000 annually.
   • Top Roles (e.g., Data Scientist, ML Engineer, Data Analyst): ₹18,00,000 - ₹30,00,000+
   • Factors Influencing Salary: Skills in Python, machine learning, AI, experience, and the industry (e.g., finance, healthcare, tech).

So, What’s the Next Step?

The fields of data science and cyber security share a common element for success on their respective career paths: certification. Fortunately, Simplilearn is a one-stop resource covering both areas and smooths the road to professional success.

Unlock the potential of big data and take your career to the next level with our Data Scientist course. Learn key skills like data analysis, machine learning, and statistical modeling, and gain hands-on experience with real-world projects

Similarly, Simplilearn provides a Cyber Security Expert Master’s program that provides cybersecurity professionals with an impressive range of foundational, intermediate, and advanced security skills.

Regardless of which profession you choose to pursue, Simplilearn is ready to help you make your career dreams come true. So visit Simplilearn today and start turning your IT hopes into reality.