Data Analytics VS Cyber Security: Head-to-Head Comparison

In the last post, we discussed how data analytics is different from statistics, and in this post, we will discuss the core differences between data analytics and cyber security.

Cybersecurity and data analytics are both highly dynamic and competitive fields. Therefore, to succeed in these career paths, aspirants must have an in-depth understanding of these domains.

In recent years, big data has played an important role in cyber security by analyzing huge data to uncover vulnerabilities. Professionals in data science are looking into ways to improve cyber security measures.

There are, however, significant differences between the two career paths.

In this blog, we will explore the differences between the two fields: Data Analytics VS Cyber Security.

Data Analytics VS Cyber Security: Core Differences

Basis Data Science Cyber Security
Education Level Data science requires a significantly higher level of education in contrast to cyber security. To have a successful career in the field of data science, an aspirant must have both a bachelor’s and a master’s degree. A degree in information security, computer engineering, or computer science, combined with other relevant certifications such as CEH, CISA, and CISSP, is required to work in the field of data science.
Skills Data scientists must be proficient in programming languages such as Python and SQL. They should also be familiar with AI and ML approaches and have an affinity and understanding of statistics and mathematics. Creative problem-solving, incident response, intrusion detection, and a strong willingness to learn and upskill are just a few of the skills needed to succeed in cyber security.
Job Roles Data analyst, machine learning engineer, data scientist, data architect, and data engineer are some of the known job descriptions in data science. Cyber security analyst, network engineer, cryptographer, systems engineer, and other positions are some of the job descriptions available in the field of data science.
Expertise Data science experts gather important information from raw data in order to construct models and derive actionable insights. Cyber security specialists create strong security solutions to secure company data, networks, and systems, among other things.
Role Data science specialists are responsible for data analysis, model development, predictive analytics, machine learning, and other similar tasks. Cyber security experts are responsible for defending data and networks, auditing policies, monitoring security infrastructure, and other relevant tasks.

Let’s have a look at the professional differences between the two great fields to get a sense of how your career will differ if you choose either of these two:

Data Analytics VS Cyber Security: Career Comparison

  1. Role

  2. If you’re just starting out as a data analyst or data scientist, your primary responsibility will be to collect and evaluate data from numerous sources. Everything falls under your purview, from loading data to offering important insights to business analysts or stakeholders.

    However, as a cyber security professional, your responsibility will be to protect your company’s information and keep it secure from any form of cyberattack, no matter how complex. You are authorized to use various tools to monitor your network and implement any necessary procedures to preserve the integrity of your organization’s data.

  3. Educational qualifications

  4. A high level of education is required to obtain a career in data analytics or data science in a reputable company. Of course, you can get a job after finishing your bachelor’s degree. To grow in your industry, you will need to obtain a master’s degree, and learn from the best data analytics podcasts and books.

    On the other hand, becoming a cyber security specialist does not necessitate a high level of education. If you have a bachelor’s degree in a similar profession, such as IT or security, you will be all set to begin your job and handle the work.

  5. Career prospects for the future

  6. The world is using the internet now more than ever before. As more people use the internet, the amount of client data available to businesses is likely to increase tremendously. This will, in turn, create a greater demand for specialists in the field of data science to leverage all that data.

    The US Bureau of Labor Statistics predicts a 28 percent increase in the number of jobs in the data science domain through 2026. To put that 28 percent into perspective, that equates to over 11.5 million new employment in the industry.

    Hence, it’s quite evident from the statistics that data analytics has a lot of room to grow and thrive.

    On the other hand, cyber security is something that can never fall behind. The demand and need for cyber security are increasing as technology progresses and hackers increasingly become more meticulous. This growth, however, is not unique to the current period.

    Cyber security will always remain a basic yet major requirement as long as computers exist.

Further, if you want to understand the importance of data analytics and cyber security, keep reading.

What Is Data Analytics or Data Science?

Data analytics, often known as data science, is a collection of tools, algorithms, and machine learning techniques that work together to uncover a variety of hidden patterns and insights in unprocessed raw data. Data science is carried out in five steps: planning, analyzing, explaining, deploying, and monitoring a model or platform.

Machine learning, prescriptive analytics, and predictive causal analytics are all used in data science to make forecasts and decisions. Data analytics is critical in business. It has aided in the transformation of business in a variety of ways.

Data analytics is carried out in stages, beginning with the planning process; the data scientist or manager selects a specific project and defines all potential outcomes. The data scientist will then construct a data model with the help of several in-database technologies or open-source libraries.

To access the relevant data, as a data scientist, you must have the right tools. Accuracy is paramount, and the data scientist should test the model before launching or deploying it. Evaluating provides the data scientist the opportunity to enhance or replace the model with a better option.

The data scientist will deploy the system after explaining and presenting it. It is challenging to deploy the system since you must take a proper machine learning model and make it function efficiently. The final step is to monitor the model to check that it is functioning properly.

It is your responsibility as a data scientist to explain what is going on by analyzing data history. A data analyst must examine data from several perspectives, including predictive causal analytics. Predictive causal analytics can be utilized to predict various outcomes of a specific event in the future.

Many businesses employ data science to transform their data into a strategic advantage in order to enhance and optimize their products or services.

By monitoring weather patterns, traffic conditions, and other variables, data science has helped business organizations improve their efficiency. Data science also contributes to increased sales by making recommendations to clients based on previous purchases.

Importance of Data Science

Almost everyone is aware that data science has revolutionized the way organizations operate today. Let us now assess the domain’s significance:

  1. Making better decisions

    Data science has enabled organizational leaders to make strategic business decisions that are data-driven. Many companies recruit data scientists by offering higher salaries and additional incentives. This has aided companies in exploring data science and applying it for business purposes.

  2. Accurate trend investigation

    Trend investigation or analysis is one of the major approaches used by businesses to develop appropriate and attainable goals. For important solutions to business problems, data scientists analyze organizational data to determine and recommend the best course of action.

  3. Identifying opportunities

    Data science enables businesses to discover profitable opportunities by examining historical data about customers, products, and services. The expertise of data scientists assists firms in exploring new growth opportunities.

  4. Analyze consumer behavior

    Data science is the foundation of product strategy and organizational marketing. Data science experts analyze consumer-related data to gain insights into consumer behavior. These insights help management strategize and make appropriate decisions.

What Is Cyber Security?

As the term suggests, cyber security is largely concerned with the security of data sources and organizational networks. By security, it means denying access to information to anyone who is not allowed, even if they are a member of the organization.

Cyber security has existed since the dawn of computing because there is no use in uploading sensitive material to networks or computers if one cannot safeguard it from unauthorized personnel.

Many organizations are always on the search for bright and innovative minds capable of achieving a goal without being explicitly taught how to do so. These cyber security specialists are proficient at determining how unauthorized users would attempt to access personal data and then taking actions to prevent the information from being compromised.

In the event of a cyber-attack or other threat, cyber security is also responsible for determining how to recover lost or compromised data.

To be successful in cyber security, you must have a combination of technical and non-technical skills. To begin, you must have a thorough understanding of the technological devices with which you will be working. You can use this talent to access databases, handle files and records, manage code, and use spreadsheets.

But how different is cyber security from data science?

Let’s find out!

Importance of Cyber Security

Cyber security is important and used in many different sectors of the economy, including the military, government agencies, banking, corporate, medicine, and so forth.

A robust cyber security system is required for the entire network of data sharing. This has also been one of the motivating factors for organizations to invest in staff training and development. Let us look at some of the perks of cyber security:

  1. Data protection

  2. Because so much business data is accessible and stored online, it is vulnerable to a variety of risks. Implementing excellent cyber security measures protects all types of sensitive or classified company data.

  3. Mitigation of cyberattack losses

  4. Every day, 30,000 websites are hacked, according to statistics. In fact, a corporation is hit by a cyberattack every 39 seconds, and more than 60% of enterprises worldwide have been hit by at least one type of cyberattack.

    This would result in client mistrust and a loss of brand reputation. Cyber security measures and solid network security are the only ways to prevent cyberattack losses. As a result, organizations all around the world are recruiting cyber security experts and professionals at a higher pay scale.

  5. Digitization assistance

  6. Because practically every customer’s data is now available online as a result of digitization, cyber security has become critical for many firms. Organizations have used digitalization to provide their customers with greater convenience. This has increased the demand for IT security.

    Therefore, to assist the increasing digitization, companies offer cyber security training programs and development.

    Now that we understand the individual importance of data analytics and cyber security, let’s uncover their difference when it comes to choosing a profession in these fields.

Frequently Asked Questions

Which is more pertinent, data analytics or cyber security?

Cybersecurity is undoubtedly the most important form of security measure. As people grow more technologically in the future decades, almost every facet of technological advancement will require protection.

Furthermore, cybersecurity provides a more secure professional path.

Which is more lucrative, cybersecurity or data science?

In the United States, the average annual remuneration for data scientists is $115,330. Entry-level data scientists earn roughly $81,000 per year, while senior-level data scientists earn around $164,000 per year.

On the other hand, the average compensation for cyber security experts is reported to be $76,808 per year. Salary varies from $37,000 for entry-level jobs to $160,000 for senior-level job positions.

Is data security the same as cyber security?

Data security is concerned with just data itself. Cyber security, on the other hand, comprises all forms of digital security, including digital systems and digital data.

Data Analytics or Cyber Securtiy: What Would You Choose?

There are many statistics and data that talk about countless job prospects in the cybersecurity and data analytics fields.

The final decision, however, is to be made by you!

Despite the fact that both fields are data-centric, they differ in terms of skill sets, expertise, and objectives.

Therefore, we suggest that you select the field based on your specific interests and skill set.

So thoroughly study and analyze the differences and choose what best meets your needs before making your final decision.

Gaurang Bhatt

Written by

Gaurang Bhatt

Gaurang has 15+ years of experience solving complex business problems and enabling businesses with data-driven decisions using data analysis and predictive modeling tools like Tableau, Power BI, Looker, and Google Data Studio. His expertise lies in data visualization, reporting, and creating ETL pipelines. In addition, he is passionate about exploring different technologies like machine learning and AI. He shares his knowledge and learnings on the LabsMedia platform.