The tech-savvy world we live in today is a double-edged sword.
On the one hand, technology has made our lives easier and more convenient by opening us up to new technologies like machine learning and data science.
On the other hand, it has also made us more vulnerable to things like hacking and cyber attacks.
That’s why cyber security and data science are two fields that are becoming more and more influential to businesses every single day.
In this exciting blog post, we’ll dive into the world of cyber security and data science and discover the limitless possibilities they hold for the future while also covering how the scope of both data science and cyber security will change over the next ten years.
Get ready to be blown away by these fields’ potential impact on our world!
So, buckle up and prepare for an eye-opening ride through the exciting world of technology.
And at the bottom, I’ll give you a tip to help you understand why the scope is changing.
Understanding Scope In Tech
The world of technology and the scope of its roles are constantly changing and evolving, and the fields of data science and cyber security are no exception.
In recent years, we have seen a shift in the overall scope of these two fields, with data science becoming more specialized and cyber security expanding to meet the growing demands of our increasingly security-hazarded world.
If you ask most CEOs what their top concern is, they will usually tell you that security is their number one priority. And trust me, you’ll never hear “optimization” as their primary focus (sorry, data scientists).
In today’s digital age, data breaches and cyber attacks are becoming more frequent and sophisticated.
Companies know that they must take steps to protect their sensitive information, intellectual property, and assets.
This is where the field of cyber security comes in, with experts working to identify and prevent cyber threats, ensuring the security of digital systems and data.
On the other hand, the scope of data science has become more focused (shrinking), with data scientists specializing in specific areas such as analytics, machine learning, and data visualization.
Data science aims to extract insights and knowledge from data, but as the field becomes more specialized, the scope of what can be achieved with a single data scientist is becoming more limited.
Cyber Security vs. Data Science: Scope Creep
Cyber security and data science are two fields that are evolving at a rapid pace.
In recent years, we have seen a shift in the scope of these two fields, with cybersecurity expanding to encompass more areas and data science becoming more specialized.
Cyber security has seen a growing scope as the threat of cyber-attacks and data breaches continues to increase.
Companies and individuals are becoming more aware of the importance of protecting their digital systems and data, and cyber security experts are rising to meet this demand.
As a result, the scope of cyber security is expanding to encompass a broader range of areas, including network security, cloud security, and mobile security.
On the other hand, the scope of data science is slowly shrinking.
In the past, data scientists were often viewed as a “swiss army knife,” capable of handling a wide range of tasks due to their mathematical prowess.
However, with the rise of autoML and cloud platforms, many of the routine tasks of data science are being automated (think about things like AWS SageMaker), leading companies to focus on hiring data scientists with a highly specific scope.
This has decreased the overall number of data scientists as the field has become less broad and specialized.
Cyber Security vs. Data Science: Job Outlook
The job outlook for cyber security and data science fields is constantly changing, and it’s crucial to stay up-to-date on the latest trends in the job market.
Recently, we have seen a significant increase in demand for cybersecurity professionals.
Many companies seek individuals with the skills and experience to protect their digital systems and data.
Cybersecurity is experiencing a job market boom, much like data science did ten years ago when Harvard called it the sexiest job of the 21st century (I linked the reference at the bottom).
With the growing threat of cyber-attacks and data breaches, companies are willing to pay top dollar for experts in the field.
As a result, many individuals are picking up certifications such as CompTIA and securing highly lucrative jobs in cyber security, even without any college degrees.
On the other hand, the scope of data science is becoming more specialized, leading to an increase in the level of education and experience required to secure a job in the field.
Data scientists are now required to deeply understand specific areas, such as machine learning or data visualization, making it more challenging to break into the field without the right skills, education, and experience.
I’ll give it to you straight:
If you’re starting in the tech industry, you might want to consider pursuing a career in cyber security.
With its growing scope and size, cyber security is an exciting and innovative field that is poised for EXPLOSIVE growth in the coming years.
However, as with any career decision, it’s important to consider your personal interests and skills before making a choice.
(Choose cyber security, I’m saying this, and I even work in ML & DS).
Will data science exist in 10 years?
The field of data science is constantly evolving, and it’s difficult to predict what the future will hold for this dynamic and growing field.
However, some experts believe that data science may undergo a transformation similar to what happened to manufacturing during the industrial revolution.
During the industrial revolution, many tasks once performed by a single person (like building a whole car) became automated, split up, and specialized.
For example, one person might focus on building the wheels of a car, while another might focus on the hood.
Ultimately, all of these pieces would come together to build a car.
Similarly, data science may become more specialized in the coming years, with individuals focusing on specific subsets of the field, such as data visualization or regression analysis.
This shift towards specialization would eliminate the role of the data scientist, as the umbrella term would no longer cover what you’re actually doing.
We would see experts in things like data sourcing, regression, classification, etc.
This form of optimization of the field would decrease the total number of people within the field; as any good data scientist knows, optimization usually leads to cheaper outcomes for the business.
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