Increased interest in the data science industry has created new opportunities for data scientists and analysts. Mobile applications and user-data collection continue to surge, necessitating more secure methods of accessing and transferring information and data. In response, the cybersecurity industries are in an ongoing battle to protect information networks and systems.
Cybersecurity requires knowledge of information and data collection and storage, protecting stored data, and securing transferable data. Cybersecurity for data scientists offers a broader approach to working with information and data. Data scientists with skills in programming languages like Python are well-positioned to pursue a career in Cybersecurity.
What is Cybersecurity?
As the reliance on technologies like smart devices and public internet networks has increased, outside forces have more ways to intercept our information and data. Cybersecurity focuses on protecting computer networks, systems, and data transfers. Cybersecurity professionals must understand the intricacies of networks and systems as well as be able to identify and stop potential threats to those systems.
Cybersecurity professionals incorporate both offensive and defensive cybersecurity strategies to construct plans for individuals and institutions to access information, computers, and technology more safely and securely. These plans and strategies include implementing firewalls, protecting from cyberattacks, and developing protocols around accessing data and technology and evaluating systems. The strategies used by cybersecurity professionals also range in approach, as cybersecurity threats can be as simple as keeping users from accessing unsafe sites to protecting government officials from threats to national security.
Nearly all industries and institutions rely on computer systems and networks to save, share, and transfer information and data. Consequently, there is a growing demand for cybersecurity professionals. Until recently, cybersecurity was most commonly associated with administrative professions and information technology. Many full-time or consulting cybersecurity positions were based in office environments. However, the growth of the data science industry has prompted more significant investments in cybersecurity roles that require data science training for more applied knowledge and skills.
Why Data Scientists Should Learn Cybersecurity
Today, cybersecurity is of high importance in the data science industry as companies and individuals aggregate and manage large troves of user and consumer information and big data.
The prevalence of data collection creates greater opportunities for data breaches in which hackers, and other bad actors, break into sites and systems and steal private or sensitive user information. Hacking is of particularly high concern in banking and financial institutions that manage consumer assets as well as data. In addition, governmental data breaches are not only a threat to network security but to national security as well.
Data scientists who can translate their knowledge of programming and databases into cybersecurity concerns will find multiple opportunities in the industry to better protect sensitive information and data. In contrast to traditional forms of database management, big database management requires data governance structures that not only ensure data is accessible but that data is protected within a database management system. Learning cybersecurity with Python is one of many ways that data scientists can apply their skills in programming, automation, and database management across fields and industries.
Programming with Python
Python is a versatile and beginner-friendly programming language. Learning Python for data science is essential for industry professionals interested in cybersecurity. Python is renowned for its data science libraries, and there are several Python libraries and packages for cybersecurity. For example, the Beautiful Soup library includes features to run tests on the vulnerability of a system or network.
Automation and Machine Learning
Python is also well-regarded for its capabilities in automation and machine learning. Data scientists can build their cybersecurity skills by specializing in Python for automation and machine learning. Automated machine learning–or AutoML–uses Python programming to automate specific data science tasks, such as cleaning and organizing data, with machine learning models. Specifically, data scientists can train machine learning models to recognize threats to a system or network, such as fraud detection algorithms that can flag unusual behavior patterns in a system.
Database Management and Administration
Database management and administration are valuable skills for data science professionals interested in using Python for cybersecurity. Many cybersecurity professionals specialize in network security. This type of security work focuses on the back-end development of computer systems and assigning roles to administrators and users within a system. Python is also compatible with relational database management systems and has many ways to pair with SQL for analyzing data.
Interested in Using Python for Cybersecurity?
The growth of the data science industry has hastened the growth of closely aligned industries, such as database administration and cybersecurity. Therefore, many of Noble Desktop's data science classes cover the foundational skills needed for pursuing a career in Cybersecurity. Several Python classes and bootcamps offer cybersecurity training for beginners and more advanced students. The Cybersecurity Bootcamp is for beginner data scientists looking to start a career in Cybersecurity. In contrast, the Cybersecurity with Python class teaches more advanced students to automate their tasks as cybersecurity specialists and network administrators.