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Cyber Security involves the practice of implementing multiple layers of security and protection against digital attacks across computers, devices, systems, and networks. Usually, organizations have a system and a framework in place for how they tackle attempted or successful cyberattacks. A good framework can help detect and identify threats, protect networks and systems, and recover in case any attack was successful.

Importance of Cybersecurity

Cybersecurity is even more significant now as most things that we enjoy today are in the form of connected devices and systems. With IoT revolutionizing the way the world operates, it has become imperative that Cybersecurity be implemented in all systems that are prone to threats and attacks to prevent extortion attempts, identity theft, loss of valuable data, misuse of sensitive information, etc.
Critical infrastructures such as hospitals, financial service companies, power plants, etc. possess sensitive data not only pertaining to their consumers but also to themselves. This calls for serious consideration for Cyber Security implementation to keep our society functioning without disruptions.

The programming requirements of data science demands a very versatile yet flexible language which is simple to write the code but can handle highly complex mathematical processing. Python is most suited for such requirements as it has already established itself both as a language for general computing as well as scientific computing. More over it is being continuously upgraded in form of new addition to its plethora of libraries aimed at different programming requirements. Below we will discuss such features of python which makes it the preferred language for data science.

A simple and easy to learn language which achieves result in fewer lines of code than other similar languages like R. Its simplicity also makes it robust to handle complex scenarios with minimal code and much less confusion on the general flow of the program.
It is cross platform, so the same code works in multiple environments without needing any change. That makes it perfect to be used in a multi-environment setup easily.
It executes faster than other similar languages used for data analysis like R and MATLAB.
Its excellent memory management capability, especially garbage collection makes it versatile in gracefully managing very large volume of data transformation, slicing, dicing and visualization.

It is believed that these heavier elements, and some isotopes of lighter elements, have been produced by successive capture of neutrons. Two processes of neutron capture may be distinguished: the r -process, rapid neutron capture; and the s -process, slow neutron capture. If neutrons are added to a stable nucleus, it is not long before the product nucleus becomes unstable and the neutron is converted into a proton. Outside a nucleus, a neutron decays into a proton and an electron by a process called beta decay (β-decay). Inside a nucleus it can be stable if the nucleus does not contain too many neutrons. In slow neutron capture, neutrons are added at a rate such that whenever an unstable nucleus is formed, it beta-decays before another neutron can be added. If neutrons can be added more rapidly, as in the r -process, the unstable nuclei formed cannot decay before additional neutrons are added until a nucleus is eventually produced that will not accept a further neutron. This nucleus, however, will eventually be subject to beta decay, thus permitting further neutron capture.