This article will show you how to build a simple machine learning powered data science web app in Python using the streamlit library in less than 50 lines of code.
The threats against organisations are growing in volume and success, but can AI in cyber security stop the rot and turn failure into success? There is a list of growing cyber security threats, ranging from a rise in identity thefts and account takeovers to vindictive ransomware strains. Businesses are feeling the strain, especially Fortune 500
Augmented data preparation tools provide critical tools for business users to discover opportunities and navigate complex data and data sources with ease and speed.
While chasing perfection is perfectly fine, it’s up to you to decide and prioritize what’s the most important task to do, depending on your situation and context.
Distributed denial-of-service (DDoS) is one of the oldest and the most dynamically advancing vectors of cybercrime. This technique has become an element of unethical business competition, where ill-disposed entrepreneurs resort to DDoS-on-demand services to disrupt their rivals’ activities.
You can learn Java, and it will make you a better programmer. Although it may seem daunting, just remember that many programmers before you have successfully learned Java, so it absolutely can be done.
Data is everywhere and making the right decisions becomes increasingly difficult due to an information overload of our system. Statistics allow to better process and understand data if applied correctly. But what if statistics are misleading?
For a decision to be data-driven, it has to be the data — as opposed to something else entirely — that drive it. Seems so straightforward, and yet it’s so rare in practice because decision-makers lack a key psychological habit.
Cyber insurance is becoming a strong measure for CFOs and CEOs to consider in their arsenal of protective measures against cyber threats, as long as they remain otherwise committed to adherence to cyber security good practices.
A primary role of data scientists is not necessarily to prepare the data infrastructure and put it in place, but knowing at least getting the gist of data architecture will benefit well to understand the daily works.
Applications can be quickly produced that will run on the mainframe or in a hybrid cloud environment – allowing the mainframe to be simply part of that environment and not a special part that requires everything to be done differently on it. It all makes it very easy to move mission-critical applications to the cloud.