There are two myths about how data scientists solve problems: one is that the problem naturally exists, hence the challenge for a data scientist is to use an algorithm and put it into production. Another myth considers data scientists always try leveraging the most advanced algorithms, the fancier model equals a better solution.
Now is the time to treat cyber security strategically – not tactically – and to embed it into your culture – not just your technology or your operations. The post-COVID winners will be those who seize the moment.
Takeaway: AI and ML are powerful forces in disrupting cybercrime, protecting enterprises, and safeguarding against data breaches. Cybercrime is constantly finding new ways to wreak havoc, steal your private information, and commit all kinds of mischief. New technologies such as artificial intelligence (AI) and machine learning (ML) have already been leveraged by hackers and cyber criminals for their malicious intents.
What do you exactly do to transform a data problem through to completion and generate data-driven insights? And most importantly of all, where do you start?
There are actually an abundance of useful and important applications of graph theory! In this article, I will try to explain briefly what some of these applications are.
Mobile app security has become paramount in any development process and custom software developers are constantly on the lookout for the latest solutions that minimize risk or even eliminate risk completely. App security is not a benefit or a feature, but a bare necessity.
“Good security governance” 🔒 is not a piece of useless consultant jargon. It is an essential protective layer for any organisation. It ensures a visible endorsement of security values from the top down, brings clarity around security roles, responsibilities and accountabilities across the whole organisation.
Do you want to break into data science? But just don’t know how? If so, then read on as this article is for you.
When you have more data that’s way larger than your local RAM ,say 100GB, you can either still use Pandas to handle data with some tricks to certain extent or choose a better tool — in this case, Dask.
Pandas is one of the most useful tools a data scientist can use. It provides several handy functionalities to extract information. Unfortunately, using Pandas requires data to be loaded into DataFrames, which aren’t great for handling massive quantities of data — quantities of data common at a company where you would need such data manipulation
By building a data-driven culture, the organization will find its team members more willing to make recommendations. Creativity is the lifeblood of an enterprise!
Probabilistic programming is a tool for statistical modeling. The idea is to borrow lessons from the world of programming languages and apply them to the problems of designing and using statistical models.
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