How Artificial Intelligence Could Change the Medical Field

How Artificial Intelligence Could Change the Medical Field

Recruiting within Data Science and having a partner who is a Surgeon got me thinking about the advances in medicine, and after a lengthy discussion I thought I'd share some of my learnings.

There is no doubt that advancement in science and technology has affected every aspect of human endeavor. Processes and procedures are being replaced with software, apps and the likes at different levels and medical practice is not left out.

It is believed that we are in a sort of revolution that is characterised by new technologies that are fusing the physical, digital and biological worlds, these have, in turn, impacted all disciplines, economies, and industries. And it is most certain that one of the major catalysts for the revolution in the medical practice is going to be artificial intelligence.

Years back, who would have imagined the use of artificial intelligence in the medical field? But today, it’s a different ball game as artificial intelligence (AI) algorithms are showing promises in performing medical procedures.

For example, Artificial Intelligence (AI) have been able to diagnose the presence or absence of tuberculosis (TB) in chest x-ray images with amazing accuracy. This feat was possible because researchers first “trained� the AIs with hundreds of x-ray ...

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The DNA of a Data Scientist

The DNA of a Data Scientist

The role has been coined  ‘the sexiest job of the 21st century’ by the Harvard Business Review and there is good reason for it.

Data Science can be a highly rewarding career path. However, not everyone is cut out for the job. Being a great data scientist takes a certain set of skills.

We’ve compiled a list of all the things that make up the best in the business.

A high level of education

Not strictly speaking mandatory. But those working under the title without, at least, a master’s degree are a very small minority (less than 20%). In fact, almost half of all data scientists have gone as far as completing a PHD.

The ideal education would be in the realm of mathematics, statistics, or computer science.

Is able to understand coding

There are a variety of different types of coding prevalent in the industry. A data scientist needs to be able to understand at least some of them.

Python, C/C++, Java, Pearl. All of those regularly crop up in data science. With Python being the most prevalent.

Has a certain level of proficiency in statistics

If you think back to your stats classes, the words ‘statistics’ and ‘data’ go hand in hand. And it’s true that statistical knowledge is important for data ...

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Future-Proof: Data-Driven Marketing Predictions for 2017

Future-Proof: Data-Driven Marketing Predictions for 2017

We recently spoke with Daniel Cantorna, Head of Data Products at ICLP, to give us his insights with regards to the future of big data in 2017 and his specialty which is data driven marketing. Daniel has worked on marketing automation programmes and has architected solutions to collect, store and understand customer data, as well as having created several data products that allow marketers to drive better decisions. Here are some of his thoughts:

The Difference Between Data-Driven Marketing or Data-Informed Marketing. There is a big difference between data-driven and data-informed marketing, but the two approaches can strengthen each other.

Essentially, data-driven marketing is the direct use of insights or outcomes from data analysis to inform a marketing decision which then impacts on a customer experience in some way. For example, offering special discounts to customers who click on your ad, visit your web page, or sign up for your mail list.

On the other hand, data-informed marketing describes a process whereby the outcome of some data analysis may inform or direct a future decision e.g. we have noticed that there is a dip in sales when products are advertised using these (x, y, z) words, so let’s try using a different product ...

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