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Here’s a great overview of deep learning, an artificial intelligence function that imitates the working of the human brain in processing data and creating patterns for use in decision making.

Deep learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural network (ANN). It has networks capable of learning unsupervised or unstructured data. Deep learning is often known as deep neural learning or deep neural network.

Data scientists with the right combination of skills are in high demand.

But what are hiring teams on the lookout for?

As with many roles, both technical expertise and soft skills are important. As data scientist Vin Vashishta writes, data science without soft skills has “limited value to the business”.

An increasingly diverse skillset is proving essential to data science and its future. “Very non-typical skillsets” are getting more attention, says head of data science at Aon’s Centre for Innovation and Analytics (ACIA) Jennifer Cruise.

“These skills are drawn from across mathematics, statistics and computer science, but you don’t have to be a mathematician or a computer programmer to pursue careers in data science, artificial intelligence or any of the other STEM areas.”

Advancing Analytics explainshow to parameterize Spark in Synapse Analytics, meaning you can plug notebooks to our orchestration pipelines and dynamically pass parameters to change how it works each time.

But how does it actually work?

Simon’s digging in to give us a quick peek at the new functionality.

For more details on the new parameters, take a peek here: https://docs.microsoft.com/en-us/azure/synapse-analytics/spark/apache-spark-development-using-notebooks#orchestrate-notebook