Data science trends of the future 2022

Viewed in Data Science Central

As data science evolves, artificial intelligence and machine learning is beginning to influence the vast majority of industry sectors.

This is great news for knowledge workers; data science has become field blossoming with both job opportunities and promising opportunities for expansion in the years and decades to come.

In this article, Michael Kevin Spencer spots a number of emerging data science trends.

Hybrid data automation

High-value businesses start with high data quality.

With the volume and complexity of today’s data, automating data discovery, data preparation and disparate data blending is no longer an option to extract value.

Artificial Intelligence as a Service

With data science growing and machine learning evolving, artificial intelligence platforms and services democratize artificial intelligence expertise.

About 12,000 AI startups in the world are amplifying the scale in which humans adjust to this new reality. Spencer describes the field as “a holy grail for career aspirants and students alike.”

Cloud computing providers market leaders like Google, Baidu, Microsoft and Amazon make these incredible tools accessible to even the smaller companies and entrepreneurs.

Speak Python

Python Programming Language remains an entry point for getting into data science and the artificial intelligence world.

Python is not just a tool; it’s also a culture. Python is omni-present in data science programming libraries.

Analytics is business critical

Many businesses were once approaching analytics as a nice-to-have support function but they are now increasingly embracing it as mission critical.

Why? Better data and analytics mean better business decisions. Analytics is the core business function that ultimately makes data valuable, insightful and actionable in real time.

 

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