What does 2022 have in store for data science? Find out the biggest trends set to transform the way we work and live in the year ahead. Also, read how the self-professed future of decision making in soccer (or football for purists), real analytics, is impacting the game like Moneyball did for baseball a couple of decades ago. Then, how some organizations are turning the challenges of AI, ML and data science into opportunity. And finally, geoscientist, bridging the gap and learning to work and innovate with increasingly large and complex data sets.
The 5 Biggest Data Science Trends In 2022
By Bernard Marr, contributing writer for Forbes.com
The emergence of data science as a field of study and practical application over the last century has led to the development of technologies such as deep learning, natural language processing, and computer vision. Broadly speaking, it has enabled the emergence of machine learning (ML) as a way of working towards what we refer to as artificial intelligence (AI), a field of technology that’s rapidly transforming the way we work and live.
Real Analytics – the real life Moneyball revolutionising football recruitment
By Graeme Bailey, contributing writer for 90min.com
Moneyball is a term that comes from a movie and book about baseball, and basically how stats and analytics could be used to gain a competitive advantage.
What are the threats and opportunities of data science and machine learning?
By Simon Clare, contributing writer for Ftadviser.com
As with most things, many of the threats and challenges in using data science, artificial intelligence and machine learning techniques can also be presented as opportunities, if not for today, then certainly for the future.
Training the Next Generation of Physical Data Scientists
By Amy McGovern and John Allen, contributing writer for Eos.org
Artificial intelligence (AI), machine learning (ML), and data science provide flexible, scalable, and interpretable approaches to harness the growing volume of available data that can help us improve the understanding and prediction of a wide variety of geoscience phenomena, including natural hazards, climate change, and severe weather events. As such, AI/ML and data science are gaining popularity throughout the geosciences. However, geoscience education has not kept up with this trend, leaving students and researchers with knowledge gaps that hinder their ability to innovate and grow through the development of new approaches to and applications of their research. To bridge these gaps, we need to train a new generation of data scientists who are prepared to address the unique needs of geoscience data and related phenomena.
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