Saturday, February 4, 2023

6 Top Data Science Predictions for 2023

The discipline of data science has come into its own since the appearance of big data more than a decade ago.

Just as the universe is growing, so too does big data keep getting even bigger. In parallel, the importance of the data scientist has risen within organizations.

Here are some of the top predictions for data science in 2023:

Top Data Science Predictions

1. AI Boom Fuels Data Science Growth

Jens Graupmann, SVP of product and innovation at Exasol, expects investments in artificial intelligence (AI) to soar from $122 billion in 2022 to more than $300 billion in 2026.

What’s more, Infosys estimates that companies can generate over $460 billion in incremental profit if they are able to optimize AI and data science practices.

“Businesses should prepare for heightened scrutiny on delivering ROI from their AI investments in 2023,” Graupman said.

“Successful AI/ML deployment will depend on the relationship between data scientists and data engineers. Harmonious collaboration is critical to ensure that ML scoring models created by data scientists, for example, are properly integrated into production systems and processes by data engineers.”

See more: 5 Top Artificial Intelligence (AI) Trends

2.-Machine-Learning-Growth-To-Remain-Strong

Machine learning (ML) has been an area of heavy growth for several years as a sub-discipline within the broader AI and data science categories.

Expect this trend to continue in 2023. Salaries continue to increase and demand is unrelenting. But ML isn’t a one-size-fits-all proposition.

“There are different types of ML with classic ML often characterized by how an algorithm learns to become more accurate in its predictions,” said David Foote, chief analyst, Foote Partners.

“Machine learning has already seen many use cases, and they will increase in number. Jobs and skills in AI and ML, especially in deep learning, will maintain their hotness and support job creation and cash market values for skills for the foreseeable future.”

See more: 5 Top Machine Learning (ML) Trends

3. More MLOps In Data Science

A recent survey proved that 65% of data scientists spend time performing tasks that could have been easily done and in less time if they used machine learning tools.

Although MLOps may be a valuable practice that companies can implement, many in IT and data science remain unaware of its benefits. Some of the benefits include improving turnaround time, reducing defects, and increasing data science productivity, according to Lucas Bonatto, founder and CEO, Elemeno, a platform that helps data scientists build a scalable software infrastructure.

Integrating ML models into an organization can help it stay relevant and grow in a technology- and information-oriented world, Bonatto said.

See more: How to Tackle Machine Learning’s MLOps Tooling Mess

4. Data Science In Cloud Management

As the cloud has grown, so has complexity. Those that manage their clouds most efficiently are blessed with lower costs and more productivity.

But the profusion of multicloud environments coupled with huge volumes of cloud-based big data and a labyrinth of applications competing for compute resources and data access call for cloud management to up its game.

Those with large inventories of cloud data and applications are beginning to harness data science to learn more about their cloud environments, how to run them better, and how to contain costs.

“Companies will need to start diving a little deeper into the key value they are looking for and which cloud provider can provide it best,” said Amit Rathi, VP of engineering, Virtana.

“For some AI and ML capabilities, there may be a specific cloud that has a significant upper hand or for PaaS, there could be another cloud which delivers a significant discount based on previous usage. For organizations to drive the value needed to stay competitive, it will be critical to have the right infrastructure and tools in place to effectively manage data and operations in a multicloud environment.”

See more: 5 Top Multicloud Trends

5. The Rise Of Bioinformatics

There are many different aspects to data science. One of the most prominent use cases is bioinformatics, an interdisciplinary field that develops methods and software tools to better understand biological data, especially when the datasets are large and complex.

Bioinformatics combines a diverse range of disciplines — including biology, chemistry, physics, computer science, information engineering, mathematics, and statistics — for the analysis and interpretation of biological data, such as genomics. Image and signal processing is used, for example, to extract results from large amounts of data used in sequencing and annotating genomes and mutations. It also plays a role in text mining of biological literature and developing biological and gene ontologies to organize and query biological data, according to Foote Partners.

Foote Partners regularly reviews pay rates and notes the hottest skill and certification in IT. Data science in general and bioinformatics in particular scored high among the most desirable subjects in IT with the strongest growth in market value in the most recent report. Bioinformatics had an 18.8% rise in market value as a skill over the last six months.

“Analyzing biological data to produce meaningful information involves writing and running software programs that use algorithms from graph theory, AI, soft computing, data mining, image processing, and computer simulation,” Foote said.

6. Neural Radiance Fields

When people think of data science, maybe they think of zeros and ones, charts, numbers, or text. But one of the biggest advances made in data science and AI tech is in the realm of imagery and virtual reality (VR).

“Right now, a hot trend is neural radiance fields, which can generate a realistic 3D environment from 2D images,” said Ricardo Michel Reyes, co-founder and chief science officer, Erudit.

Nvidia, the large graphics processing unit (GPU) company, has created a metaverse from various images. This is thanks to the ability to process data fast.

Reyes said one example of a practical application of this that we should see in 2023 is realistic virtual shops. Imagine putting on a VR headset to enter into a virtual furniture store where you can visually check out the look, texture, and size of a couch, as if you were actually there.

“Building on this, my 2023 prediction is we’ll be seeing this move into touch and even sound and smell,” Reyes said.

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