Recently, I’ve been working on a new course offering in Enthought Academy titled Software Engineering for Scientists and Engineers course. I’ve focused on distilling the software engineering best practices that we use at Enthought with our clients, with the twist of “what parts are most useful for a scientist who writes software for R&D?” After all, we don’t want to simply replicate an academic 12-week course in software engineering; instead, we want to teach the subset of ideas that will make the most impact on scientific programming projects in the least amount of time. Unfortunately, I lost track of time and ran into my deadline for this blog. Panic time? Not this year.

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Digital transformation is reshaping industries, demanding scientific organizations to adapt and remain competitive in the rapidly changing landscape. Data lies at the heart of this transformation, providing the foundation for strategic decision-making and innovative breakthroughs. To achieve sustained success and outpace the competition, visionary leaders understand the need to not only keep pace with digital advancements but to stay ahead through continuous innovation and adaptation.

As a company that delivers Digital Transformation for Science, part of our job at Enthought is to understand the trends that will affect how our clients do their science. Below are three trends that caught our attention in 2022 that we predict will take center stage in 2023.

When thinking about enhancing R&D processes, Newton’s second law of motion provides the perfect framework.

Classical mechanics teaches us that putting a body into motion requires applying force. The resulting acceleration will be the sum of the forces applied to the body, divided by the body’s mass: a = F/m. So, if we want to accelerate R&D, what are the forces we need to consider? And, what is the mass?

In the blog post Configuring a Neural Network Output Layer we highlighted how to correctly set up an output layer for deep learning models. Here, we discuss how to make sense of what a neural network actually returns from the output layers. If you are like me, you may have been surprised when you first encountered the output of a simple classification neural net.

Labs are resetting the trajectory for drug development: reducing timelines from years to months; decreasing costs from billions to millions; and gaining an advantage by delivering drugs to market in months rather than decades.

This value combination is a compelling case for investment in digital capability and organizational transformation.

In our recent C&EN Webinar: Accelerating Consumer Products Reformulation with Machine Learning, we demonstrated how to leverage digital tools and technology to bring new products to market faster. The webinar was well attended by scientists, engineers, and business leaders across the product development spectrum eager to learn how these concepts can be applied to their work. We received many good questions during the event, and wanted to make our detailed responses available to the wider community. If you have any follow up questions, please reach out to us. Enthought is here to help your business navigate the challenges in R&D digital transformation and affect real change and business value generation.

Digital skills personas for success in digital transformation

The digital skills mix varies widely across companies, from those just starting to invest in digital transformation initiatives, to ones well into their journey. Building a community of people who think digitally and are able to innovate and quickly prototype ideas is key to delivering results.