When data scientists are tasked with building a business solution, the goal is to efficiently develop a solution that’s accurate, reliable, and scalable. But the data science process involves so many steps that they often don’t get to spend enough time on the modeling or algorithmic development portion of the work needed to develop the optimal solution.

At 84.51°, we’ve developed tools and resources that help data scientists focus on the science quicker. We’ve created standard, reusable components that data scientists can use to simplify the end-to-end process and make it easier to go from ideation to production.

These components, which we often refer to as being like Lego bricks, embed best practices and enable data scientists to efficiently solve for chunks of their solution. They make it easier to create a better data science solution while using leading-edge techniques, for solutions that are better, faster and standardized so they’re easier to build and share.

The packages we have created help streamline the data science process from the beginning. The first step of the process is data wrangling, which involves transforming raw data into a more useful form for the solution they are building.

Once they have an idea of the model or solution, they’re going to develop to solve a business problem, data scientists spend a lot of time trying to find out where the data is, what the quality of the data is, how to get access to it, and who the experts on that data are so they can learn about any variables.

Through our standard, reusable components, we’re removing those barriers and making it easier to get started. And we’re doing it through process and code line.

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