How Data Science Is Used Throughout The Automotive Lifecycle

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How Data Science Is Used Throughout The Automotive Lifecycle A data-driven strategy is necessary for creating better, safer vehicles. With connected and autonomous vehicles, data science unlocks better mobility solutions for all. The Ford Model T was introduced in 1908 and quickly became popular due to its low cost, durability, versatility, and ease of maintenance. It is credited with "putting the world on wheels," increasing global mobility through manufacturing efficiencies at a cost the average consumer could afford. Today, the automotive industry is still on the cutting edge of technology, changing the way people get from point A to point B. Michael Crabtree, Lead Data Scientist at Ford Motor Company and instructor of our course Credit Risk Modeling in Python, stated in a recent webinar that the key difference is that its innovation is now driven by data science rather than manufacturing. Join the popular data analytics course in Mumbai, to gain profound knowledge on big data tools.

In the automotive industry, smart cities necessitate data science. Data science is scaling mobility for low-income communities in the same way that the manufacturing scalability of the Model T did over 100 years ago. It facilitates this change for everyone, regardless of class, gender, or ability, by making transportation easily accessible without the high cost of ownership. Optimization algorithms, for example, can provide businesses with energy-efficient vehicles to service rural communities for services ranging from Amazon deliveries to plumbing and food delivery. Data scientists also collaborate with reliability engineers to develop vehicles that help differently-abled communities. These are just a few examples, but Michael claims that there are almost limitless applications for data science, with many more yet to be discovered.

Working with data Because of the maturity and breadth of the automotive industry, there are numerous opportunities for companies to rebuild around data. One application interacts with data from various data systems and data types. Many data scientists are used to working with tabular data, which is data in a table format similar to Excel. However, automotive data scientists have access to a much broader range of data. In the automotive industry, for example, raw instrumentation data is commonly stored as a stream of hexadecimal digits. They may also come across data from intelligence systems, such as images and sensor point clouds. An automotive data scientist may be needed to understand why an autonomous vehicle behaves in a certain way and how this varies across vehicle models. Another opportunity is volume: Michael's largest database at Ford has 80 billion rows and queries in less than 10 seconds! Some of the automotive industry's real-time and


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