To create better, safer vehicles, a data-driven methodology is required. With linked and driverless vehicles, data science enables better transportation solutions for all.

The Model T, produced by Ford Motor Company since 1908, has endured thanks to its affordability, toughness, adaptability, and simplicity of maintenance. It is credited with "setting the globe on wheels," enabling greater worldwide mobility at a cost that the typical customer could afford.

The automotive sector is still at the forefront of technology today, revolutionizing how people get from point A to point B. The lead data scientist at Ford Motor Company and lecturer of our course Credit Risk Modeling in Python, Michael Crabtree, stated in a recent webinar that the company's innovation is now driven by data science rather than manufacturing.

Nowadays, data science, not manufacturing, is what drives innovation at Ford.


Data science is required for smart cities in the automotive sector.

Data science is scaling mobility for lower-income areas today, just like the Model T's industrial scalability did more than a century ago when it made mobility accessible to the general public. Regardless of class, gender, or ability, it makes transportation widely available without the exorbitant cost of ownership and is supporting this change for everyone.

For instance, optimization algorithms can give companies access to fuel-efficient cars to serve rural areas with everything from plumbing and food deliveries to Amazon deliveries. To create vehicles that help communities with disabilities, data scientists are also collaborating with reliability engineers.

These are just a few instances, but according to Michael, there are virtually unlimited application cases for data science, many of which have yet to be discovered.


Utilizing data in the automotive sector


There are numerous chances for businesses to rebuild around data because of the maturity and scope of the automobile sector.


Working with data from several data systems and data kinds is accomplished by a single application. The data comes in a table format, much like Excel, and many data scientists are accustomed to manipulating tabular data. However, data scientists in the automotive industry have access to a considerably wider range of data. For instance, a stream of hexadecimal digits is frequently used to store raw instrumentation data in the automotive sector. They might also come across information from intelligence systems in the form of point clouds and images from sensors. An automotive data scientist may also merge point clouds with instrumentation data and add it to a set of tables to better understand why an autonomous car performs a specific way and how it differs between vehicle models.


A further chance is a volume: The largest database Michael built for Ford has 80 billion records and responds to requests in under ten seconds! In the automobile sector, some real-time and transactional systems process more than 150 million records each day. Very big data clusters are required due to the enormous amount of automotive data collected. Data clusters in the petabyte (a million gigabytes) range are common in the automotive sector. (Visit the data science course for more details.)


Every step of the lifecycle of an automotive product involves data science.

  • Data Science Drives Product Development

 Before a vehicle may be sold to a consumer, many stages must be completed. Product development in the automotive industry starts with data science. Analyzing novel model configurations and modeling part reliability are two examples of activities for which data science is utilized. Data science adds to the process through simulation and analysis at scale as opposed to developing components and testing at each level as an isolated system.

  • Data Science Drives Excellence In Manufacturing

                       Additionally, auto industry data scientists make sure that only premium autos are offered. Even if engineers are capable of testing each vehicle's quality, this needs to be done for each one separately. A full population of parts, suppliers, and test data can be analyzed by data scientists. They closely examine their suppliers' financial performance, make predictions about their ability to provide on time based on past performance and use econometrics with regressions to evaluate the supplier regions' economic climate.


Check out the data science course in Mumbai, to know how data science techniques are utilized in various fields. Learnbay’s data science training has aided many aspirants land securing a position in leading firms.