Article: Key Steps to Model Creation: Data Cleaning & Data Exploration, by Stu Bailey, Contributor, InfoWorld
In today’s modern world, businesses are starting to recognize the value that robust analytics can bring to both their understanding of their industry and their bottom line.
The steps to create, deploy and gain results from a model require collaboration between Data Science and IT. This means letting the IT Team work on IT and having the Data Scientists be Scientists.
In this article, we’ll uncover some of the lesser known, but essential steps of the data science process that revolve around data cleaning and exploration. This process involves examining raw data and condensing it down to a more usable form and identifying patterns and relationships in data, we will cover:
- Reveal key insights into the data that will eventually translate into real value for the end user
- Gain insights that could be previously unknown relationships between features, other actionable phenomena
Both data cleaning and exploration are key steps in the model creation process, and by following best practices and philosophies around these processes an organization can enable successful collaboration and iteration between data science and IT teams.
Make sure to continue following us along in our series of posts to discover more key best practices to creating analytics from lab to factory, as a service!
