How it comes together
Our Data Science Ecosystem is the fastest and most cost-effective way to turn raw data into Data Science results and business recommendations.
The ecosystem can be described in various ways, from a model development perspective, a data handling view, or by the fundamentals around which the ecosystem thrives.
“Business intelligence” is an umbrella term for drawing meaningful information from a company’s raw data. It requires extracting, transforming, loading, and visualizing a company’s various, and often disparate, data sources.
Statistics help make inferences about data and explain experimental outcomes. From A/B testing and error-based algorithms to location-based analysis, these analytics power critical business decisions.
Specific techniques are effectively applied to summarize and understand data sets. This is often aided by data visualizations, which are helpful for effectively interpreting the data at hand and identifying patterns and outliers.
An extension of traditional computer programming, artificial intelligence relies on agents to interpret data and fuel optimization models. Machine learning automates model building, and allows machines to process and adapt with experience.
Important insights can be extracted from building and using models built on historical data. Predictive modeling is a form of advanced analytics used to make predictions about significant unknown future events.
Many unstructured data sets exist of plain text, image, audio, and visual files that contain valuable information. Discovering patterns in multimedia data involves extracting implicit knowledge from these varied sources.