Data science requires proficiency in one or more programming languages, depending on the task at hand (1). These languages can be connected together. To save time while programming, we can use libraries or frameworks. They are collections of pre-written code. We can import them into our code, allowing us to easily use their functions and…
Category: Data Analytics
From measuring performance to predicting outcomes, how can we design the models, metrics, and methods that turn raw data into business advantage. This section explores the analytical engine behind decisions like scoring systems, financial diagnostics, clustering methods, predictive models, and the architecture that makes it all run.
The Data Analytics Lifecycle: From Exploration to Prescription
Let’s embark on an searching quest, akin to finding ‘Dollar Waldo’ in the expansive world of data. Just as Waldo blends into the crowd, waiting to be discovered, valuable actionable insights often hide within vast datasets, requiring a keen eye and systematic approach to be unearthed. In this post, we will embark on a journey…