The entry point for most of us into analytics is our experience with Microsoft Excel. At some point though, projects and organizations require more. The breakdowns are typically:
We need to separate the data from the analysis, with storage, for a single source of truth.
We need to be able to automate the analysis.
We need the analysis to be reproducible.
We should not pay a third party obscene amounts of money for something as basic as arithmetic.
It’s fashionable for business leaders today to tout their sophisticated data science initiatives. Yet the path between identifying business problems and realizing the business value is not often clear.
A reality for many enterprises is that they have:
diverse individual customers, globally, with growing brand expectations.
ever more complex customer offerings - each with quality, lead time, conversion rate and dozens of other success metrics.
growing complexity in managing the ethical and regulatory aspects of their digital offering.
Brandon Rohrer of iRobot curates a “Teachable” collection of courses/blogs called End-to-End Machine Learning which include a number of free introductory sections, including:
Data Science Concepts, is a pocket guide to building the things that data scientists build. It opens with a 49 minute video, Data Science for Absolutely Everyone from his days building these materials in 2016 with Microsoft. As amazing as it is, there are only five questions that machine learning can answer: