Data Science Desktop Survival Guide by Graham Williams
Desktop Survival Project Home Preface Data Science Introducing R R Constructs R Tasks R Strings R Read, Write, and Create Data Template Data Exploration Data Wrangling Data Visualisation Statistics ML Template ML Scenarios ML Activities ML Applications ML Algorithms Cluster Analysis Decision Trees Computer Vision Graph Data Privacy Literate Data Science Coding with Style Resources Bibliography Index

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Chapter: Data Science

20200104

Science is analytic description, philosophy is synthetic interpretation. Science wishes to resolve the whole into parts, the organism into organs, the obscure into the known. (Durant, 1926)

Today we live in a data rich, information driven, knowledge strained, and wisdom scant world. Data surrounds us every where we look. Data describes every facet of everything we know and do. Today we are capturing and storing more data electronically (i.e., digitising the data) at a rate we humans have never before been capable of. We have so much data available and even more yet to be digitised from the world around us, and so much of the digitised data yet to be analysed. Most of our work today is about data, and perhaps it always has been. Data science is a broad church capturing the endeavour of analysing data and information by appropriately applying an ever changing and vast collection of techniques and technology to deliver knowledge to be synthesised into wisdom. The role of a data scientist is to perform the transformations that make sense of it all in an evidence based endeavour delivering the knowledge deployed with wisdom. The data scientist acts with humanity and philosophy to synthesise knowledge into wisdom whereby we thrive to resolve the obscure into the known. It is this synthesis that delivers the real benefit of the science—whether that benefit be for business, industry, government, environment, but always for humanity. A data scientist brings together a suite of skills to solve problems in a data driven way. It is not the only way to solve problems, and indeed may not even be the best way to solve problems, but it is the current best way to deal with many real world use cases today. In the future we will see more focus on knowledge representation and causal reasoning, but for now we can do a lot with data.