Practical Data Science

PART I Building Blocks Chapter 1 Introduction to Data Science o Data Science o Data Lakes o Extracting Actionable Business Knowledge from Data Lakes Chapter 2 The Building Blocks of the Data Science Technology Stack o Spark o Mesos o Akka o Cassandra o Kafka o Elasticsearch o R o Data Vault o Data Warehouse Bus Matrix o MQTT PART II Layers Chapter 3 A Layered Framework of Practical Methods for Performing Good Data Science o The Top Layers of a Layered Framework o Engineering a Layered Framework for High-Level Data Science Chapter 4 The Business Layer Chapter 7 The Audit, Balance, and Control Layer Chapter 8 The Functional Layer and Its Super Steps PART III The Super Steps of the Functional Layer Chapter 9 The Retrieve Super Step o Ingress Layers o Data Lakes and the Internet of Things o Deploying the Retrieve Super Step in a Technology Stack Chapter 10 The Assess Super Step o Data Quality o Data Scrubbing o Deploying the Assess Super Step in a Technology Stack Chapter 11 The Process Super Step o Data Vault and Time-Person-Object-Location-Event Hubs o Machine Learning o Deploying the Process Super Step in a Technology Stack Chapter 12 The Transform Super Step o Data Warehouse Bus Matrix o Statistics o Graph Database o Deploying the Tranform Super Step in a Technology Stack Chapter 13 The Organize Super Step of the Functional Layer o Data Mart o Deploying the Organize Super Step in a Technology Stack Chapter 14 The Report Super Step of the Functional Layer o Virtualization o Deploying the Report Super Step in a Technology Stack PART IV Clusters and Grids Chapter 15 Building Cluster-Grid Appliances for Polyglot Data Science o o Grids o Torus Networks o Mesosphere Micro Services o Cloud Computing o  Fog Computing o Bare Metal Solutions Chapter 16 The Future of Data Science and Data Engineering