Agile Data Science 2.0 (O'Reilly 2017) defines a methodology and a software stack with which to apply the methods. *The methodology* seeks to deliver data products in short sprints by going meta and putting the focus on the applied research process itself. *The stack* is but an example of one meeting the requirements that it be utterly scalable and utterly efficient in use by application developers as well as data engineers. It includes everything needed to build a full-blown predictive system: Apache Spark, Apache Kafka, Apache Incubating Airflow, MongoDB, ElasticSearch, Apache Parquet, Python/Flask, JQuery. This talk will cover the full lifecycle of large data application development and will show how to use lessons from agile software engineering to apply data science using this full-stack to build better analytics applications.
About the speaker:
Russell Jurney is principal consultant at Data Syndrome, a product analytics consultancy dedicated to advancing the adoption of the development methodology Agile Data Science, as outlined in the book Agile Data Science 2.0. He has worked as a data scientist building data products for over a decade, starting in interactive web visualization and then segwaying towards data products, machine learning and artificial intelligence at companies such as Ning, LinkedIn and Hortonworks. Russell is a self taught visualization software engineer, data engineer, data scientist, writer and most recently, I’m becoming a teacher.