The Agile Data Science meetup is a place for sharing and discussion of the practice and knowledge of applying agile methods to the practice of data science teams and projects. Experts will present their methods, experience and lessons learned in agile data science. The content will be targeted at data scientists, machine learning engineers, product managers and software engineers working on analytics products and systems.
May 6 - 8, 2019 from 9AM - 5PM
Three Days, 8 Hours Per Day = 24 Hours
Note: this is a paid event, but most events in this group will be free.
This is a professional development class that teaches how to iteratively craft entire analytics web applications using Python, Flask, Spark (SQL, Streaming, MLlib), Kafka, MongoDB, ElasticSearch, Bootstrap and D3.js. This stack is a popular one and is an example of the kind of stack needed to process and refine data at scale in real world applications of data science. During this course, students will use airline flight data to produce an entire analytic web application, from the ground up.
The course will serve as a tutorial in which the student learns basic skills in all the categories needed to ship an entire analytics application. Each section, the student will add a layer to their application, creating the start of something they can really use in their respective domains. While users will not learn the tools in detail, they will establish the working foundation needed, and will practice the kind of active learning-as-you-go that analytics requires. The goal of the course is to establish a working foundation with working code that the student can extend as they learn going forward. Working end-to-end code makes learning much easier.
The organizational principle behind the course is the 'data value pyramid', pictured below. Students will climb the data-value pyramid, refining data at one step to reach the next.
This class will be a chance for a practicing data scientist or web developer to learn to turn their data and analyses into full-blown actionable web applications. We will put the student in a position where she can independently improve on the foundation we have given her to go on to build great analytics applications!