These days we find ourselves awash in oceans of data. We use tools like MongoDB, Hadoop, Spark, Pig, D3, Tableau, Compass, and R to help us make sense of them all. But are we truly data literate?
An American election has come and gone, leaving pundits and analysts dazed and confused. Predictive models, suffused with historical data and built on standard assumptions proved grossly wrong. It was a reminder that testing inputs against presumptive biases is key to avoiding the "garbage in, garbage out" conundrum.Working backward from summarized rollups, it calls for analytical processes driven with clean and complete data informed with clear, insightful premises. Data literacy calls not only for a clear understanding of the pros and cons of our analytical decisions and how to read their results, but also an awareness around input completeness and relevance.
This month we have two veteran speakers from MDBW 16 who share their experience using MongoDB to help acquire, clean, organize, and filter information for meaningful consumption.
Aileen Nielsen Mobile Health Metrics: A MongoDB-first Approach
Given the rapidly growing and changing environment of healthcare analytics in the mobile sector, it's important to have an agile and flexible system for tracking and analyzing data. MongoDB is perfect for this and easily reflects most of the ways that health care data is routinely displayed to users of health-related mobile applications. MongoDB is the right backend choice for a mobile-first approach to healthcare technology. My talk will focus on iOS development with MongoDB. This session will show you how adaptable MongoDB is to human behavior, both on the developer side and on the customer side and this recognition of human behavior is critical to mobile development. Topics discussed will include aggregation, analytics, and mobile development.
Aileen Nielsen serves as Software Engineer at OneDrop.Today, a diabetes management platform.Since completing degrees in anthropology, law, and physics from Princeton, Yale, and Columbia respectively, Aileen Nielsen has worked in corporate law, physics research laboratories, and, most recently, NYC startups oriented towards improving daily life for under-served populations - particularly groups who have yet to fully enjoy the benefits of mobile technology. She has interests ranging from defensive software engineering to UX designs for reducing cognitive load to the interplay between law and technology. In addition to engineering One Drop's diabetes-management products by day, Aileen currently serves as a member of the New York City Bar Association's Science and Law Committee, where she chairs a subcommittee devoted to exploring and advocating for scientifically-driven regulation - and deregulation - of new and existing technologies.
Jennifer Shin Validating an Open Society
Methodologies for big data have been uniquely challenging to incorporate into science. Despite the abundance of publicly available data sources, there is a lack of resources available that outline best practices for working with these data sets, which can put research and development projects at at risk when utilizing technological tools, such as APIs.
This talk will explain the challenges that exist with utilizing open data sources and tools and demonstrate how mathematical concepts can be applied to validate the data and test data technologies. In particular, we will discuss our research and experience using the FDA's openFDA API.
JenniferShinis the Founder of 8 Path Solutions, a data science, analytics, and technology company and Senior Principal Data Scientist at The Nielsen Company. As an experienced data scientist and management consultant, Jennifer has led complex, large scale, and high profile projects for corporate, public, and private clients, including GE Capital, the Carlyle Group, Fortress Investment Group, and Columbia University.
A recognized thought leader, her expertise has been acknowledged byUSA Today,VentureBeat, and Reuters, and in 2015 she was identified by IBM as aBig Data & Analytics Hero. Jennifer is on the faculty at UC Berkeley Master of Information and Data Science program, lecturing in statistics and data science.She is also an instructor at Columbia Business Schooland on the Advisory Board for the M.S. in Data Analytics program at the City University of New York.Jennifer earned bothher undergraduate degree in Economics, Mathematics, and Creative Writing and her graduate degree in Statistics from Columbia University.
Agenda:
6:00pm Meet fellow technologists over pizza & soft drinks
6:30pm Mobile Health Metrics: A MongoDB-First Approach (Aileen Nielsen)
7:15pm Validating an Open Society (Jennifer Shin)
8:00pm Extended Q&A with speakers
Please join us for this post-Thanksgiving event. We're looking forward to seeing you there and hearing your thoughts on these talks and what we could do differently to improve data decisions.
