Pleased to Meetcha: Big Data Meetups in New York and Boston


I moved to New York this past Monday. It’s been a year and a half since I last lived here, and changes which were fledgling when I left (Silicon Alley, Williamsburg waterfront, smoothie stands) are full-fledged now. Part of my reason for moving was selfish—I missed the city. Part of my reason was practical—New York’s big data ecosystem is leaner, in some areas faster, more focused on the consumer: in other words, it’s a good complement to the rich, database- and life-science/healthcare-focused ecosystem in Boston.

While I’ve shifted my residential alliances to New York, professionally, I’ll be keeping a foot in each city, exercising my role as Riparian’s mouth by meeting as many data scientists, analysts, and high-volume email users as I can, partly on an individual basis, and partly through meetups like the ones listed below. (At last, I get to the point of this post!). The following are just a small selection of data-related meetups in New York and Boston, but I think they ably represent some of the buzziest aspects of and players in this very buzzy topic.

 New York

1. Analytics and Data in Financial Services

  • Description: Knowledge of and fluency in the language of analytics is becoming increasingly important in business, especially in the financial services industry.
  • Aimed at: People doing big data analysis, especially those in the FS arena
  • Hosted by: Jaime Fitzgerald (in / t)
  • Members: 346
  • Past meetups: “From Tufte to the Magic Kingdom: Telling the Story Behind the Data,” “Member Demos of Predictive Models”

2. Predictive Analytics, Applied Machine Learning, Big Data

  • Description: Discuss diverse topics in predictive analytics and applied machine learning.
  • aimed at: Analysts, computer scientists, engineers, executives, entrepreneurs and students with a deep interest in these fields & related technologies.
  • hosted by:
  • Alex Lin (in / t)
  • members: 2155
  • Past/Upcoming meetups: “Designing Machine Learning Algorithms for Hadoop,” “The art of predictive analytics: More data, same models”

3. Open Analytics NYC

  • Description: A group devoted to the use and development of open source, big data, agile intelligence solutions, for the NYC Metro area.
  • Aimed at: People interested in solving real business problems utilizing open source, big data analytical solutions.
  • Hosted by: Scott Raspa (in / t)
  • Members: 152
  • Upcoming meetup: “How to Gain Intelligence from Open Analytic Solutions using MongoDB & Hadoop”

4. Digital Semiotics

  • Description: Talk about the relationship between semiotics and digital technology, covering topics around: traditional semiotics, computational semiotics, computational linguistics, user experience, interface design, interaction design, information architecture, robotics/intelligent machines, human-computer interaction
  • Aimed at: Academics, advertising professionals, independent researchers, computer scientists, digital anthropologists, linguists, designers
  • Hosted by: Thomas Wendt (b / t)
  • Members: 44
  • Upcoming meetup: n/a

5. NoSQL NYC

  • Description:  Discuss any alternative databases, from large distributed key-values hashtables to document-stores.
  • Aimed at: NoSQL enthusiasts
  • Hosted by: Edward Capriolo (b / in), Mark Pollack (in)
  • Members: 930
  • Upcoming/past meetups: “The Graph in Your Data - A Neo4j Intro & An Intro to GoldenOrb”

Boston

1. Boston Predictive Analytics

  • Description:  Present informative lectures, hands-on tutorials, networking events, etc. Three main focal points:  business applications, advanced mathematics, and computer science; with topics covering recommender systems, machine learning, Google Analytics, data visualization, social media / text analytics, and related topics.
  • Aimed at: ML/NLP/Data Miners/Modelers
  • Hosted by: John Verosteck (b / t)
  •  Members: 1139
  • Past/upcoming meetups: “Content Recommendations Using Bayesian Classification via Apache Mahout”

2. Open Analytics Boston

  • Description: see “Open Analytics New York”
  • Aimed at: ditto
  • Hosted by: Scott Raspa
  • Members: 95
  • Upcoming meetups: n/a

3. The Data Scientist

  • Description: This group will concentrate on understanding the tools and skill-sets needed to become an effective Data Scientist. We will explore all topics related to the data lifecycle including acquiring new data sets, parsing new data sets, filtering and organizing data, mining data patterns, advanced algorithms, visually representing data, telling stories with data and softer skills such as negotiations and selling your ideas based upon data.
  • Aimed at: Data scientists, and those who aspire to that title.
  • Hosted by: John Baker (in / t), Carrie Stalder (in / t)
  • Members: 158
  • Upcoming meetups: n/a

4. Emerging Business Technology

  • Description: Provides engineers, practitioners and managers the context needed to evaluate and adopt rapidly evolving business technologies. Leave with an understanding of what the technology is, why it’s used, when to use it, and next steps to take. We’ll review use cases, processes, tools, and practices in a mini-conference format through short presentations, hands-on tutorials, Q&A and code walkthroughs. Topics may include Mobile app development, HTML5, responsive design, high-concurrency applications, interaction design, modern languages and frameworks, NoSQL databases, and mobile / tablet application design.
  • Aimed at: Engineers and business users.
  • Hosted by: Dan Adams (b / t)
  • Members: 172
  • Upcoming meetups: “NoSQL in the Real World”

5. Boston Hadoop User Group

  • Description: For developers who are using Hadoop, or would like to learn more about it. Also includes technologies built with/on top of Hadoop: Hive, Pig, HBase, etc.
  • Aimed at: See above. Essentially, anyone interested in big data technologies (not only Hadoop-specific ones, though those are the foci).
  • Hosted by: Reed Shea (in / t) and, last month, myself (in / t)
  • Members: 761
  • Upcoming/past meetups: “Training Session with Hortonworks,” “More Data vs. Better Data vs. Better Algorithms

 

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