meetups on DataScience.LA

SatRday LA - R Conference Announcement

Mon, 28 Jan 2019 01:25:06 -0800

Come and have fun with local useRs at SatRday LA on April 6, 2019. This is the first SatRday in LA, and the second in the states. If you have not heard of SatRday, it is a one-day affordable, inclusive, non-profit R conference organized by local R users.

Meetup: Machine Learning Best Algorithms: Gradient Boosting Machines (GBM)

Fri, 20 Jul 2018 17:43:00 -0700

Update_1: Slides for the 4 talks added below (at each talk section separately).

Update_2: Photos from the meetup and a short description of the event added below (at the bottom of the post).

Update_3: Video recording of the meetup here:

Meetup: Machine Learning in Production with Szilard Pafka

Fri, 28 Apr 2017 00:55:25 -0700

Update: Video recording of the meetup here:

▶ Machine Learning in Production with Szilard Pafka

Slides here:

Meetup: Stream processing with R in AWS

Wed, 08 Mar 2017 09:29:48 -0800

Update: Slides click here.

Video recording here:

This video is no longer available on YouTube (original link).

Stream processing with R in AWS
by Gergely Daróczi

Abstract: R is rarely mentioned among the big data tools, although it’s fairly well scalable for most data science problems and ETL tasks. This talk presents an open-source R package to interact with Amazon Kinesis via the MultiLangDaemon bundled with the Amazon KCL to start multiple R sessions on a machine or cluster of nodes to process data from theoretically any number of Kinesis shards. Besides the technical background and a quick introduction on how Kinesis works, this talk will feature some stream processing use-cases at CARD.com, and will also provide an overview and hands-on demos on the related data infrastructure built on the top of Docker, Amazon ECS, ECR, KMS, Redshift and a bunch of third-party APIs.

Meetup: No-Bullshit Data Science

Fri, 17 Feb 2017 11:49:40 -0800

Update: Slides here:

Slides: https://speakerdeck.com/szilard/machine-learning-meetup-febr-2017

Our next meetup:

No-Bullshit Data Science
by Szilard Pafka

Abstract: While extracting business value from data has been performed by practitioners for decades, the last several years have seen an unprecedented amount of hype in this field. This hype has created not only unrealistic expectations in results, but also glamour in the usage of the newest tools assumably capable of extraordinary feats. In this talk I will apply the much needed methods of critical thinking and quantitative measurements (that data scientists are supposed to use daily in solving problems for their companies) to assess the capabilities of the most widely used software tools for data science. I will discuss in details two such analyses, one concerning the size of datasets used for analytics and the other one regarding the performance of machine learning software used for supervised learning.

Meetup: Machine Learning at Google

Thu, 24 Nov 2016 22:27:11 -0800

UPDATE: The meetup “sold out” in record time (35 minutes!) from announcing it on our mailing list. There are more than 250 people on the waiting list now.

Machine Learning at Google
#

Come and hear about a range of the work that is being done in Google’s Los Angeles office in the area of machine learning:

How-to organize a satRday conference (v0.1)

Fri, 18 Nov 2016 13:31:13 -0800

It has been over 2 months since the first satRday conference ended and I shared some quick impressions on that, where I also promised a follow-up post on my experiences as ending up being the main organizer of the event – this is what you are reading now. Although the idea was born at the EARL Boston conference last year, and I decided to take part in the project while living in Los Angeles – the conference took place in Budapest after all as logistics seemed to be easier there thanks to my closer relationship to the Hungarian R User Group.

Deep Learning @Whisper

Tue, 03 May 2016 11:37:29 -0700

Deep Learning @Whisper
talk by Nick Stucky-Mack

Video now available, thank you to community member Carl Mullins for providing video services:

▶ Deep Learning @Whisper THU, MAY 12 AT 6 00 PM, VENICE, CA

In this talk we will discuss how Whisper uses machine learning for natural language processing tasks. We will focus on how Whisper applies deep learning to moderate content created by its more than 20 million monthly active users. The talk will explore the problem of short text classification, and will cover Whisper’s choices in machine learning models, software development, and hardware.

What has Kaggle learned from 2 million machine learning models?

Fri, 05 Feb 2016 16:36:40 -0800

What has Kaggle learned from 2 million machine learning models?

Anthony Goldbloom, founder and CEO of Kaggle

UPDATE:Video recording of the talk available here

Kaggle is a community of almost 450K data scientists who have built almost 2MM machine learning models to participate in our competitions. Data scientists come to Kaggle to learn, collaborate and develop the state of the art in machine learning. This talk will cover some of the lessons on winning techniques we have learned from the Kaggle community.

PyDSLA: Erin LeDell, Presents Intro to Machine Learning with H2O and Python

Tue, 05 Jan 2016 09:30:53 -0800

Join PyDSLA for a talk by Erin LeDell Intro to Machine Learning with H2O and Python

Get your tickets HERE

Summary:

This workshop will provide an overview of how to use H2O, the scalable open source machine learning library, from Python. The core algorithms of H2O are implemented in Java, however, fully-featured APIs are available in R, Python, Scala, and also through a web interface. The focus of this hands-on workshop will be the “h2o” Python module.

LA Data Warehouse, Business Intelligence and Analytics - Jihoon Son

Tue, 05 Jan 2016 09:30:37 -0800

Tickets available HERE

Talk:

Jihoon Son Presents - Introduction to Apache Tajo: Future of Data Warehouse

Abstract: Apache Tajo is a data warehouse system for Web-scale data. It provides virtual integration of a multitude of diverse data sources, thereby facilitating easy and rapid data integration which has been regarded as an essential, but heavy step in business intelligence. In addition, it has a fault-tolerable distributed query engine for accelerating query speed. With the “query federation” and “distributed processing” capacities, Tajo is capable of providing users with reliable and efficient analysis of Web-scale data spread on multiple sources. I will introduce Apache Tajo including its overall architecture, current state and challenges, and discuss advantages what Tajo can bring to users. In addition, I will give a brief demo of Tajo.

DW/BI/Analytics Meetup Summary: Inaugural Event

Sun, 15 Feb 2015 13:45:12 -0800

The LA Data Warehouse, Business Analytics and Analytics meetup had a fantastic first event with a great audience (full house), an awesome host (thanks Edmunds: Punnoose for taking the lead and the Team onsite and helping) and a great mix of 3 talks overviewing the field.

Data Science LA Meetup Summary: Data Science @ IRIS.TV

Thu, 08 Jan 2015 20:00:26 -0800

(Posting the slides and video recording from this event is long overdue - our apologies for the delay.)

At the September LA Data Science/Machine Learning meetup we had IRIS.TV’s Chief Data Scientist Tom Sullivan and VP of Engineering Joel Spitalnik presenting.

IRIS.TV is a video recommendation engine, and in their excellent talk Tom & Joel shared with us their approach to personalization and how they use machine learning to achieve their goals. They also shared the architecture of their systems, including how data is flowing between the various parts (i.e. the ETL pipeline). One thing that popped up as a question and I found interesting is that the usage of R and Python in their company is split 50-50. In fact, while Tom is using mainly R, Joel is using mainly Python. While this is perhaps unusual at a relatively small startup, our survey on software tools used for data science revealed that most data scientists in LA (at least those attending the meetups) are using R or Python, with a roughly equal split/small advantage for R. It just happens that for obvious reasons (such as network effects) that one tool dominates in most companies.

DataVis with Plot.ly (@plotlygraphs) - Meetup Summary

Tue, 30 Dec 2014 15:55:03 -0800

It pains me to admit it, but even though I had visited their site, created an account, and played around with their tools, I didn’t really get the value proposition behind Plot.ly. I already use ggplot, bokeh, and d3.js, I already use knitr and ipython notebooks, so why do I need a new way of posting my plots on a web page?

Photos from our LA User Groups First Annual Holiday Party

Tue, 09 Dec 2014 10:02:02 -0800

It was a whole bunch of intense fun photographing so many of you at our Los Angeles User Groups Holiday Party last week! (Only junkies and programmers would think “User” is a good name for a holiday party.) But we do throw a helluva party, and nearly 80 of you were photographed for head shots. You guys are awesome, and we all really enjoyed it!

Data Science Meetup - January 13: Data Science @ Datapop

Tue, 25 Nov 2014 09:45:53 -0800

Data Science @ Datapop: The Marketing Knowledge Graph

presented by Dr. Ben Zamanzadeh, CTO at DataPop

Today’s eCommerce has evolved into a complex multi-dimensional optimization problem. DataPop’s semantic technology and Marketing Knowledge Graph is used to generate Omni-Channel advertising campaigns. This presentation will review our technology stack, machine learning models, discuss what worked what did not work for us and do a live demo of Marketing Knowledge Graph.

LA R meetup - January 21: RStudio's Shiny with Joe Cheng

Thu, 20 Nov 2014 10:04:38 -0800

At this meetup we’re fortunate to have RStudio’s lead developer Joe Cheng flying in from Seattle to talk about Shiny. Shiny makes it possible to develop interactive web applications by writing only a handful of R code. It lowers the barriers to constructing interactive dashboards that you can use to better explain your results to your boss and/or co-workers, or it can help you productionalize your analysis so that others in your company can make use of it regularly without the need to continually bother you.

LA R Meetup Summary: Highlights from useR! 2014 - Part 2

Tue, 18 Nov 2014 09:20:24 -0800

Last week the LA R meetup featured another round of 5 speakers each highlighting a few things they had found interesting at the useR! 2014 conference. This was the second such event after the first one in September - probably because useR! was awesome enough that it inspired 10 volunteers to stand up on stage and talk more about it.

Python Data Science Meetup Summary: Data Science with Python

Mon, 10 Nov 2014 10:02:46 -0800

Last week, the Python Data Science LA meetup debuted with a fabulous event. It was already much anticipated, with more than 250 people showing interest (RSVP+waiting list), and a lucky 100 converged upon the hip Venice Arts venue to hear our speakers. Our sponsor OpenMail has also done a fantastic job in getting us this venue, bringing food+drinks and taking care of all the details. Thanks OpenMail Team!

Data Science Toolbox Survey Results... Surprise! R and Python win

Wed, 24 Sep 2014 10:25:13 -0700

This is a re-publication of a blog post from a blog I created not long before we got the idea to start DataScience.LA, and in many aspects it is the genesis of DSLA (the blog’s name, the people getting together - thanks Eduardo, Amelia and Leigh). It was also the spark that lead to the creation of the Python Data Science LA meetup.

The case for Apache Spark

Tue, 23 Sep 2014 10:00:05 -0700

I have been following Big Data, Hadoop in particular, for the past four years and a lot has changed since then. At the time I was looking for the next big wave of technologies, especially on the back end of development as that was my forte at the time. After our initial introduction, the more I learned the more I felt Hadoop was the right tool to get the job done. Focusing upon the development aspects of job vs actual maintenance of the servers/cluster has always been a no-brainer for me. Now that Hadoop has been adopted by a wide variety of industries and has grown quite popular, it has become much more apparent that there are indeed areas where Hadoop is lacking. Security and real-time analytics are two of these concerns which both affect real-time processing and reporting, but fortunately there is a lot of current work happening to address these limitations. Consequently, there is a group of big data vendors who are investing a large number of resources as a remedy, bringing us newer tools such as Yarn, Tez, Storm, etc. Also, acquisitions of XA Secure by Hortonworks and Gazzang by Cloudera are contributing to the solving these issues around security.

LA R Meetup Summary: Highlights from the useR! 2014 Conference

Wed, 10 Sep 2014 09:45:20 -0700

Last week the LA R meetup featured 5 speakers each highlighting a few things they had found interesting at the useR! 2014 conference (which I had the pleasure of helping to organize this year in Los Angeles). This meetup proved to be interesting even to those of us who attended the conference (which was about half of our audience), simply because there were so many cool things to hear and enough parallel sessions at the conference that no one could possibly capture all of the action.

DataVis LA Meetup Summary: Eyeo Festival Highlights

Fri, 05 Sep 2014 09:40:04 -0700

Last week, I got the opportunity to share the highlights from the eyeo festival at the DataVis LA meetup. The eyeo festival is a yearly event held in Minneapolis that draws together artists, technologists, designers, and many more people who work with art and computers. As I said in the meetup description, a lot of the things that caught my eye this year could be described better by the term “art” than straight “data visualization,” although there’s usually both at the festival. I also really liked that this year’s festival emphasized the history of computer art and data visualization. By bringing in Frieder Nake, Lillian Schwartz, and Roman Verostko, the organizers grounded us in the history that started in the 1960s.

Announcing Partnership with LA Big Data Community

Wed, 03 Sep 2014 09:54:50 -0700

Today I am happy to announce that we are expanding our partnership on several fronts with my friend Subash D’Souza, a leader of the Big Data community in LA.

During the past few years I have been organizing several data science meetups in Los Angeles (LA R, DataVis LA, Data Science/Machine Learning) and Subash has provided the same kind of leadership on the big data front (LA Hadoop, HBase, LA Big Data). While I think of big data as primarily technology and of data science as a blend of statistics and technology (and not only for large data sets), there is clearly a significant overlap between these two fields. Considering this overlap, we have already been collaborating for some time regarding these various meetups, e.g. organizing joint events or speaking at one another’s meetups.

Win Books and T-shirts in Our DataScience.LA Meetup Raffles, Powered by R and dplyr!

Tue, 02 Sep 2014 09:41:54 -0700

We have a few pieces of swag that we are raffling off to the community at our upcoming meetup events. Our stash consists of 25 data-oriented books, primarily from O’Reilly plus 25 T-shirts from useR! 2014 conference and Revolution Analytics (see picture above). Did we get your attention?

Announcing the New Python Data Science LA Meetup Group

Fri, 29 Aug 2014 09:01:19 -0700

I am excited to announce that we are starting a new meetup group - Python Data Science LA - to serve the needs of the LA data science community using Python!

At an earlier Data Science/Machine Learning meetup we conducted an informal survey on tools for Data Science which showed that a large majority of data scientists use R or Python, with R taking a slight lead. (I know, those attending the meetups bias the results towards open source tools.)

Data Science meetup - September 15: Data Science @ IRIS.TV

Tue, 19 Aug 2014 10:00:44 -0700

Data Science @ IRIS.TV

by Dr. Thomas Sullivan, Chief Data Scientist and Joel Spitalnik, VP Engineering

While video recommendations best serve the viewer experience when presented in real time, machine learning is a computationally intensive process that is often done on the back end. IRIS.TV’s utilizes a hybrid process to synchronize computational tasks for learning so that engineering and data science teams can shrink the latency between analyzing history and presentation of recommended videos to improve the experience.