Meetups

Update: Video Recording: ▶ LA Data Science Meetup, March 30, 2021 - Stanislav Kirillov: CatBoost Slides: here

Update: Video recording: ▶ LA Data Science Meetup, Jan 12, 2021 - James Lamb: Recent Developments in LightGBM Slides and code: here.

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.

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:

Update: Video of the 2nd half of the talk is here (1st half was not recorded, sorry): This video is no longer available on YouTube (original link). In our next meetup, we’ll have Hadley (I think no introduction needed). Packages made easy by Hadley Wickham

Update: Video recording of the meetup here: ▶ Machine Learning in Production with Szilard Pafka Slides here:

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.

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.

Update: Video recording of the meet up here: This video is no longer available on YouTube (original link). Come to the PyDSLA January Meetup to hear talks about PyMC3 and jobs.py! REGISTER HERE For this event we are incredibly fortunate to have two incredibly speakers give really interesting talks.

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:

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.

REGISTER HERE ON EVENTBRITE DATE AND TIME Wed, November 2, 2016 6:30 PM – 9:30 PM PDT Come to the PyDSLA November Meetup to hear talks from the community! In the last month, the organizers of the PyDSLA Meetup sent out a call for speakers, and the community answered with a number of great members coming forward willing and able to talk about some of the cool ways they are using the Python Data Science ecosystem to do cool stuff.

Update: This event has ended. Slides are available here. # Update: Video recording by Carl Mullins: # This video is no longer available on YouTube (original link).

XGBoost is a fantastic open source implementation of Gradient Boosting Machines, one of the most accurate general purpose supervised learning algorithms. With interfaces from both R and Python among others, it has become a favorite tool in Kaggle competitions, and it’s also widely used in real-world applications. In this meetup we’ll have a talk from the best person on the topic,Tianqi Chen, main author of XGBoost.

SIGN UP HERE! Video Available Now: ▶ PyDSLA The Nitty Gritty of Advanced Analytics Using Apache Spark in Python May 5, 2016 Talk by Miklos Christine, solutions engineer at Databricks

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.

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.

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.

If you’ve never heard of kaggle, I guess the first question I have to ask is “how did you end up on DataScience.LA?” :) Kaggle is one of the foremost machine learning competition websites on the internet. Companies from GE to Criteo and everything in between create competitions. These competitions are in the form of a dataset, ranging from small to relatively large (up to many many gigabytes), a stated goal (such as predicting an event or finding a numeric value) and an evaluation criteria. The Kaggle community has grown in the last few years, so that now competitions often host thousands of participants!

Click HERE to sign up on Eventbrite! This is the second event of the new Data Warehouse, Business Intelligence and Analytics meetup. Talks: 1. Eduardo Arino de la Rubia: MySQL to Vertica with Tungsten Replicator In this very (very) short talk, I will discuss how you can, for free, stream your MySQL data to a Vertica Community Edition cluster, and do near real-time analytics on up to 1TB of data.