Posts on DataScience.LA

Goodbye DataScience.LA, goodbye California!

Thu, 01 Jul 2021 10:04:03 -0700

I (Szilard) started organizing meetups more than a decade ago (the LA R Users Group est. 2009 was the very first data meetup in the Los Angeles area). A few years later, a couple of us (Amelia, Leigh, Eduardo and I, and also several contributors) started a few more meetup groups (e.g. Data Science) and this very website DataScience.LA. However, in recent years my DataScience.LA Leadership Teammates relocated and now it’s my turn, I’m the last one to leave California (I’m very excited to move to Texas!). While we are ceasing all activities in LA now and this is the last update/post on this website, we will keep the existing/old content accessible for a while. It was a great pleasure running these events/this site and getting to know so many of you. LA has so many great data science folks! However, now it’s time to say goodbye, goodbye LA and California, and good luck to you all.

Overview of H2O's GBM implementation

Mon, 10 May 2021 09:13:53 -0700

Update: Video Recording:

▶ LA Data Science Meetup, June 8, 2021 - Michal Kurka: H2O's GBM

Slides: here

CatBoost: Distributed Training, Uncertainty Estimation and Other News

Wed, 10 Mar 2021 08:36:43 -0800

Update: Video Recording:

▶ LA Data Science Meetup, March 30, 2021 - Stanislav Kirillov: CatBoost

Slides: here

Recent Developments in LightGBM

Tue, 15 Dec 2020 01:53:23 -0800

Update: Video recording:

▶ LA Data Science Meetup, Jan 12, 2021 - James Lamb: Recent Developments in LightGBM

Slides and code: here.

2 talks: GBM Benchmark In-Depth | The State of XGBoost

Thu, 15 Oct 2020 23:31:01 -0700

Update: Video recordings:

Talk #1 (Szilard Pafka: GBM Benchmarks)

▶ LA Data Science Meetup, Nov. 10, 2020 - Talk #1 - Szilard Pafka: GBM Benchmarks

Slides here

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:

R Meetup with Hadley Wickham

Sat, 28 Oct 2017 12:57:13 -0700

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

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.

PyDSLA: PyMC3 and jobs.py

Thu, 19 Jan 2017 21:15:07 -0800

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!

For this event we are incredibly fortunate to have two incredibly speakers give really interesting talks.

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.

PyDSLA: Talks from the LA Data Science Community

Fri, 14 Oct 2016 15:29:39 -0700

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.

DSSTNE: A New Deep Learning Framework for Large Sparse Datasets

Fri, 14 Oct 2016 15:29:22 -0700

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 workshop and meetup talk with Tianqi Chen

Mon, 06 Jun 2016 09:00:50 -0700

XGBoost is a fantastic open source implementation of Gradient Boosting Machines, a general purpose supervised learning method that achieves the highest accuracy on a wide range of datasets in practical applications. Deep learning is all the hype now, but apart from specific domains such as images, speech or text (i.e. problems with higher-level abstractions to be learnt and/or perception problems, where deep learning achieved some remarkable results indeed), it is usually outperformed by Gradient Boosting in a majority of general business domains and supervised learning applications. Proof of this and also because XGBoost has an easy-to-use interface from both R and Python, XGBoost has become a favorite tool in Kaggle competitions. Besides feature engineering, cross-validation and ensembling, XGBoost is a key ingredient for achieving the highest accuracy in many data science competitions and more importantly in practical applications.

XGBoost: meetup announcement

Thu, 19 May 2016 10:06:17 -0700

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.

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.

PyDSLA: The Nitty Gritty of Advanced Analytics Using Apache Spark in Python

Mon, 18 Apr 2016 12:57:06 -0700

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?

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.

Big RAM is eating big data - Size of datasets used for analytics

Wed, 18 Nov 2015 09:41:22 -0800

With so much hype about “big data” and the industry pushing for “big data” analytical tools for everyone, the question arises how many people have big data (for analytics) and how many of them really need these tools (which are more complex and often more immature compared to the traditional tools for analytics).

Meetup Summary - Winning Data Science Competitions

Fri, 13 Nov 2015 12:13:40 -0800

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!

R and Impala: it's better to KISS than using Java

Thu, 29 Oct 2015 15:10:25 -0700

One of the best things I like in working at CARD.com is that I am not only crunching R code in 24/7, but I also have the chance to interact with and improve the related data infrastructure with some interesting technologies.

After joining the company in January, I soon realized that while Impala is a very powerful database for handling data that do not comfortably fit in MySQL, it’s still not as fast as one might expect when querying large amount of data from R. Sometimes I had to wait several minutes for a query to run! So I used this spare time to think about how to improve the workflow.

Winning Data Science Competitions

Mon, 19 Oct 2015 22:19:42 -0700

Our next Data Science meetup:

Winning Data Science Competitions

by Jeong-Yoon Lee

In this talk, I am going to share my experiences in data science competitions. After an overview of the competition structure and best practices, I will present the 1st prize winner’s solution for KDD Cup 2015 with the behind-the-scenes story.

LA City College Forms Data Science Team with City of LA

Tue, 29 Sep 2015 15:28:11 -0700

In a partnership with the City of Los Angeles government, LA City College (LACC) has formed a civic data analysis team for 2- and 4-year college students and anyone looking to enter the data science profession. The team will complete a beginning-level data analysis project on Friday afternoons during a 10-week period during the Fall 2015 semester, using data provided by the City of LA. We will present our results to City of LA staff in December.

Announcing Meetup! Lior Abraham of Inter|Ana

Tue, 23 Jun 2015 17:10:06 -0700

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.

PyDSLA Video: Carlos Guestrin, CEO of DATO

Thu, 18 Jun 2015 15:53:26 -0700

On May 14th at the Venice Arts Center, the Los Angeles Python and Data Science communities were fortunate to hear a talk from world-renowned expert and CEO of Dato, Carlos Guestrin. Often times when you have a meetup where a presenter is tightly identified with a company, the concern is that the presentation will fundamentally become a product pitch. We had nothing to be worried about in this case - Carlos presented for nearly an hour on a wide range of subjects from how the Machine Learning pipeline has changed over time to Deep Learning!

Benchmarking Random Forest Implementations

Tue, 19 May 2015 08:30:44 -0700

I currently have the need for machine learning tools that can deal with observations of the order of 10 millions in the context of binary classification. That kind of data is a few GBs in size and it fits comfortably nowadays in the RAM of a decent single machine. It is a trivial task for linear models, as there are plenty of open source tools that can train a logistic regression with this amount of data on a single machine in a few seconds, even while using only 1 processor core (many of these tools are single-threaded). Linear models are also the gold standard of large-scale machine learning that can run on clusters, processing very large distributed datasets.

PyDSLA: Dato CEO, Carlos Guestrin, Presents Dato

Tue, 05 May 2015 08:40:40 -0700

*** EVENTBRITE LINK HERE ***

PyDSLA - Dato: A Platform for Practical, Large-Scale Machine Learning

Join PyDSLA for a talk by Carlos Guestrin about Presents Practical, Large-Scale Machine Learning in python.

Summary:

Data Science is perhaps the hottest profession on the market today. Folks with backgrounds ranging from Statistics and Physics to Engineering and Computer Science are eager and excited to transition to this field. However, designing and deploying data analysis and machine learning apps at scale is a significant challenge to overcome: For some folks, machine learning algorithms and methods can be obscure, too mathy, and disconnected from practice. For others, writing and deploying scalable software requires significant effort, which distracts them from focusing on their deep analytic efforts.

Data Science @ Whisper

Mon, 30 Mar 2015 09:23:55 -0700

Where were you on the evening of Monday, March 23rd? Do you remember what you were up to? If you were one of the over 100 attendees to the latest installment of Szilard’s Data Science Meetup, you definitely remember! You were at Cross Campus Santa Monica, receiving a teaser on fundamentals of probabilistic programming by LA ML Meetup founder Rob Zinkov along with a detailed master class on modern machine learning architectures and approaches by Los Angeles’ own Whisper!

ASA DataFest 2015 at UCLA

Wed, 04 Mar 2015 10:00:20 -0800

What is DataFest?
#

DataFest is a team competition, where a team of up to 5 undergraduate students has less than 48 hours to provide the best insight gleaned from a large, complex data set — and communicate that insight to an audience of their peers and the DataFest judges. DataFest 2015 will be April 24th through the 26th, beginning at 6pm on Friday the 24th.

Data Science/Statistics/R @Google

Fri, 27 Feb 2015 11:50:48 -0800

This meetup will be hosted by Google and we’ll have Peter Lipman and Pete Meyer talk about the data science/statistics projects they have been working on at Google.

Human Evaluation Comparisons: Common Framework with Applications

We designed a study to determine whether being on device influenced a quality evaluator’s perception of ads quality. Here, we present a common framework, developed into an internal R package, that was used to analyze the study.

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.

Yihui Xie presents HTML Widgets

Fri, 13 Feb 2015 10:35:31 -0800

The Shiny framework, by RStudio, is growing in popularity as a tool that makes it easy for R developers to leverage the web as a deployment platform for sharing their analysis. However, since the very earliest release of Shiny, the development group has been bombarded with the same series questions over and over again, questions like “Does it work with d3.js?” and “Can I use JavaScript in Shiny?”

Joe Cheng presents Shiny

Fri, 13 Feb 2015 10:25:38 -0800

The Shiny framework is a web application framework for the R programming language, designed by Joe Cheng, to help R programmers turn their analysis into interactive web applications. Fortunately, it does without requiring that the R programmer know HTML, CSS or JavaScript - greatly widening the possible audience of users. At the January DataScience.LA R Meetup, we were fortunate to have Joe Cheng, Software Engineer at RStudio and original architect of Shiny present an introduction to Shiny.

PyDSLA - Blaze: Connecting analysts to big computation

Sat, 07 Feb 2015 15:35:01 -0800

Join PyDSLA for a talk by Matthew Rocklin about the high level APIs being developed for Python for expressive data manipulation and analysis.

Summary:

Pandas and NumPy serve us well. They give interactive high-level access to efficient low-level code. As datasets grow larger than memory this low-level code becomes obsolete but the high-level interface remains valuable. Blaze connects that high-level interface to a number of other computational systems like SQL, Mongo, or Spark, extending the scope of interactive analysis. In this talk we go over common use cases and Blaze as well as a bit of its internal design.

Re: Sorry for the mix up regarding Data Science candidates

Fri, 06 Feb 2015 11:25:56 -0800

This was originally posted on the author’s LinkedIn

You will find lots of advice on how to get a job as a data scientist. I want to add some signal to the noise.

I happen to be hiring for a Data Scientist or two as of this writing, and was thinking of doing a blog post on offering advice for those looking. But then I thought of something better and more insightful: It is a cut-and-paste of an email that I sent to our Director of Talent Acquisition last week when he asked me, “Can you share with me what you’re looking for in regards to your Data Scientist candidates?”

DataScience LA Meetup Summary: Data Science @ DataPop

Mon, 26 Jan 2015 10:41:08 -0800

Ben Zamanzadeh - Data Science @ Datapop - LA Data Science meetup - January 2015 · Speaker Deck

On January 13th, DataScience.LA held its first Data Science meetup of 2015, hosting a speaker from a local semantic advertising startup, DataPop. Their CTO, Dr. Ben Zamanzadeh, gave an in-depth presentation (linked above) of his perspective on e-commerce and advertising, which focused on extracting semantic meaning and DataPop’s marketing knowledge graph. He began his talk by discussing the evolution of e-commerce and moved quickly to semantics, making some very strong statements regarding DataPop’s architectural choices. For example, even though they provide a semantic graph, they are based on Redshift and do not use graph databases - which was quite the surprise to us here at DataScience.LA! At one point, Dr. Zamanzadeh even commented,

Announcing First LA Data Warehouse, Business Intelligence and Analytics Meetup!

Thu, 22 Jan 2015 20:26:41 -0800

We are pleased to announce the first meetup of the LA Data Warehouse, Business Intelligence and Analytics meetup group! On February 9th, we have 4 speakers scheduled to give an engaging overview along with a few of their insights into the field.

Secure your Shiny apps (against SQL injection)

Mon, 12 Jan 2015 09:25:12 -0800

Shiny takes inputs from UI elements and sends them to the server, where the application can access them as R variables. While Shiny has security measures in place, as in any typical web application, it remains the developer’s responsibility to sanitize the inputs before using them. For example, Shiny has no way to protect you if you are using an input in a SQL query such as select ... from ... where field = 'input'. Someone manipulating the websocket communication can craft a specially-formatted input that can force the database to execute a query that it is not supposed to do, termed an SQL injection. This might give an attacker access to private data or the ability to do other nefarious things, and it is a common security issue.

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?

Meetup: DataVis with Plotly on December 16th

Fri, 12 Dec 2014 10:35:30 -0800

Plotly is a web-based platform for making graphs and analyzing data. Plotly’s APIs and web app let teams work together online to make 2D, 3D, and streaming graphs using R, Python, MATLAB, Julia, SAS, and and Excel, all together, all in one platform. The goal is to support technical and non-technical collaboration at all levels. Plotly translates ggplot2, matplotlib, and MATLAB plots into interactive, web-based, shareable plots.

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!

dplyr - some more reflections

Thu, 04 Dec 2014 09:30:00 -0800

Yesterday, I published a post here on DataScience.LA with a simple/basic benchmark for dplyr. It was the main part of a short talk at our last Los Angeles R meetup. Below, I continue my thoughts with some reflections on dplyr’s API and philosophy, AKA the other part of my talk.

dplyr and a very basic benchmark

Wed, 03 Dec 2014 09:35:05 -0800

Earlier this month I gave a short talk about dplyr at the Los Angeles R meetup. It was one of the 5 talks about the useR! 2014 conference, and you can see the summaries and slides from the other 4 talks here.

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!

Hadley Wickham’s dplyr tutorial at useR! 2014, Part 2

Mon, 03 Nov 2014 10:00:28 -0800

Part 2 of the dplyr tutorial is now available! Hadley will be covering Grouped Mutate/Filter, Joins, Do, and Databases. (You can find Part 1 of the tutorial here.)

For our viewers who have just discovered the wonders of R, keep in mind that this tutorial was filmed in early July of 2014. As R packages naturally update over time (well, depending on the programmers!), please make sure to check Hadley’s Githubfor the latest changes to the dplyr package.

LA R Meetup - November 11: Highlights from the useR! 2014 conference - Part 2

Wed, 29 Oct 2014 09:16:21 -0700

At this meetup, five more LA R meetup members will present short talks of what they have learned at useR! 2014. This is the 2nd round of talks about useR! since we had twice as many volunteers to speak as we could accommodate in one meetup (indeed, useR! was that awesome). The first round of talks at our September meetup is summarized here (with slides).

Going (a bit more) public

Mon, 27 Oct 2014 09:48:53 -0700

I ♥ data. But I also ♥ privacy. Even though I have been running a few data meetups for the past few years, I have managed to keep myself out of public eye - even in my professional life. I must admit, I’m giving a bit away in an recent interview with Data Science Weekly. They had excellent questions to pose, and I’d like to take a moment here to summarize two main points from the interview: my thoughts on when and how much time it took me to became a data scientist and how I feel about the hype in the field today. If you’d like, you can read the full interview here.

OpenIntro.org on why introductory textbooks are so expensive

Mon, 20 Oct 2014 11:14:57 -0700

David Diez works on the OpenIntro team, which develops open-source textbooks, statistical software labs, and other course resources for intro-level statistics. This video above is the talk he gave at useR! 2014 entitled “Textbooks fail where Software Succeeds” in which he outlines the challenges unique to open-source textbooks, the state of OpenIntro, and how people can get involved. Below, he discusses these challenges further and offers his thoughts around their solutions.

JJ Allaire, the useR! 2014 interview

Wed, 15 Oct 2014 10:00:42 -0700

JJ Allaire has courageously bet, time and time again, on the cutting edge of technologies. In doing so, he has not only benefited from the fertile rains but has often times himself been the rainmaker. He has, to a great degree, democratized development on the web by bringing the power to create interactive web applications to a wider audience than those select few who had access to both a shell account and Unix knowledge. ColdFusion was a platform by the web and for the web, and its adoption revolutionized programming ‘for the web’ as its own worthy pursuit. This story also resolutely validates the idea that by creating tools for developers at the beginning of a time of revolutionary change in technology, one could not only validate the tools but also help stage the unfolding narrative as one with many participants.

Hadley Wickham's dplyr tutorial at useR! 2014, Part 1

Mon, 13 Oct 2014 10:02:53 -0700

Hadley Wickham (perhaps you’ve heard of his work) presented a 2 hour workshop on dplyr at this year’s useR! conference at UCLA. This tutorial was definitely a highlight of the week-long conference for me, and working on this tutorial video has also made me very appreciative of how versatile the dplyr package can be. It clearly is the chef’s knife of data science tools.

Hadley Wickham presents dplyr at useR! 2014

Mon, 13 Oct 2014 10:00:21 -0700

Hadley Wickham is hard at work, releasing packages which leverage the expressive power of R to keep easy things intuitive, and to make hard things possible. Having already developed a number of packages which address other steps in the data processing and analysis pipeline, he chose to address the process by which R developers ingest, manipulate, and transform data. This package, dplyr, is a narrowly focused library, designed to provide 90% of the functionality required in most data munging tasks. It may not solve every possible problem in data munging, but dplyr’s built in primitives will reduce your need to drop to “base R” for most common tasks. It does so by leveraging the magritr package’s “pipe” operator, and is inspired by dataflow programming constructs. It is fast, it is powerful, and it has been the single largest productivity boost that many of us here at DataScience.LA have adopted in 2014. In this video, Hadley provides a very quick introduction to the dplyr package, describing his philosophy behind the package’s design while providing a number of illustrative examples as to the flexibility, expressiveness, and power of this tool.

Python Data Science Meetup: Data Science with Python

Wed, 08 Oct 2014 09:37:41 -0700

I’m pleased to announce the first event of our new Python Data Science LA meetup, started by two of us here at DataScience.LA (our very own Szilard and Eduardo).

We’ll have 5 speakers at this event, covering various aspects of the Python Data Science ecosystem (NumPy, SciPy, pandas, matplotlib, scikit-learn, NetworkX, NLTK, IPython notebook, etc.)

A Conversation with Joe Cheng at useR! 2014

Mon, 06 Oct 2014 10:18:56 -0700

Joe Cheng is a software engineer. Unfortunately the term gets thrown around pretty lightly, so I’d like to make sure that I convey the appropriate amount of gravitas when I refer to him as such. Joe Cheng is a *great* Software Engineer. He has worked for some of the largest technology companies on the planet, building cutting edge tools which empowered the web browser as a full blown platform instead of a historical curiosity. Joe is one of those software engineers who saw a future with the web as a platform for modern software, then worked hard to make it a reality. When the time came to build the next generation of data science IDEs, an IDE targeting the web as a platform, RStudio called Joe.

Packrat presentation at useR! 2014

Fri, 26 Sep 2014 11:25:44 -0700

There comes a time in a software toolchain’s lifecycle where the focus shifts from developer productivity and preference to the ability to deploy solutions in production which coexist gracefully in the enterprise. Configuration management and dependency management are two of the first points of friction in this transition, as the free-wheeling methods preferred by developers are at odds with the structured methods and isolation desired by administrators, who serve as gatekeepers to enterprise deployment. R is currently at this very point in the transition, in the midst of transformation from a language and environment used primarily in academic research to its growing place in the enterprise. At useR! 2014 there were a series of presentations on how dependency management can be addressed within the R ecosystem, ranging from “provider” side options (e.g. freezing CRAN and private repositories) to the client side options, where a project maintains its own dependencies as part of its codebase.

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.

Dirk Eddelbuettel, the useR! 2014 Interview

Mon, 22 Sep 2014 10:35:04 -0700

First things first, Dirk Eddelbuettel was recently named ordinary. This seems contradictory, since Dirk is a known HPC expert, an organizer of the R in Finance conference, the creator of Rcpp, and a Debian contributor. These are only a few of the many accolades bestowed upon Dirk without even a hint of puffery. And yet, Dirk Eddelbuettel is considered ordinary. What makes Dirk ordinary? It should be mentioned that to the Vienna-based nonprofit that provides R’s leadership, the R Foundation for Statistical Computing, ‘ordinary’ means something quite different to the lay person. Ordinary members provide guidance and direction for the R Project for Statistical Computing. It’s hard to imagine someone more qualified than Dirk for this task.

Dirk Eddelbuettel on open source in finance

Mon, 22 Sep 2014 10:34:28 -0700

The industry as a whole has been a very large net importer of open source technologies.

Interactive Visualizations from R using rCharts

Thu, 18 Sep 2014 14:26:20 -0700

At useR! 2014 Ramnath Vaidyanathan gave a tutorial and a presentation on one of his current project, rCharts. This R package allows you to create, customize and share interactive visualizations straight from R. rCharts leverages several existing javascript visualization libraries such as polychart, MorrisJS, NVD3, xCharts, HighCharts, leaflet, and Rickshaw, providing R users a fantastic array of new features and visualization styles.

Paying for statistics grad school (AKA the $$)

Wed, 17 Sep 2014 10:01:37 -0700

(This is my third post on statistics graduate school. You can read the first, on coursework, the difference between a Masters and a PhD, and proving your worth here, and the second, on advancing to candidacy, writing the dissertation, and having fun in grad school, here.)

Interview with Romain Francois at useR! 2014

Mon, 15 Sep 2014 22:17:41 -0700

At the useR! 2014 conference, without a doubt one of the overriding themes was R’s history, legacy, and future as an interface into the “best of the best” algorithms which were available. Romain Francois’ package, Rcpp11, is very much the spirit and the letter of this ethos and telos of R as an interface. This package is a reimplementation of Dirk Eddelbuettel’s Rcpp package with an emphasis on C++ 11’s features. Whether it’s an R user hitting performance limitations, or someone trying to integrate a C/C++ library into their analysis, Romain’s tools are there to help.

Romain Francois

Mon, 15 Sep 2014 18:55:27 -0700

If I could meet young me I would punch him in the face.

Martin Maechler Invited Talk at useR! 2014 - Good Practices in R Programming

Thu, 11 Sep 2014 10:38:11 -0700

Martin Maechler is a member of R-Core. This distinction puts him in the very apex of understanding of the R programming language and environment. He is a professor of statistics with wide and varied interests and a leading expert in statistical computing environments. He is also a prolific programmer, evidenced by his maintenance of the officially recommended cluster package for unsupervised clustering and analytics, the lead developer of Emacs Speaks Statistics, and co-maintainer of the Matrix package… plus too many other contributions to list them all here.

Martin Maechler on Programming

Thu, 11 Sep 2014 10:37:49 -0700

Your R ‘programs’ should become works of art.

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.

Heather Turner, the useR! 2014 Interview

Mon, 08 Sep 2014 23:58:02 -0700

Heather Turner is a biostatistician and Associate Fellow at the University of Warwick, as well as a consultant. She is a former editor of the R Journal, was a local organizer for the useR! 2011 conference, and holds a wealth of information about the field of data science, the state of the technology, and the wider community.

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.

Shiny: R made interactive @ useR! 2014

Thu, 04 Sep 2014 11:13:51 -0700

At useR! 2014, one of the most anticipated presentations was Joe Cheng’s Shiny: R made interactive. It was but one of the of a fantastic series of talks by RStudio representatives; check out Winston Chang’s ggvis: Interactive graphics in R for another great talk. Shiny begins with your R code and ends with the customer’s view: an interactive, browser-accessible application. This enables you to place a great deal of power into your users’ hands to customize their view and provides a variety of new and innovative ways to interact with the data, such as by tuning parameters and focusing on sub-populations. Essentially, Shiny functions by allowing you to “wire up” up a reactive application, greatly speeding up the development of a data and analysis based web app.

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.

The Importance of 'Janitorial Work' in Research

Tue, 02 Sep 2014 11:47:29 -0700

As someone who has spent a significant amount of time ‘data wrangling’ outside the field of data science, I found this recent New York Times article both highly interesting and nothing new - I’ve seen the same issue discussed in a variety of fields with similar automation solutions presented each time. Granted, there are fields where this janitorial drudgery is absolutely necessary, such as molecular biology. As a scientist working on researching mRNA expression you certainly have to keep RNase-free conditions, meaning you have to ‘clean up’ your bench in a more detailed way than you’d ever imagine before you can run your first PCR cycle. This includes specific protocols for washing your dishes several times with special water before you even start because if you don’t, you’re not likely to get any bands in your electrophoresis gel and you’ll lose several days (or weeks!) of work.

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?

Yihui Xie, the useR! 2014 Interview

Mon, 01 Sep 2014 11:00:48 -0700

In science consensus is irrelevant. What is relevant is reproducible results. The greatest scientists in history are great precisely because they broke with the consensus.

– Michael Crichton

Yihui Xie intended to make it easier to do his homework, but instead found himself tackling one of the greatest problems in modern science: the reproducibility of results.

Yihui Xie on the birth of knitR

Mon, 01 Sep 2014 09:05:34 -0700

I wanted to do my homework more conveniently

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.)

Matt Dowle's data.table Talk from useR! 2014

Thu, 28 Aug 2014 11:04:36 -0700

Matt Dowle holds a wealth of knowledge and is worth listening to for a variety of reasons. He has worked for some of the world’s largest financial organizations, holding positions at the focal point between money and data. He has been programming in R for well over a decade. He has lived the open source mantra, by not only consuming open source software but also releasing his own community contributions to CRAN. On top of those accomplishments, he’s also quite funny and spins a heck of a good yarn.

Part 2 of Statistics Grad school - What is it really like?

Wed, 27 Aug 2014 10:02:02 -0700

(Note: This is my second installment on grad school, you can read the first part on coursework, the difference between a Master’s and a Ph.D., and proving your worth, here.)

Advancement to candidacy
#

After finishing coursework, the next step in our PhD program is to “advance to candidacy.” This is the formal process of proposing your dissertation research. Our department has a diverse set of research interests, both theoretical and applied, so dissertations can take a variety of forms. This is yet another difference between Master’s and PhD projects– typically, Master’s projects are more directed by faculty members and PhD projects are chosen primarily by the students themselves. Either way, the process begins with a conversation between the student and his/her advisor.

David Smith, the useR! 2014 Interview

Mon, 25 Aug 2014 10:00:32 -0700

“At one point, the only source for daily R news was David Smith’s blog.” – Szilard Pafka

David Smith is an integral part of the R community. His background in computational statistics goes back to the early 90s . David has worked at Revolution R, one of the leading R companies, for nearly a decade. His name has been included in lists such as:

David Smith

Mon, 25 Aug 2014 09:59:44 -0700

R is really cool and people that use it can do a lot…

R in Production - Panel Discussion - LA R meetup @ useR! 2014

Thu, 21 Aug 2014 14:27:50 -0700

(Please click here if the video above doesn’t load.)

The video above is from an event at useR! 2014 consisting of Yasmin Lucero’s presentation plus an incredible panel discussion, which is included here. The title of the panel was “R’s Place in the Production Environment”, and was moderated by Tareef Kawaf, President at RStudio. Five high-profile members of the R community were on this panel, spanning the gamut of the enterprise:

R in Production - Opening Talk by Yasmin Lucero - LA R meetup @ useR! 2014

Thu, 21 Aug 2014 13:54:56 -0700

(Please click here if the video above doesn’t load.)

During useR! 2014, the LA R Meetup (run by Szilard Pafka) organized an event focused on R’s status in industry. This event took the form of a presentation by Yasmin Lucero, Senior Statistician at AOL-Gravity, followed by a panel discussion. The video above captures the presentation, where Yasmin provides an excellent overview of the current status of R in the enterprise and breaks down the taxonomy of solutions for which R is commonly chosen as a development platform. From batch reporting and dashboards for internal deployment to full blown public facing analytics data products, R has certainly found a home in business. Yasmin’s talk concludes with specific, operational aspects of managing R in the enterprise, such as library and dependency management. Enjoy the video and don’t forget to tune in for Part 2 of this session where the panel discussed “R’s Place in the Production Environment.”

Introduction to Data Science for High School Students

Wed, 20 Aug 2014 10:02:52 -0700

Another exciting development in data science coming from our department at UCLA is a high school class called Introduction to Data Science (IDS). This project has been made possible by a National Science Foundation grant to support Mobilize, for which Rob Gould (mentioned in my previous post on DataFest) is the Principal Investigator.

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.

Announcing the DSLA Podcast!

Mon, 18 Aug 2014 11:36:47 -0700

You’ve asked and we’ve listened. The audio content from our DataScience.LA interviews will now be released as a podcast! Check us out on iTunes or point your podcast app to here!

We’ve already released the audio from the userR! conference interviews of Hadley Wickham, Max Kuhn, and Tal Galili on iTunes and hope to release upcoming interviews in podcast form shortly after our video blog posts. Thanks for being a part of the DataScience.LA community and we look forward to hearing more from you!

Tal Galili

Mon, 18 Aug 2014 10:32:01 -0700

What I can say is that I am joyful at looking at what people are doing!

A Conversation with Tal Galili at useR! 2014

Mon, 18 Aug 2014 10:31:16 -0700

“One can acquire everything in solitude except character.”

― Stendhal

The Interview
#

Tal Galili is, in many ways, a central spoke of the R community. Both gregarious and thoughtful, he has grown his website R-bloggers into the definitive aggregation of the R community’s voice through his genuine, passion-driven intensity. Tal had a simple desire as a young programmer - to learn more about his chosen tools - and looked to the internet to find other voices like his. When googling for “R blogs”, Tal found numerous blogs about pirates, but only a handful about R. This interview details how Tal started R-bloggers and decided to challenge the status quo, as well as giving us a peek behind the curtain for his new projects.

LA R meetup - September 4: Highlights from the useR! 2014 Conference

Thu, 14 Aug 2014 13:46:12 -0700

Five LA R meetup members will present short talks of what they learned at useR! 2014
(plus we have 5 more volunteers for the following meetup!)

1. Timothy J. Triche, Jr., PhD
Fellow, Jane Anne Nohl Division of Hematology, USC/Norris Comprehensive Cancer Center

Winston Chang's "Interactive Graphics with ggvis" at useR! 2014

Thu, 14 Aug 2014 10:15:15 -0700

Winston Chang’s “Interactive Graphics with ggvis” talk at useR! 2014 was in many ways a watershed moment for the the data science community, as the future directions of rich data visualization for analytics, communication, and the web were revealed. In this talk, Winston introduces the viewer to ggvis, lays out the fundamental architecture, and provides an overview of reactive architectures.

DataVis LA Meetup - August 26: Eyeo Festival Highlights

Tue, 12 Aug 2014 09:58:18 -0700

Eyeo festival highlights

Amelia McNamara

From the festival website, “Eyeo assembles an incredible set of creative coders, data designers and artists, and attendees – expect enthralling talks, unique workshops and interactions with open source instigators and super fascinating practitioners.” I’ll talk about some of the most inspiring talks and projects I saw at Eyeo this year, including work by Santiago Ortiz, Lillian Schwartz, Cod.Act, Kate Crawford and Nicholas Felton. Most of this work can be described best by the term “art,” so come expecting provocations, not prototypes. To learn more about the festival, visit eyeofestival.com

Max Kuhn on caret

Mon, 11 Aug 2014 10:02:40 -0700

We’ve made a lot of the same mistakes, and we want to protect people from doing them

A Conversation with Max Kuhn - The useR! 2014 Interview

Mon, 11 Aug 2014 10:02:16 -0700

The Interview
#

In the video above, Max provides some amazing insights into the why and how of caret, an R package he created. He also discusses his book on Applied Predictive Modeling which he co-authored with Kjell Johnson, including details on how he set out to write the book he wished he would have had. As a special bonus, Max also describes Quinlan’s C5.0, an alternate “forest of decision trees” algorithm, the secrets of which were hidden behind commercial licensing for years - and which has recently been ported and made available to the R ecosystem. Whether you are a beginner just getting your feet wet with R and predictive modeling, or a seasoned data scientist, this interview has something for everyone.

John Chambers useR! 2014 Keynote

Wed, 06 Aug 2014 18:14:19 -0700

At useR! 2014, John Chambers was generous enough to provide us with insight into the very early stages of user-centric interactive data exploration. He explains, step by step, how his insight to provide an interface into algorithms, putting the user first has placed us on the fruitful path which analysts, statisticians, and data scientists enjoy to this day. In his talk, John Chambers also does a fantastic job of highlighting a number of active projects, new and vibrant in the R ecosystem, which are helping to continue this legacy of “a software interface into the best algorithms.” The future is bright, and new and dynamic ideas are building off these thoughtful, well measured, solid foundations of the past.

A Conversation with Hadley Wickham - the useR! 2014 interview

Wed, 06 Aug 2014 18:13:38 -0700

Hadley Wickham is famous. He’s not Kardashian famous, but walking around useR! and seeing the community’s reaction to him, there’s no question, he’s ‘R famous’. If you have the good fortune to see his talks, tutorials, or sessions in person, you owe it to yourself to do so. He projects depth and wisdom with a booming voice, which combines with a hard-won confidence brought about by years of honing his craft and developing his expertise. He takes as much time as is needed to answer questions, listens to every single bit of feedback and succeeds in making you feel that what you say indeed matters. Hadley Wickham has poise. It’s also quite obvious, if you watch him for long enough, that this fame suits him like an itchy sweater made by a loving grandparent. It brings warmth and it comes from a place of love, but it’s always a little uncomfortable regardless of how well it may fit.

Hadley on R

Wed, 06 Aug 2014 18:12:57 -0700

I think it’s interesting that R is an extremely fixable language.

Hadley Wickham

Making Maps with a Punchline

Wed, 06 Aug 2014 18:09:43 -0700

I’ve had a lifelong fascination with maps, and working with R definitely enables my map habit. Perhaps there’s something about being an immigrant that made me particularly introspective, since from a very early age I was aware that I was “from” one place on the map and now lived at this “other place.”

Blogging Accelerator for DataScience.LA

Wed, 06 Aug 2014 18:08:58 -0700

By now I’m sure you’ve checked out our blog and have wondered, ‘How can I contribute my ideas and share my projects with the community?’ There are two ways to contribute content to DataScience.LA, either as a regular contributor or through our Blogging Accelerator. Our vision is to help unleash you, members of the community, as effective data science communicators while establishing Los Angeles as one of the leading places for data thinking, data journalism, and data science. (Ready to write a blog post right now? Send us an email at info@datascience.la to get started as a contributor.)

Statistics grad school-- what is it really like?

Wed, 06 Aug 2014 18:06:58 -0700

As a PhD graduate student in statistics here at UCLA, I’d like to talk a bit about our graduate program to give you an idea of what to expect if you’re interested in UCLA or statistics graduate school in general. First, it’s important to note that UCLA does not have a specific “data science” degree program at this point; the department offers a PhD and a Master’s degree, both simply labeled “statistics.” Some statistics departments have made the decision to change their name to “data science,” but that is often just a marketing decision. The UCLA department is pretty forward-thinking and has a focus on computation, so it’s fairly “data science”-y without having that exact name.

Data science goes to college with DataFest

Wed, 06 Aug 2014 17:28:33 -0700

Below is the first of several exciting data science developments for the younger generation, happening right here in Los Angeles. This project is unique because it engages undergraduates in real data analysis, something that happens quite rarely in a classroom. If projects like this catch on as a new trend, we’re going to have some real competition for our jobs in a few years!

Meetup Groups: DataVis LA

Mon, 04 Aug 2014 18:49:08 -0700

Data visualization helps us understand data. The DataVis LA meetup discusses the many aspects of data visualization: exploratory data analysis, presentation graphics, infographics, design, art, etc., and brings together a diverse community of data scientists, statisticians, software developers, BI specialists, business analysts, journalists, designers and artists with the goal of learning more about the art and science of data visualization from each other.

Meetup Groups: LA Machine Learning

Mon, 04 Aug 2014 18:39:27 -0700

The LA Machine Learning Meetup began with events discussing a large variety of machine learning topics such as classification, clustering, neural networks, graphical algorithms, information retrieval, search, game theory, computational learning theory, reinforcement learning, collaborative filtering etc. More recently, a Data Science Track was launched with the focus on business applications and the entire process of data mining (e.g. business understanding, data collection, exploratory data analysis, data transformations, feature engineering, modeling, model validation, deployment, communication of results). While in fact Machine Learning is a part of Data Science and not the other way around, rather than starting a new meetup group, Data Science is featured within the already existing Machine Learning meetup.

useR! 2014 Conference Videos and Release Plan

Mon, 04 Aug 2014 18:11:48 -0700

Many of us here in the DataScience.LA Team were also involved in organizing the useR! 2014 conference, the annual get-together of the wonderful R community, which brought over 700 R users and developers from all over the world to Los Angeles this past July.

Meetup Groups: LA R Meetup

Mon, 04 Aug 2014 16:19:58 -0700

R is an open source software and programming language for data analysis, statistical modeling, visualization and data mining among other things. Recently, as the term “data science” has emerged to describe collectively these tasks, R has also become the primary tool of choice for data scientists. The LA R meetup began before this data science boom, back in 2009, by bringing together local users of R and featuring talks with world-class speakers while creating fruitful networking opportunities for its members. In retrospect, this group served as the very first data science meetup in Los Angeles and has significantly contributed to the development of our local data science community.

Welcome to Data Science LA!

Sat, 02 Aug 2014 21:12:52 -0700

Welcome! We are launching DataScience.LA to provide a home for all the content (slides, code, videos etc.) for several meetups in LA (Data Science/Machine Learning, R, DataVis etc.) and to engage the Los Angeles data science community in an open, active discussion about best practices and new developments in the field.

Posts on DataScience.LA

Goodbye DataScience.LA, goodbye California!

Overview of H2O's GBM implementation

CatBoost: Distributed Training, Uncertainty Estimation and Other News

Recent Developments in LightGBM

2 talks: GBM Benchmark In-Depth | The State of XGBoost

SatRday LA - R Conference Announcement

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

R Meetup with Hadley Wickham

Meetup: Machine Learning in Production with Szilard Pafka

Meetup: Stream processing with R in AWS

Meetup: No-Bullshit Data Science

PyDSLA: PyMC3 and jobs.py

Meetup: Machine Learning at Google

Machine Learning at Google #

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

PyDSLA: Talks from the LA Data Science Community

DSSTNE: A New Deep Learning Framework for Large Sparse Datasets

Update: This event has ended. Slides are available here. #

Update: Video recording by Carl Mullins: #

XGBoost workshop and meetup talk with Tianqi Chen

XGBoost: meetup announcement

Deep Learning @Whisper

PyDSLA: The Nitty Gritty of Advanced Analytics Using Apache Spark in Python

What has Kaggle learned from 2 million machine learning models?

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

LA Data Warehouse, Business Intelligence and Analytics - Jihoon Son

Big RAM is eating big data - Size of datasets used for analytics

Meetup Summary - Winning Data Science Competitions

R and Impala: it's better to KISS than using Java

Winning Data Science Competitions

LA City College Forms Data Science Team with City of LA

Announcing Meetup! Lior Abraham of Inter|Ana

PyDSLA Video: Carlos Guestrin, CEO of DATO

Benchmarking Random Forest Implementations

PyDSLA: Dato CEO, Carlos Guestrin, Presents Dato

Data Science @ Whisper

ASA DataFest 2015 at UCLA

What is DataFest? #

Data Science/Statistics/R @Google

DW/BI/Analytics Meetup Summary: Inaugural Event

Yihui Xie presents HTML Widgets

Joe Cheng presents Shiny

PyDSLA - Blaze: Connecting analysts to big computation

Re: Sorry for the mix up regarding Data Science candidates

DataScience LA Meetup Summary: Data Science @ DataPop

Announcing First LA Data Warehouse, Business Intelligence and Analytics Meetup!

Secure your Shiny apps (against SQL injection)

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

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

Meetup: DataVis with Plotly on December 16th

Photos from our LA User Groups First Annual Holiday Party

dplyr - some more reflections

dplyr and a very basic benchmark

Data Science Meetup - January 13: Data Science @ Datapop

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

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

Python Data Science Meetup Summary: Data Science with Python

Hadley Wickham’s dplyr tutorial at useR! 2014, Part 2

LA R Meetup - November 11: Highlights from the useR! 2014 conference - Part 2

Going (a bit more) public

OpenIntro.org on why introductory textbooks are so expensive

JJ Allaire, the useR! 2014 interview

Hadley Wickham's dplyr tutorial at useR! 2014, Part 1

Hadley Wickham presents dplyr at useR! 2014

Python Data Science Meetup: Data Science with Python

A Conversation with Joe Cheng at useR! 2014

Packrat presentation at useR! 2014

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

The case for Apache Spark

Dirk Eddelbuettel, the useR! 2014 Interview

Dirk Eddelbuettel on open source in finance

Interactive Visualizations from R using rCharts

Paying for statistics grad school (AKA the $$)

Interview with Romain Francois at useR! 2014

Romain Francois

Martin Maechler Invited Talk at useR! 2014 - Good Practices in R Programming

Martin Maechler on Programming

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

Heather Turner, the useR! 2014 Interview

Machine Learning at Google
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Update: This event has ended. Slides are available here.
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Update: Video recording by Carl Mullins:
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What is DataFest?
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Advancement to candidacy
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The Interview
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The Interview
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