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Interview Tobias Verbeke Open Analytics #rstats #startups

Here is an interview with Tobias Verbeke, Managing Director of Open Analytics (http://www.openanalytics.eu/). Open Analytics is doing cutting edge work with R in the enterprise software space.
Ajay- Describe your career journey including your involvement with Open Source and R. What things enticed you to try R?

Tobias- I discovered the free software foundation while still at university and spent wonderful evenings configuring my GNU/Linux system and reading RMS essays. For the statistics classes proprietary software was proposed and that was obviously not an option, so I started tackling all problems using R which was at the time (around 2000) still an underdog together with pspp (a command-line SPSS clone) and xlispstat. From that moment on, I decided that R was the hammer and all problems to be solved were nails ;-) In my early career I worked as a statistician / data miner for a general consulting company which gave me the opportunity to bring R into Fortune 500 companies and learn what was needed to support its use in an enterprise context. In 2008 I founded Open Analytics to turn these lessons into practice and we started building tools to support the data analysis process using R. The first big project was Architect, which started as an eclipse-based R IDE, but more and more evolves into an IDE for data science more generally. In parallel we started working on infrastructure to automate R-based analyses and to plug R (and therefore statistical logic) into larger business processes and soon we had a tool suite to cover the needs of industry.

Ajay- What is RSB all about- what needs does it satisfy- who can use it ?

Tobias- RSB stands for the R Service Bus and is communication middleware and a work manager for R jobs. It allows to trigger and receive results from R jobs using a plethora of protocols such as RESTful web services, e-mail protocols, sftp, folder polling etc. The idea is to enable people to push a button (or software to make a request) and have them receive automated R based analysis results or reports for their data.

Ajay- What is your vision and what have been the challenges and learning so far in the project

Tobias- RSB started when automating toxicological analyses in pharmaceutical industry in collaboration with Philippe Lamote. Together with David Dossot, an exceptional software architect in Vancouver, we decided to cleanly separate concerns, namely to separate the integration layer (RSB) from the statistical layer (R) and, likewise, from the application layer. As a result any arbitrary R code can be run via RSB and any client application can interact with RSB as long as it can talk one of the many supported protocols. This fundamental design principle makes us different from alternative solutions where statistical logic and integration logic are always somehow interwoven, which results in maintenance and integration headaches. One of the challenges has been to keep focus on the core task of automating statistical analyses and not deviating into features that would turn RSB into a tool for interaction with an R session, which deserves an entirely different approach. Rservice-diagram

Ajay- Computing seems to be moving to an heterogeneous cloud , server and desktop model. What do you think about the R and Cloud Computing- current and future

Tobias- From a freedom perspective, cloud computing and the SaaS model is often a step backwards, but in our own practice we obviously follow our customers’ needs and offer RSB hosting from our data centers as well. Also, our other products e.g. the R IDE Architect are ready for the cloud and use on servers via Architect Server. As far as R itself concerns in relation to cloud computing, I foresee its use to increase. At Open Analytics we see an increasing demand for R-based statistical engines that power web applications living in the cloud.

Ajay- You recently released RSB version 6. What are all the new features. What is the planned roadmap going forward

Tobias- RSB 6.0 is all about large-scale production environments and strong security. It kicked off on a project where RSB was responsible for spitting 8500 predictions per second. Such large-scale production deployments of RSB motivated the development of a series of features. First of all RSB was made lightning fast: we achieved a full round trip from REST call to prediction in 7 ms on the mentioned use case. In order to allow for high throughput, RSB also gained a synchronous API (RSB 5.0 had an asynchronous API only). Another new feature is the availability of client-side connection pooling to the pool manager of R processes that are read to serve RSB. Besides speed, this type of production environments also need monitoring and resilience in case of issues. For the monitoring, we made sure that everything is in place for monitoring and remotely querying not only the RSB application itself, but also the pool of R processes managed by RServi.

 

(Note from Ajay- RJ is an open source library providing tools and interfaces to integrate R in Java applications. RJ project also provides a pool for R engines, easy to setup and manage by a web-interface or JMX. One or multiple client can borrow the R engines (called RServi)  see http://www.walware.de/it/rj/ and https://github.com/walware/rj-servi)
Also, we now allow to define node validation strategies to be able to check that R nodes are still functioning properly. If not, the nodes are killed and new nodes are started and added to the pool. In terms of security, we are now able to cover a very wide spectrum of authentication and authorization. We have machines up and running using openid, basic http authentication, LDAP, SSL client certificates etc. to serve everyone from the individual user who is happy with openid authentication for his RSB app to large investment banks who have very strong security requirements. The next step is to provide tighter integration with Architect, such that people can release new RSB applications without leaving the IDE.

Ajay- How does the startup ecosystem in Europe compare with say the SF Bay Area, What are some of the good things and not so great things

Tobias- I do not feel qualified to answer such a question, since I founded a single company in Antwerp, Belgium. That being said, Belgium is great! :-)

Ajay- How can we popularize STEM education using MooCs , training etc

Tobias- Free software. Free as in beer and as in free speech!

Ajay- Describe the open source ecosystem in general and R ecosystem in  particular for Europe. How does it compare with other locations in your opinion

Tobias- Open source is probably a global ecosystem and crosses oceans very easily. Dries Buytaert started off Drupal in Belgium and now operates from the US interacting with a global community. From a business perspective, there are as many open source models as there are open source companies. I noticed that the major US R companies (Revolution Analytics and RStudio) cherished the open source philosophy initially, but drifted both into models combining open source and proprietary components. At Open Analytics, there are only open source products and enterprise customers have access to exactly the same functionality as a student may have in a developing country. That being said, I don’t believe this is a matter of geography, but has to do more with the origins and different strategies of the companies.

Ajay- What do you do for work life balance and de stressing when not shipping  code.

Tobias- In a previous life the athletics track helped keeping hands off the keyboard. Currently, my children find very effective ways to achieve similar goals

About-

OpenAnalytics is a consulting company specialized in statistical computing using open technologies. You can read more on it at http://www.openanalytics.eu

Using Windows Azure Machine Learning as a service with R #rstats

A Brief Tutorial I wrote by playing with the software at manage.windowsazure.com

Interview Antonio Piccolboni Big Data Analytics RHadoop #rstats

Here is an interview with Antonio Piccolboni , a consultant on big data analytics who has most notably worked on the RHadoop project for Revolution Analytics. Here he tells us about writing better code, and the projects he has been involved with.
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DecisionStats(DS)- Describe your career journey from being a computer science student to one of the principal creators for RHadoop. What motivated you, what challenges did you overcome. What were the turning points.(You have 3500+ citations. What are most of those citations regarding.)

Antonio (AP)- I completed my undergrad in CS in Italy. I liked research and industry didn’t seem so exciting back then, both because of the lack of a local industry and the Microsoft monopoly, so I entered the PhD program.
After a couple of false starts I focused on bioinformatics. I was very fortunate to get involved in an international collaboration and that paved the way for a move to the United States. I wanted to work in the US as an academic, but for a variety of reasons that didn’t work out.
Instead I briefly joined a new proteomics department in a mass spectrometry company, then a research group doing transcriptomics, also in industry, but largely grant-funded. That’s the period when I accumulated most of my citations.
After several years there, I realized that bioinformatics was not offering the opportunities I was hoping for and that I was missing out on great changes that were happening in the computer industry, in particular Hadoop, so after much deliberation I took the plunge and worked first for a web ratings company and then a social network, where I took the role of what is now called a “data scientist”, using the statistical skills that I acquired during the first part of my career. After taking a year off to work on my own idea I became a free lance and Revolution Analytics one of my clients, and I became involved in RHadoop.
As you can see there were several turning points. It seems to me one needs to seek a balance of determination and flexibility, both mental and financial, to explore different options, while trying to make the most of each experience. Also, be at least aware of what happens outside your specialty area. Finally, the mandatory statistical warning: any generalizations from a single career are questionable at best.

 

DS-What are the top five things you have learnt for better research productivity and code output in your distinguished career as a computer scientist.
AP-1. Keep your code short. Conciseness in code seems to correlate with a variety of desirable properties, like testability and maintainability. There are several aspects to it and I have a linkblog about this (asceticprogrammer.info). If I had said “simple”, different people would have understood different things, but when you say “short” it’s clear and actionable, albeit not universally accepted.
2. Test your code. Since proving code correct is unfeasible for the vast majority of projects, development is more like an experimental science, where you assemble programs and then corroborate that they have the desired properties via experiments. Testing can have many forms, but no testing is no option.
3. Many seem to think that programming is an abstract activity somewhere in between mathematics and machines. I think a developer’s constituency are people, be them the millions using a social network or the handful using a specialized API. So I try to understand how people interact with my work, what they try to achieve, what their background is and so forth.
4. Programming is a difficult activity, meaning that failure happens even to the best and brightest. Learning to take risk into account and mitigate it is very important.
5. Programs are dynamic artifacts. For each line of code, one may not only ask if it is correct but for how long, as assumptions shift, or how often it will be executed. For a feature, one could wonder how many will use it, and how many additional lines of code will be necessary to maintain it.
6. Bonus statistical suggestion: check the assumptions. Academic statistics has an emphasis on theorems and optimality, bemoaned already by Tukey over sixty years ago. Theorems are great guides for data analysis, but rely on assumptions being met, and, when they are not, consequences can be unpredictable. When you apply the most straightforward, run of the mill test or estimator, you are responsible for checking the assumptions, or otherwise validating the results. “It looked like a normal distribution” won’t cut it when things go wrong.

 

DS-Describe the RHadoop project- especially the newer plyrmr package. How was the journey to create it.
AP-Hadoop is for data and R is for statistics, to use slogans, so it’s natural to ask the question of how to combine them, and RHadoop is one possible answer.
We selected a few important components of Hadoop and provided an R API. plyrmr is an offshoot of rmr, which is an API to the mapreduce system. While rmr has enjoyed some success, we received feedback that a simplified API would enable even more people to directly access and analyze the data.Again based on feedback we decided to focus on structured data, equivalent to an R data frame. We tried to reduce the role of user-defined functions as parameters to be fed into the API, and when custom functions are needed they are simpler. Grouping and regrouping the data is fundamental to mapreduce. While in rmr the programmer has to process two data structures, one for the data itself and the other describing the grouping, plyrmr uses a very familiar SQL-like “group” function.
Finally, we added a layer of delayed evaluation that allows to perform certain optimizations automatically and encourages reuse by reducing the cost of abstraction. We found enough commonalities with the popular package plyr that we decided to use it as a model, hence the tribute in the name. This lowers the cognitive burden for a typical user.

 

DS-Hue is an example of making interfaces easier for users to use Hadoop. so are sandboxes and video trainings. How can we make it easier to create better interfaces to software like RHadoop et al
AP- It’s always a trade-off between power and ease of use, however I believe that the ability to express analyses in a repeatable and communicable way is fundamental to science and necessary to business and one of the key elements in the success of R. I haven’t seen a point and click GUI that satisfies these requirements yet, albeit it’s not inconceivable. For me, the most fruitful effort is still on languages and APIs. While some people write their own algorithms, the typical data analyst needs a large repertoire of algorithms that can be applied to specific problems. I see a lot of straightforward adaptations of sequential algorithms or parallel algorithms that work at smaller scales, and I think that’s the wrong direction. Extreme data sizes call for algorithms that work within stricter memory, work and communication constraints than before. On the other hand, the abundance of data, at least in some cases, offers the option of using less powerful or efficient statistics. It’s a trade off whose exploration has just started.

 

DS-What do you do to maintain work life balance and manage your time
 
AP- I think becoming a freelancer affords me a flexibility that employed work generally lacks. I can put in more or fewer hours depending on competing priorities and can move them around other needs, like being with family in the morning or going for a bike ride while it’s sunny.  I am not sure I manage my time incredibly well, but I try to keep track of where I spend it at least by broad categories, whether I am billing it to a client or not. “If you can not measure it, you can not improve it”, a quote usually attributed to Lord Kelvin.

 
DS- What do you think is the future of R as an enterprise and research software in terms of computing on mobile, desktop, cloud and how do you see things evolve from here

AP- One of the most interesting things that are happening right now is the development of different R interpreters. A successful language needs at least two viable implementations in my opinion. None of the alternatives is ready for prime time at the moment, but work is ongoing. Some implementations are experimental but demonstrate technological advances that can be then incorporated into the other interpreters. The main challenge is transitioning the language and the community to the world of parallel and distributed programming, which is a hardware-imposed priority. RHadoop is meant to help with that, for the largest data sets. Collaboration and publishing on the web is being addressed by many valuable tools and it looks to me the solutions exist already and it’s more a problem of adoption.  For the enterprise, there are companies offering training, consulting, licensing,  centralized deployments,  database APIs, you name it. It would be interesting to see touch interfaces applied to interactive data visualization, but while there is progress on the latter, touch on desktop is limited to a single platform and R doesn’t run on mobile, so I don’t see it as an imminent development.

 

About-
Antonio Piccolboni is an  experienced data scientist (FlowingdataRadar on this emerging role) with industrial and academic backgrounds currently working as an independent consultant on big data analytics. His clients include Revolution Analytics. His other recent work is on social network analysis (hi5) and web analytics (Quantcast). You can contact him via http://piccolboni.info/about.html or his LinkedIn profile

Interview Vivian Zhang co-founder SupStat

Here is an interview with Vivian Zhang, CTO and co-founder Supstat which is an interesting startup in the R ecosystem. In this interview Vivian talks of the startup journey, helping spread R in China and New York City, and managing Meetups, conferences and training business with balance and excellence.

download

DecisionStats- (DS) Describe the story behind creation of SupStat Inc and the journey so far along with milestones and turning points. What is your vision for SupStat and what do you want it to accomplish and how.

Vivian Zhang(VZ) -

Creation:

SupStat was born in 2012 out of the collaboration of 60+ individuals(Statistician, Computer Engineers, Mathematician,Professors, graduate students and talend Data genius)who met through a well-known non-profit organization in China, Capital of Statistics. The SupStat team met through various collaborations on R packages and analytic work. In 2013, SupStat became involved in the New York City data science community through hosting the NYC Open Data Meetup, and soon began offering formal courses through the NYC Data Science Academy. SupStat offers consulting services in the areas of R development, data visualization, and big data solutions. We are experienced with many technologies and languages including R, Python, Hadoop, Spark, Node.js, etc. Courses offered include Data Science with R (Beginner, Intermediate), Data Science with Python (Beginner, Intermediate), and Hadoop (Beginner, Intermediate), as well as many targeted courses on R packages and data visualization tools.

Allen and I, the two co-founders, have been passionate about Data Mining since a young age (we talked about it back in 1997). With industry experience as Chief Data scientist/Senior Analyst and a spirit of entrepreneurship, we started the firm by gathering all the top R/Hadoop/D3.js programmers we knew.

Milestones of SupStat:

June 2012, Established in Beijing

July 2012,  Offered R intensive Bootcamp in Beijing to 50+ college students

June 2013, Established in NYC

Nov 2013,  Launched our NYC training brand: NYC Data Science Academy

Jan 2014,  Became premium partner of Revolution Analytics in China

Feb 2014,  Became training and reseller partner of RStudio in US and China

April 2014, Became Exclusive reseller partner of Transwarp in US

                Started to offer R built-in and professional services for Hadoop/Spark

May 2014, Organized and sponsored R conference in Beijing

                NYC Open Data Meetup had 1800+ members in one year

Jun 2014, Sponsored UCLA R conference (Vivian was panelist for female R programmer talk.)

The major turning point was in November, 2013, when we decided to start our NYC office and launched the NYC Data Science Academy.

Our Mission:

We are committed to helping our clients make distinctive, lasting and substantial improvement in their performance, sales, clients and employee satisfaction by fully utilizing data. We are a value-driven firm. For us this means:

  • Solving the hardest problems

  • Utilizing state-of-the-art data science to help our clients succeed

  • Applying a structured problem-solving approach where all options are considered, researched, and analyzed carefully before recommendations are made

Our Vision: Be a firm that attracts exceptional people to meet the growing demand for data analysis and visualization.

Future goals:

With engaged clients, we want to share the excitement, unlimited potential and methodologies of using data to create business value. We want to be the go-to firm when people think of getting data analytic training, consulting, and big data products.

With top data scientists, we want to be the home for those who want different data challenges all the time. We promote their open data/demo work in the community and  expand the impact of the analytic tools and methodologies they developed. We connect the best ones to build the strongest team.

With new college students and young professionals, we want to help them succeed and be able to handle real world problems right away though our short-term, intensive training programs and internship programs. Through our rich experience, we have tailored our training program to solve some of the critical problems people face in their workplace.

Through our partnerships we want to spread the best technologies between the US and China. We want to close the gap and bring solutions and offerings to clients we serve. We are at the frontline to pick what is the best product for our clients.

We are glad we have the opportunity to do what we love and are good at, and will continue to enjoy doing it with a growing and energetic team.

DS -What is the state of open source statistical software in China? How have you contributed to R in China and how do you see the future of open source evolve there?

VZ- People love R and embrace R.  In May 2014, We helped to organize and sponsor the R conference in Beijing, with 1400 attendants. See our blog post for more details: http://www.r-bloggers.com/the-7th-china-r-conference-in-beijing/

We have helped organize two R conferences in China in the past year, Spring in Beijing and Winter in Shanghai. And we will do a Summer R conference in Guangzhou this year. That’s three R conferences in one year!

DS- Describe some of your work with your partners in helping sell and support R in China and USA

VZ- Revolution Analytics and RStudio are very respected in the R community. We are glad to work and learn from them through collaboration.

With Revolution, we provide services to do proof-of-concept and professional services including analytics and visualization. We also sell Revolution products and licenses in China. With RStudio, we sell Rstudio Server Pro and Shiny and promote training programs around those products in NYC. We plan to sell these products in China starting this summer. With Transwarp, we offer the best R analytic and paralleling experience through the Hadoop/Spark ecosystem.

DS- You have done many free workshops in multiple cities. What has been the response so far.

VZ- Let us first talk about what happened in NYC.

I went to a few meetups before I started my own meetup group. Most of the presentation/talks were awesome but they were not delivered and constructed in a way that attendants could learn and apply the technology right away. Most of the time, those events didn’t offer source code or technical details in the slides.

When I started my own group, my goal was “whatever cool stuff we showed you, we will teach you how to do it.” The majority of the events were designed as hands-on workshops while we hosted a few high profile speakers’ talks from time to time (including the chief data science scientist for the Obama Campaign).

My workshops cover a wide range of topics, including R, Python, Hadoop, D3.js, data processing, Tableau, location data query, open data, etc. People are super excited and keep saying “oh wow oh wow”, “never thought that I could do it”, ”it is pretty cool.” Soon our attendants started giving back to the group by teaching their skills and fun projects, offering free conference room, and sponsoring pizzas.

We are glad we have built a community of sharing experience and passion for data science. And I think this is a very unique thing we can do in NYC (due to the fact everything is close to half-hour subway distance). We host events 2-3 times per week and have attracted 1900 members in one year.

In other cities such as Shanghai and Beijing, we do free workshops for college students and scholars every month. We promise to go to the colleges as far as within 24 hours distance by train from Beijing.  Through partnerships with Capital of Statistics and DataUnion, we hosted entrepreneur sharing events with devoted speeches and lighting talks.

In NYC, we typically see 15 to 150 people per event. U.S. sponsors have included McKinsey & Company, Thoughtworks, and others. Our Beijing monthly tech event sees over 500 attendees and gains attraction from event co-hosts including Baiyu, Youku and others.

DS- What are some interesting projects of Supstat that you would like to showcase.

VZ- Let me start with one interesting open data project on Citibike data done by our team. The blog post, slides and meetup videos can be found at http://nycdatascience.com/meetup/nyc-open-data-project-ii-my-citibike/

Citibike provides a public bike service. There are many bike stations in NYC. People want to take a bike from a station with at least one available bike. And when they get to the destination, they want to return their bike to a station with at least one available slot. Our goal was to predict where to rent and where to return Citibikes. We showed the complete workflow including data scraping, cleaning, manipulation, processing, modeling, and making algorithms into a product.

We built a program to scrape data and save it to our database automatically. Using this data we utilized models from time series theory and machine learning to predict bike numbers in all the stations. Based on the models, we built a website for this citibike system. This application helps users of citibike arrange their trips better. We also showed a few tricks such as how to set up cron job on Linux, Windows and Mac machines, and how to get around RAM limitations on servers with PostgreSQL.

We’ve done other projects in China using R to solve problems ranging from Telecommunications data caching to Cardiovascular disease prevention. Each of these projects has required a unique combination of statistical knowledge and data science tools, with R being the backbone of the solution. The commercial cases can be found at our website: http://www.supstat.com/consulting/

About-

SupStat is a statistical consulting company specialized in statistical computing and graphics using state-of-the-art technologies.

VIVIAN S. ZHANG Co-founder & CTO, NYC, Beijing and Shanghai Office

Vivian is a data scientist who has been devoted to the analytics industry and the development and use of data technologies for several years. She obtained expertise in data analysis and data management using various statistical analytical tools and programming languages as a Senior Analyst and Biostatistician at Memorial Sloan-Kettering Cancer Center and Scientific Programmer at Brown University. She is the co-founder SupStat, NYC Data Science Academy, NYC Open-Data meetup. She likes to portray herself as a programmer, data-holic, visualization evangelist.

You can read more about SupStat at http://www.supstat.com/team/

Learning and Teaching R #rstats

a brief ppt I made for the New Delhi Meetup Group http://www.meetup.com/New-Delhi-R-UseR-Group/

Updates at Statace : Early access to make your own R in the browser GUI #rstats

The guys at Statace released major updates- I am particularly excited for the ability to create a custom GUI box for your own analysis or for sharing with consulting clients or students.

What does that mean? Basically they are making it a bit like R Commander Extensions- so if you have a package or analysis you would rather do visually (than code) – you can create a GUI module for it. The modular extension is quite cool in my opinion, but further proof will be in how well designed the pudding is.

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Public sharing of results
Now you can share your analysis results for the world to see (example). Just click Share in the results pane.

Google Drive integration
We added integration with Google Drive. This makes collaboration and synchronization of large files even easier. Don’t forget we also support Dropbox. Just click the Connect to menu in the file manager.

Plots zoom and SVG export
Now you can open plots in a separate window that supports zoom in and zoom out. From it, you can also export to the SVG format which is ideal for printing. Just click the lens icon next to any plot.

Point-and-click PCA + data transformation without R knowledge
You can now carry out a PCA by just pointing and clicking though Analysis > Dimensional Analysis > Principal Components Analysis. We also added the Data menu which allows you to filter and sort datasets without any knowledge of R.

(Secret) Build your own visual dialog box to run R code
Do you have colleagues who don’t know R but need to use functionality you developed? Do you do consulting and want your customers to be able to run your models with point-and-click? Do you want to share a piece of R code with the world in an easy-to-use way?
StatAce now allows you to easily create a custom graphical interface for your R code. The process is entirely visual (no coding) and is what we use to build our own Data & Analysis menus (e.g. the bivariate correlation and linear regression dialog boxes). We are testing the functionality with a limited number of users, and their feedback has been great. Drop us a line at predict@statace.com to request early access.

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Screenshot 2014-04-15 15.34.25

 

 

 

The Amazing R-Fiddle truly brings #rstats to the browser

Datamind.com whom I interact with on and off, and also the masterminds behind http://www.rdocumentation.org/

have finally created their platform for interactive and gamified R learning on the web. Take a look- it does like slightly better than Codeacademy’s interface doesnt it. The platform is called http://www.r-fiddle.org/#/

More power to R for Cloud Computing!

Screenshot from 2013-11-21 21:37:25

Now if they could only collobrate with other players like Quandl, BigML and even StatAce for a even cooler suggestion. Even Revolution Analytics and RStudio who have very expensive training modules should be able to use this for self paced online learning courses!

 

Quote- A software of beauty is a joy forever – Keats

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