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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/

The Education of Larry Page

From naming the algorithm after himself ( PageRank ?) to forsaking his professors at Stanford ( who legally own the rights to many intellectual property), to first learning under Eric Schmidt and then pushing him out on the pretense of a political appointment to never came, to the era of silent cooperation with the US Government, to collecting a lot of data by assessing the risk of litigation (especially mobile), and to push intellectual property rights between open source and patent rights, to massive expensive lobbying and now even sidelining his brother in arms- Larry Page has emerged as the most ruthless combination of business savvy and formidable technological skills since Bill Gates.

He now owns a representative sample of nearly all the data on video (Youtube) , email (Gmail), website analytics ( Google Analytics), search engine (Google.com), advertising clicks ( Adwords and Adsense), a majority of mobile phones (Android).

And he wants more. To collect data from your thermostat. Your glasses. His government will not file an anti trust case because of national security. As an extension of US foreign policy, he will lead protests against Chinese hackers, censorship and even abandon the market than comply with Chinese Law, but he will gladly pay fines and delete links to comply with European Law.

There are ways to make money that are not evil. But they do not teach what is evil or not, at Stanford. Not even to dropouts.

larry-page_230733

Happy July 4th

To all my American friends.

july-fourth-bbq

Informatin Asymmetry is the most evil business

What is information asymmetry?

information asymmetry deals with the study of decisions in transactions where one party has more or better information than the other. This creates an imbalance of power in transactions which can sometimes cause the transactions to go awry, a kind of market failure in the worst case. Examples of this problem are adverse selection,[1] moral hazard, and information monopoly

Most commonly, information asymmetries are studied in the context of principal–agent problems. Information asymmetry causes misinforming and is essential in every communication process

Adverse selection, anti-selection, or negative selection  refers to a market process in which undesired results occur when buyers and sellers have asymmetric information (access to different information); the “bad” products or services are more likely to be selected.

The principal–agent problem or agency dilemma occurs when one person or entity (the “agent“) is able to make decisions that impact, or on behalf of, another person or entity: the “principal“. The dilemma exists because sometimes the agent is motivated to act in his own best interests rather than those of the principal.

Monopolies of knowledge arise when ruling classes maintain their political power through their control of key communications technologies.[3] An example of this occurs in ancient Egypt where a complex writing system conferred a monopoly of knowledge on literate priests and scribes.

 

  1. This especially is true in enterprise software
  2. and online advertising and spam
  3. and commodities across the globe (oil spikes after iraq, oil slumps after heating oil data, climate data, or even releases from strategic reservoirs)
  4. and internet spying which may be for economic espionage or trade negotiations but are justified as looking for terrorists.
  5. and inflation in the developing and poor countries
  6. and lobbying in the developed and rich countries

 

People who enable information asymmetry are corrupted people, misled by their own greed and agent-employees in decisions that run counter to the principles when they founded their corporation.

Do you think information asymmetry is evil? Or do you think we should jump on the bandwagon and play the game. Click those ads, while we share your data with the government!

 

Latest Interview – Rapid Miner CEO Ingo Mierswa

Here is an interview I did with the CEO of Rapid Miner, Ingo Mierswa. Ingo, who is something of a prodigy and genius with multi-lingual capabilities, stellar academic and business record talks on navigating the journey for an open source startup.

http://www.kdnuggets.com/2014/06/interview-ingo-mierswa-rapidminer-analytics-turning-points.html

Popularized by Michael (Monty) Widenius, one of the founders of MySQL and an investor in RapidMiner, business source is a commercial software license model that offers many of the benefits of open source, but with a built-in time delay on users being able to access new versions of our products.

 

Related-

  1. Guide to Data Science Cheat Sheets 2014/05/12
  2. Book Review: Data Just Right 2014/04/03
  3. Exclusive Interview: Richard Socher, founder of etcML, Easy Text Classification Startup 2014/03/31
  4. Trifacta – Tackling Data Wrangling with Automation and Machine Learning 2014/03/17
  5. Paxata automates Data Preparation for Big Data Analytics 2014/03/07
  6. etcML Promises to Make Text Classification Easy  2014/03/05
  7. Wolfram Breakthrough Knowledge-based Programming Language – what it means for Data Science? 2014/03/02

10 for 10 – Packt lowers cost of books for students and researchers alike

The high cost of textbooks and science books is an open scandal. Despite this publishers are barely profitable, and the ecosystem is ripe for disruption.

Packt is one such player. I have reviewed many books for them ( in return I get ebooks and books – some of which I give to my students).

Now they have an intriguing offer.

As you are aware, this month, Packt is celebrating 10 years of success with over 2000 Titles in its Library. To celebrate this huge milestone, we have come up with an exciting opportunity for collaboration which you might be interested in.

Packt is offering all of its eBooks and Videos at just $10 each. This campaign is specifically aimed towards thanking all our customers for their support and opening up our comprehensive range of titles just for $10 each. This promotion covers every title and customers can stock up on as many copies as they like until July 5th. I hope you find this as a great opportunity to explore what’s new and maintain your personal and professional development.

Interested- you can see http://www.packtpub.com/10years

Disclosure- The author was offered 2 free ebooks as part of this campaign on social media. Books is one thing he is willing to blog for ;)

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