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Writing for kdnuggets.com

I have been writing freelance for kdnuggets.com

Its a great learning for me to be a better writer especially in my discipline.

These are a list of articles -interviews are in bold and I will keep updating this list

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

Using R for random number creation from time stamps #rstats

Suppose – let us just suppose- you want to create random numbers that are reproducible , and derived from time stamps

Here is the code in R

> a=as.numeric(Sys.time())
> set.seed(a)
> rnorm(log(a))

Note- you can create a custom function  ( I used  the log) for generating random numbers of the system time too. This creates a random numbered list of pseudo random numbers (since nothing machine driven is purely random in the strict philosophy of the word)


[1]  39621645  99451316 109889294 110275233 278994547   6554596  38654159  68748122   8920823  13293010
[11]  57664241  24533980 174529340 105304151 168006526  39173857  12810354 145341412 241341095  86568818
[21] 105672257

Possible applications- things that need both random numbers (like encryption keys) and time stamps (like events , web or industrial logs or as pseudo random pass codes in Google 2 factor authentication )

Note I used the rnorm function but you could possibly draw the functions also as a random input (rnorm or rcauchy)

Again I would trust my own random ness than one generated by an arm of US Govt (see http://www.nist.gov/itl/csd/ct/nist_beacon.cfm )

Update- Random numbers in R



The currently available RNG kinds are given below. kind is partially matched to this list. The default is "Mersenne-Twister".

The seed, .Random.seed[-1] == r[1:3] is an integer vector of length 3, where each r[i] is in 1:(p[i] - 1), where p is the length 3 vector of primes, p = (30269, 30307, 30323). The Wichmann–Hill generator has a cycle length of 6.9536e12 (= prod(p-1)/4, see Applied Statistics (1984) 33, 123 which corrects the original article).

A multiply-with-carry RNG is used, as recommended by George Marsaglia in his post to the mailing list ‘sci.stat.math’. It has a period of more than 2^60 and has passed all tests (according to Marsaglia). The seed is two integers (all values allowed).

Marsaglia’s famous Super-Duper from the 70′s. This is the original version which does not pass the MTUPLE test of the Diehard battery. It has a period of about 4.6*10^18 for most initial seeds. The seed is two integers (all values allowed for the first seed: the second must be odd).

We use the implementation by Reeds et al. (1982–84).

The two seeds are the Tausworthe and congruence long integers, respectively. A one-to-one mapping to S’s .Random.seed[1:12] is possible but we will not publish one, not least as this generator is not exactly the same as that in recent versions of S-PLUS.

From Matsumoto and Nishimura (1998). A twisted GFSR with period 2^19937 – 1 and equidistribution in 623 consecutive dimensions (over the whole period). The ‘seed’ is a 624-dimensional set of 32-bit integers plus a current position in that set.

A 32-bit integer GFSR using lagged Fibonacci sequences with subtraction. That is, the recurrence used is

X[j] = (X[j-100] – X[j-37]) mod 2^30

and the ‘seed’ is the set of the 100 last numbers (actually recorded as 101 numbers, the last being a cyclic shift of the buffer). The period is around 2^129.

An earlier version from Knuth (1997).

The 2002 version was not backwards compatible with the earlier version: the initialization of the GFSR from the seed was altered. R did not allow you to choose consecutive seeds, the reported ‘weakness’, and already scrambled the seeds.

Initialization of this generator is done in interpreted R code and so takes a short but noticeable time.

A ‘combined multiple-recursive generator’ from L’Ecuyer (1999), each element of which is a feedback multiplicative generator with three integer elements: thus the seed is a (signed) integer vector of length 6. The period is around 2^191.

The 6 elements of the seed are internally regarded as 32-bit unsigned integers. Neither the first three nor the last three should be all zero, and they are limited to less than 4294967087 and 4294944443 respectively.

This is not particularly interesting of itself, but provides the basis for the multiple streams used in package parallel.

Use a user-supplied generator.


Function RNGkind allows user-coded uniform and normal random number generators to be supplied.

6 weeks Data Scientist Online Courses #rstats

Hosting a 6 weekend live online certification course on Business Analytics with R starting June 1 at Edureka.Check www.edureka.in/r-for-analytics for more details. Course has been decided to ensure more open data science than current expensive offerings that are tech rather than business oriented but more support and customization than a MOOC This is because many business customers don’t care if it is lapply or ddapply, or command line or GUI, as long  as they get good ROI on time and money spent in shifting to R from other analytics software.

Screenshot from 2013-05-28 07:16:41



Predictive Analytics World goes to Chicago

Message from our Sponsors and my favorite Analytics conference ( only if I could attend a cool analytics conference nearby in Asia (singapore/turkey?)  -sighs) Even useR wont come to Asia ever?-

This is the number 1 conference for analytics in the world and it is next month in Chicago, USA? So you think you have the best analytics software or product or service. Here is where you can find it out!

It’s time to amp-up your analytics strategy. It’s time to beef up your analytics strategy by attending Predictive Analytics World Chicago, June 10-13, 2013. With over 30 case studies from leading organizations across a spectrum of industries, this is the must-attend event for anyone serious about their analytics strategy.

Here’s what your peers had to say about their experience at PAW:

“Great speakers, interesting content, and great networking. PAW conferences are among my favorite analytic events!”
– Karl Rexer, Ph.D. Rexer Analytics“This vendor neutral conference always gives me tangible ideas I can put to work right away.”
– Greg Hayworth, Humana

“Predictive Analytics World did a great job keeping up with the trends in Predictive Modeling. There were also plenty of opportunities to learn about the most valuable resources available to data scientists.”
– Conor Sontag, Marketing Evolution

“People who are in analytics must join Predictive Analytics World and see the state of the art projects.”
– Burak Buyuktombak, Avea Telecommunication Services (Turkey)

And there is more where that came from.

Who’s attending PAW Chicago 2013?

Here are just a few of the many companies attending:

Whose attending PAW Chicago

And many more!

Registration options for all budgets.

PAW Chicago has a variety of conference pass options available to meet budgets of all sizes.

Learn more about pricing and how to register.

Register Now!

2013 Chicago Sponsors
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Visual Guides to CRISP-DM ,KDD and SEMMA

UPDATED- Here are three great examples of a visualization making a process easy to understand. Please click on the images to read them clearly.

1) It visualizes CRISP-DM and is made by Nicole Leaper (http://exde.wordpress.com/2009/03/13/a-visual-guide-to-crisp-dm-methodology/)


2) KDD -Knowledge Discovery in Databases -visualization by Fayyad whom I have interviewed here at http://www.decisionstats.com/interview-dr-usama-fayyad-founder-open-insights-llc/

and work By Gregory Piatetsky Shapiro interviewed by this website here



3) I am also attaching a visual representation of SEMMA from http://www.dataprix.net/en/blogs/respinosamilla/theory-data-mining



Interview Dr. Ian Fellows Fellstat.com #rstats Deducer

Here is an interview with Dr Ian Fellows, creator of acclaimed packages in R like Deducer and the Founder and President of Fellstat.com
Ajay- Describe your involvement with the Deducer Project, and the various plugins associated with it. What has been the usage and response for Deducer from R Community.
Ian- Deducer is a graphical user interface for data analysis built on R. It sprung out of a disconnect between the toolchain used by myself and the toolchain of the psychologists that I worked with at the University of California, San DIego. They were primarily SPSS user, whereas I liked to use R, especially for anything that was not a standard analysis.
I felt that there was a big gap in the audience that R serves. Not all consumers or producers of statistics can be expected to have the computational background (command-line programming) that R requires. I think it is important to recognize and work with the areas of expertise that statistical users have. I’m not an expert in psychology, and they didn’t expect me to be one. They are not experts in computation, and I don’t think that we should expect them to be in order to be a part of the R toolchain community.ian
This was the impetus behind Deducer, so it is fundamentally designed to be a familiar experience for users coming from an SPSS background and provides a full implementation of the standard methods in statistics, and data manipulation from descriptives to generalized linear models. Additionally, it has an advanced GUI for creating visualizations which has been well received, and won the John Chambers award for statistical software in 2011.
Uptake of the system is difficult to measure as CRAN does not track package downloads, but from what I can tell there has been a steadily increasing user base. The online manual has been accessed by over 75,000 unique users, with over 400,000 page views. There is a small, active group of developers creating add-on packages supporting various sub-diciplines of statistics. There are 8 packages on CRAN extending/using Deducer, and quite a few more on r-forge.
Ajay- Do you see any potential for Deducer as an enterprise software product (like R Studio et al)
Ian- Like R Studio, Deducer is used in enterprise environments but is not specifically geared towards that environment. I do see potential in that realm, but don’t have any particular plan to make an enterprise version of Deducer.
Ajay- Describe your work in Texas Hold’em Poker. Do you see any potential for R for diversifying into the casino analytics – which has hitherto been served exclusively by non open source analytics vendors.
Ian- As a Statistician, I’m very much interested in problems of inference under uncertainty, especially when the problem space is huge. Creating an Artificial Intelligence that can play (heads-up limit) Texas Hold’em Poker at a high level is a perfect example of this. There is uncertainty created by the random drawing of cards, the problem space is 10^{18}, and our opponent can adapt to any strategy that we employ.
While high level chess A.I.s have existed for decades, the first viable program to tackle full scale poker was introduced in 2003 by the incomparable Computer Poker Research group at the University of Alberta. Thus poker represents a significant challenge which can be used as a test bed to break new ground in applied game theory. In 2007 and 2008 I submitted entries to the AAA’s annual computer poker competition, which pits A.I.s from universities across the world against each other. My program, which was based on an approximate game theoretic equilibrium calculated using a co-evolutionary process called fictitious play, came in second behind the Alberta team.
Ajay- Describe your work in social media analytics for R. What potential do you see for Social Network Analysis given the current usage of it in business analytics and business intelligence tools for enterprise.
Ian- My dissertation focused on new model classes for social network analysis (http://arxiv.org/pdf/1208.0121v1.pdf and http://arxiv.org/pdf/1303.1219.pdf). R has a great collection of tools for social network analysis in the statnet suite of packages, which represents the forefront of the literature on the statistical modeling of social networks. I think that if the analytics data is small enough for the models to be fit, these tools can represent a qualitative leap in the understanding and prediction of user behavior.
Most uses of social networks in enterprise analytics that I have seen are limited to descriptive statistics (what is a user’s centrality; what is the degree distribution), and the use of these descriptive statistics as fixed predictors in a model. I believe that this approach is an important first step, but ignores the stochastic nature of the network, and the dynamics of tie formation and dissolution. Realistic modeling of the network can lead to more principled, and more accurate predictions of the quantities that enterprise users care about.
The rub is that the Markov Chain Monte Carlo Maximum Likelihood algorithms used to fit modern generative social network models (such as exponential-family random graph models) do not scale well at all. These models are typically limited to fitting networks with fewer than 50,000 vertices, which is clearly insufficient for most analytics customers who have networks more on the order of 50,000,000.
This problem is not insoluble though. Part of my ongoing research involves scalable algorithms for fitting social network models.
Ajay- You decided to go from your Phd into consulting (www.fellstat.com) . What were some of the options you considered in this career choice.
Ian- I’ve been working in the role of a statistical consultant for the last 7 years, starting as an in-house consultant at UCSD after obtaining my MS. Fellows Statistics has been operating for the last 3 years, though not fulltime until January of this year. As I had already been consulting, it was a natural progression to transition to consulting fulltime once I graduated with my Phd.
This has allowed me to both work on interesting corporate projects, and continue research related to my dissertation via sub-awards from various universities.
Ajay- What does Fellstat.com offer in its consulting practice.
Ian- Fellows Statistics offers personalized analytics services to both corporate and academic clients. We are a boutique company, that can scale from a single statistician to a small team of analysts chosen specifically with the client’s needs in mind. I believe that by being small, we can provide better, close-to-the-ground responsive service to our clients.
As a practice, we live at the intersection of mathematical sophistication, and computational skill, with a hint of UI design thrown into the mix. Corporate clients can expect a diverse range of analytic skills from the development of novel algorithms to the design and presentation of data for a general audience. We’ve worked with Revolution Analytics developing algorithms for their ScaleR product, the Center for Disease Control developing graphical user interfaces set to be deployed for world-wide HIV surveillance, and Prospectus analyzing clinical trial data for retinal surgery. With access to the cutting edge research taking place in the academic community, and the skills to implement them in corporate environments, Fellows Statistics is able to offer clients world-class analytics services.
Ajay- How does big data affect the practice of statistics in business decisions.
Ian- There is a big gap in terms of how the basic practice of statistics is taught in most universities, and the types of analyses that are useful when data sizes become large. Back when I was at UCSD, I remember a researcher there jokingly saying that everything is correlated rho=.2. He was joking, but there is a lot of truth to that statement. As data sizes get larger everything becomes significant if a hypothesis test is done, because the test has the power to detect even trivial relationships.
Ajay- How is the R community including developers coping with the Big Data era? What do you think R can do more for Big Data?
Ian- On the open source side, there has been a lot of movement to improve R’s handling of big data. The bigmemory project and the ff package both serve to extend R’s reach beyond in-memory data structures.  Revolution Analytics also has the ScaleR package, which costs money, but is lightning fast and has an ever growing list of analytic techniques implemented. There are also several packages integrating R with hadoop.
Ajay- Describe your research into data visualization including word cloud and other packages. What do you think of Shiny, D3.Js and online data visualization?
Ian- I recently had the opportunity to delve into d3.js for a client project, and absolutely love it. Combined with Shiny, d3 and R one can very quickly create a web visualization of an R modeling technique. One limitation of d3 is that it doesn’t work well with internet explorer 6-8. Once these browsers finally leave the ecosystem, I expect an explosion of sites using d3.
Ajay- Do you think wordcloud is an overused data visualization type and how can it be refined?
Ian- I would say yes, but not for the reasons you would think. A lot of people criticize word clouds because they convey the same information as a bar chart, but with less specificity. With a bar chart you can actually see the frequency, whereas you only get a relative idea with word clouds based on the size of the word.
I think this is both an absolutely correct statement, and misses the point completely. Visualizations are about communicating with the reader. If your readers are statisticians, then they will happily consume the bar chart, following the bar heights to their point on the y-axis to find the frequencies. A statistician will spend time with a graph, will mull it over, and consider what deeper truths are found there. Statisticians are weird though. Most people care as much about how pretty the graph looks as its content. To communicate to these people (i.e. everyone else) it is appropriate and right to sacrifice statistical specificity to design considerations. After all, if the user stops reading you haven’t conveyed anything.
But back to the question… I would say that they are over used because they represent a very superficial analysis of a text or corpus. The word counts do convey an aspect of a text, but not a very nuanced one. The next step in looking at a corpus of texts would be to ask how are they different and how are they the same. The wordcloud package has the comparison and commonality word clouds, which attempt to extend the basic word cloud to answer these questions (see: http://blog.fellstat.com/?p=101).

Dr. Ian Fellows is a professional statistician based out of the University of California, Los Angeles. His research interests range over many sub-disciplines of statistics. His work in statistical visualization won the prestigious John Chambers Award in 2011, and in 2007-2008 his Texas Hold’em AI programs were ranked second in the world.

Applied data analysis has been a passion for him, and he is accustomed to providing accurate, timely analysis for a wide range of projects, and assisting in the interpretation and communication of statistical results. He can be contacted at info@fellstat.com

Interview Pranay Agrawal Co-Founder Fractal Analytics

Here is an interview with Pranay Agrawal, Executive Vice President- Global Client Development, Fractal Analytics – one of India’s leading analytics services providers and one of the pioneers in analytics services delivery.

Ajay- Describe Fractal Analytics’ journey as a startup to a pioneer in the Predictive Analytics Services industry. What were some of the key turning points in the field of analytics that you have noticed during these times?


Pranay- In 2000, Fractal Analytics started as a pure-play analytics services company in India with a focus on financial services. Five years later, we spread our operation to the United States and opened new verticals. Today, we have the widest global footprint among analytics providers and have experience handling data and deep understanding of consumer behavior in over 150 counties. We have matured from an analytics service organization to a productized analytics services firm, specializing in consumer goods, retail, financial services, insurance and technology verticals.
We are on the fore-front of a massive inflection point with Big Data Analytics at the center. We have witnessed the transformation of analytics within our clients from a cost center to the most critical division that drives competitive advantage.  Advances are quickly converging in computer science, artificial intelligence, machine learning and game theory, changing the way how analytics is consumed by B2B and B2C companies. Companies that use analytics well are poised to excel in innovation, customer engagement and business performance.

Ajay- What are analytical tools that you use at Fractal Analytics? Are there any trends in analytical software usage that you have observed?

Pranay- We are tools agnostic to serve our clients using whatever platforms they need to ensure they can quickly and effectively operationalize the results we deliver.  We use R, SAS, SPSS, SpotFire, Tableau, Xcelsius, Webfocus, Microstrategy and Qlikview. We are seeing an increase in adoption of open source platform such as R, and specialize tools for dashboard like Tableau/Qlikview, plus an entire spectrum of emerging tools to process manage and extract information from Big Data that support Hadoop and NoSQL data structures

Ajay- What are Fractal Analytics plans for Big Data Analytics?

Pranay- We see our clients being overwhelmed by the increasing complexity of the data. While they are all excited by the possibilities of Big Data, on-the-ground struggle continues to realize its full potential. The analytics paradigm is changing in the context of Big Data. Our solutions focus on how to make it super-simple for our clients combined with analytics sophistication possible with Big Data.
Let’s take our Customer Genomics solution for retailers as an example. Retailers are collecting information about Shopper behaviors through every transaction. Retailers want to transform their business to make it more customer-centric but do not know how to go about it. Our Customer Genomics solution uses advanced machine learning algorithm to label every shopper across more than 80 different dimensions. Retailers use these to identify which products it should deep-discount depending on what price-sensitive shoppers buy. They are transforming the way they plan their assortment, planogram and targeted promotions armed with this intelligence.

We are also building harmonization engines using Concordia to enable real-time update of Customer Genomics based on every direct, social, or shopping transaction. This will further bridge the gap between marketing actions and consumer behavior to drive loyalty, market share and profitability.

Ajay- What are some of the key things that differentiate Fractal Analytics from the rest of the industry? How are you different?

Pranay- We are one of the pioneer pure-play analytics firm with over a decade of experience consulting with Fortune 500 companies. What clients most appreciate about working with us includes:

  • Experience managing structured and unstructured Big Data (volume, variety) with a deep understanding of consumer behavior in more than 150 counties
  • Advanced analytics leveraging supervised machine-learning platforms
  • Proprietary products for example: Concordia for data harmonization, Customer Genomics for consumer insights and personalized marketing, Pincer for pricing optimization, Eavesdrop for social media listening,  Medley for assortment optimization in retail industry and Known Value Item for retail stores
  • Deep industry expertise enables us to leverage cross-industry knowledge to solve a wide range of marketing problems
  • Lowest attrition rates in the industry and very selective hiring process makes us a great place to work

Ajay- What are some of the initiatives that you have taken to ensure employee satisfaction and happiness?

Pranay- We believe happy employees create happy customers. We are building a great place to work by taking a personal interest in grooming people. Our people are highly engaged as evidenced by 33% new hire referrals and the highest Glassdoor ratings in our industry.
We recognize the accomplishments and contributions made through many programs such as:

  1. FractElite – where peers nominate and defend the best of us
  2. Recognition board – where anyone can write a visible thank you
  3. Value cards – where anyone can acknowledge great role model behavior in one or more values
  4. Townhall – a quarterly all hands where we announce anniversaries and FractElite awards, with an open forum to ask questions
  5. Employee engagement surveys – to measure and report out on satisfaction programs
  6. Open access to managers and leadership team – to ensure we understand and appreciate each person’s unique goals and ambitions, coach for high performance, and laud their success

Ajay- How happy are Fractal Analytics customers quantitatively?  What is your retention rate- and what plans do you have for 2013?

Pranay- As consultants, delivering value with great service is critical to our growth, which has nearly doubled in the last year. Most of our clients have been with us for over five years and we are typically considered a strategic partner.
We conduct client satisfaction surveys during and after each project to measure our performance and identify opportunities to serve our clients better. In 2013, we will continue partnering with our clients to define additional process improvements from applying best practice in engagement management to building more advanced analytics and automated services to put high-impact decisions into our clients’ hands faster.


Pranay Agrawal -Pranay co-founded Fractal Analytics in 2000 and heads client engagement worldwide. He has a MBA from India Institute of Management (IIM) Ahmedabad, Bachelors in Accounting from Bangalore University, and Certified Financial Risk Manager from GARP. He is is also available online on http://www.linkedin.com/in/pranayfractal

Fractal Analytics is a provider of predictive analytics and decision sciences to financial services, insurance, consumer goods, retail, technology, pharma and telecommunication industries. Fractal Analytics helps companies compete on analytics and in understanding, predicting and influencing consumer behavior. Over 20 fortune 500 financial services, consumer packaged goods, retail and insurance companies partner with Fractal to make better data driven decisions and institutionalize analytics inside their organizations.

Fractal sets up analytical centers of excellence for its clients to tackle tough big data challenges, improve decision management, help understand, predict & influence consumer behavior, increase marketing effectiveness, reduce risk and optimize business results.



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