Tag: data

Interview David Katz ,Dataspora /David Katz Consulting

Here is an interview with David Katz ,founder of David Katz Consulting (http://www.davidkatzconsulting.com/) and an analyst at the noted firm http://dataspora.com/. He is a featured speaker at Predictive Analytics World  http://www.predictiveanalyticsworld.com/sanfrancisco/2011/speakers.php#katz)

Ajay-  Describe your background working with analytics . How can we make analytics and science more attractive career options for young students

David- I had an interest in math from an early age, spurred by reading lots of science fiction with mathematicians and scientists in leading roles. I was fortunate to be at Harry and David (Fruit of the Month Club) when they were in the forefront of applying multivariate statistics to the challenge of targeting catalogs and other snail-mail offerings. Later I had the opportunity to expand these techniques to the retail sphere with Williams-Sonoma, who grew their retail business with the support of their catalog mailings. Since they had several catalog titles and product lines, cross-selling presented additional analytic challenges, and with the growth of the internet there was still another channel to consider, with its own dynamics.

After helping to found Abacus Direct Marketing, I became an independent consultant, which provided a lot of variety in applying statistics and data mining in a variety of settings from health care to telecom to credit marketing and education.

Students should be exposed to the many roles that analytics plays in modern life, and to the excitement of finding meaningful and useful patterns in the vast profusion of data that is now available.

Ajay-  Describe your most challenging project in 3 decades of experience in this field.

David- Hard to choose just one, but the educational field has been particularly interesting. Partnering with Olympic Behavior Labs, we’ve developed systems to help identify students who are most at-risk for dropping out of school to help target interventions that could prevent dropout and promote success.

Ajay- What do you think are the top 5 trends in analytics for 2011.

David- Big Data, Privacy concerns, quick response to consumer needs, integration of testing and analysis into business processes, social networking data.

Ajay- Do you think techniques like RFM and LTV are adequately utilized by organization. How can they be propagated further.

David- Organizations vary amazingly in how sophisticated or unsophisticated the are in analytics. A key factor in success as a consultant is to understand where each client is on this continuum and how well that serves their needs.

Ajay- What are the various software you have worked for in this field- and name your favorite per category.

David- I started out using COBOL (that dates me!) then concentrated on SAS for many years. More recently R is my favorite because of its coverage, currency and programming model, and it’s debugging capabilities.

Ajay- Independent consulting can be a strenuous job. What do you do to unwind?

David- Cycling, yoga, meditation, hiking and guitar.

Biography-

David Katz, Senior Analyst, Dataspora, and President, David Katz Consulting.

David Katz has been in the forefront of applying statistical models and database technology to marketing problems since 1980. He holds a Master’s Degree in Mathematics from the University of California, Berkeley. He is one of the founders of Abacus Direct Marketing and was previously the Director of Database Development for Williams-Sonoma.

He is the founder and President of David Katz Consulting, specializing in sophisticated statistical services for a variety of applications, with a special focus on the Direct Marketing Industry. David Katz has an extensive background that includes experience in all aspects of direct marketing from data mining, to strategy, to test design and implementation. In addition, he consults on a variety of data mining and statistical applications from public health to collections analysis. He has partnered with consulting firms such as Ernst and Young, Prediction Impact, and most recently on this project with Dataspora.

For more on David’s Session in Predictive Analytics World, San Fransisco on (http://www.predictiveanalyticsworld.com/sanfrancisco/2011/agenda.php#day2-16a)

Room: Salon 5 & 6
4:45pm – 5:05pm

Track 2: Social Data and Telecom 
Case Study: Major North American Telecom
Social Networking Data for Churn Analysis

A North American Telecom found that it had a window into social contacts – who has been calling whom on its network. This data proved to be predictive of churn. Using SQL, and GAM in R, we explored how to use this data to improve the identification of likely churners. We will present many dimensions of the lessons learned on this engagement.

Speaker: David Katz, Senior Analyst, Dataspora, and President, David Katz Consulting

Exhibit Hours
Monday, March 14th:10:00am to 7:30pm

Tuesday, March 15th:9:45am to 4:30pm

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All about Data

Once Upon a Time in Mumbaai
Image via Wikipedia

Just some data thoughts for explaining the world of data better to others. –

Ajay

Note- Presentation

I made this in Nov ,2008- is it still relevant? Maybe.

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Revolution R Enterprise 4.2

Revo R gets more and more yum yum-

he following new features:

  • Direct import of SAS data sets into the native, efficient XDF file format
  • Direct import of fixed-format text data files into XDF file format
  • New commands to read subsets of rows and variables from XDF files in memory;
  • Many enhancements to the R Productivity Environment (RPE) for Windows
  • Expanded and updated user documentation
  • Added support on Linux for the big-data statistics package RevoScaleR
  • Added support on Windows for Web Services integration of predictive analytics with RevoDeployR.

Revolution R Enterprise 4.2 is available immediately for 64-bit Red Hat Enterprise Linux systems and both 32-bit and 64-bit Windows systems. Pricing starts at $1,000 per single-user workstation

And its free for academic licenses- so come on guys it is worth  atleast one download, and test.

http://www.revolutionanalytics.com/downloads/free-academic.php

 

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SAS to R Challenge: Unique benchmarking

Flag of Town of Cary
Image via Wikipedia

An interesting announcemnet from Revolution Analytics promises to convert your legacy code in SAS language not only cheaper but faster. It’ s a very very interesting challenge and I wonder how SAS users ,corporates, customers as well as the Institute itself reacts

http://www.revolutionanalytics.com/sas-challenge/

Take the SAS to R Challenge

Are you paying for expensive software licenses and hardware to run time-consuming statistical analyses on big data sets?

If you’re doing linear regressions, logistic regressions, predictions, or multivariate crosstabulations* there’s something you should know: Revolution Analytics can get the same results for a substantially lower cost and faster than SAS®.

For a limited time only, Revolution Analytics invites you take the SAS to R Challenge. Let us prove that we can deliver on our promise of replicating your results in R, faster and cheaper than SAS.

Take the challenge

Here’s how it works:

Fill out the short form below, and one of our conversion experts will contact you to discuss the SAS code you want to convert. If we think Revolution R Enterprise can get the same results faster than SAS, we’ll convert your code to R free of charge. Our goal is to demonstrate that Revolution R Enterprise will produce the same results in less time. There’s no obligation, but if you choose to convert, we guarantee that your license cost for Revolution R Enterprise will be less than half what you’re currently paying for the equivalent SAS software.**

It’s that simple.

We’ll show you that you don’t need expensive hardware and software to do high quality statistical analysis of big data. And we’ll show that you don’t need to tie up your computing resources with long running operations. With Revolution R Enterprise, you can run analyses on commodity hardware using Linux or Windows, scale to terabyte-class data problems and do it at processing speeds you would never have thought possible.

Sign up now, and we will be in touch shortly.

Take the challenge

 

—————————-

SAS is a registered trademark of the SAS Institute, Cary, NC, in the US and other countries.

*Additional statistical algorithms are being rapidly added to Revolution R Enterprise. Custom development services are also available.

**Revolution Analytics retains the right to determine eligibility for this offer. Offer available until March 31, 2011.

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GrapheR

GNU General Public License
Image via Wikipedia

GrapherR

GrapheR is a Graphical User Interface created for simple graphs.

Depends: R (>= 2.10.0), tcltk, mgcv
Description: GrapheR is a multiplatform user interface for drawing highly customizable graphs in R. It aims to be a valuable help to quickly draw publishable graphs without any knowledge of R commands. Six kinds of graphs are available: histogram, box-and-whisker plot, bar plot, pie chart, curve and scatter plot.
License: GPL-2
LazyLoad: yes
Packaged: 2011-01-24 17:47:17 UTC; Maxime
Repository: CRAN
Date/Publication: 2011-01-24 18:41:47

More information about GrapheR at CRAN
Path: /cran/new | permanent link

Advantages of using GrapheR

  • It is bi-lingual (English and French) and can import in text and csv files
  • The intention is for even non users of R, to make the simple types of Graphs.
  • The user interface is quite cleanly designed. It is thus aimed as a data visualization GUI, but for a more basic level than Deducer.
  • Easy to rename axis ,graph titles as well use sliders for changing line thickness and color

Disadvantages of using GrapheR

  • Lack of documentation or help. Especially tips on mouseover of some options should be done.
  • Some of the terms like absicca or ordinate axis may not be easily understood by a business user.
  • Default values of color are quite plain (black font on white background).
  • Can flood terminal with lots of repetitive warnings (although use of warnings() function limits it to top 50)
  • Some of axis names can be auto suggested based on which variable s being chosen for that axis.
  • Package name GrapheR refers to a graphical calculator in Mac OS – this can hinder search engine results

Using GrapheR

  • Data Input -Data Input can be customized for CSV and Text files.
  • GrapheR gives information on loaded variables (numeric versus Factors)
  • It asks you to choose the type of Graph 
  • It then asks for usual Graph Inputs (see below). Note colors can be customized (partial window). Also number of graphs per Window can be easily customized 
  • Graph is ready for publication



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Comparing Bit Torrent Downloaders

Tux, as originally drawn by Larry Ewing
Image via Wikipedia

I personally like UTorrent on Windows and KTorrent on Linux.

While no experts on this, anything that gets the data down faster while maximizing my pipes efficiency.

I also like Torrenting than  any of the sudo-apt get method of downloading software or the zip unzip,tar untar, install/make file

Torrenting is a simpler way of sharing applications but sadly not used much by the stats computing community to share downloads.

Also I think any dashboard or visualization should be sorted (but not alphabetically but numerically/categorically)

SORT THE DASHBOARD —-KEEP IT SORTED

So I am partially recreating after sorting the data viz from http://en.wikipedia.org/wiki/Comparison_of_BitTorrent_clients

BitTorrent client Magnet URI Super-seeding Embedded tracker UPnP[81] NAT Port Mapping Protocol NAT traversal[82] DHT[83] Peer exchange Encryption UDP tracker LPD
µTorrent Yes Yes[95] Yes[96] Yes[97] Yes Yes[98] Yes[99] Yes[85] Yes[100] Yes Yes[101]
BitSpirit [11] Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes No
BitTorrent 6 Yes Yes Yes Yes Yes Yes Yes Yes[85] Yes Yes Yes
OneSwarm Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes No
qBittorrent Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
SoMud Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Vuze (formerly Azureus) Yes Yes Yes Yes Yes Yes[102] Yes[87] Yes Yes Yes No
BitComet Yes Yes Separate download Yes Yes Yes Yes Yes Yes Yes No
Tixati [43] Yes Yes No Yes No No Yes Yes Yes Yes Partial
Aria2 Yes No Yes No No No Yes Yes Yes Yes Yes
Tribler Yes No Yes Yes Yes No Yes Yes Yes No No
Bitflu Yes No No No No No Yes Yes No Yes No
Deluge Yes No No Yes Yes Yes Yes Yes Yes Yes Yes
Flush Yes No No Yes Yes No Yes Yes No No Yes
KTorrent Yes No No Yes Yes Yes Yes Yes Yes Yes Partial
Shareaza Yes No No Yes Yes No Yes[93] Yes No No No
Transmission Yes No No Yes Yes Yes Yes Yes[94] Yes No Yes
LimeWire Partial Yes Yes Yes Yes No Yes Yes Yes Yes No
BitTyrant No Yes[citation needed] Yes Yes Yes Yes[86] Yes[87] Yes Yes No No
BitTornado No Yes Yes[84] Yes No No No No Yes No No
Torrent Swapper No Yes Yes[84] Yes No No No Yes No No No
Localhost No Yes Yes Yes No Yes Yes [89] No No No No
Meerkat Bittorrent Client No Yes No Yes Yes Yes Yes No Yes No No
rTorrent No Yes No No No No Yes Yes Yes Yes No[92]
TorrentFlux No Yes No Yes No No No No Yes No No
TorrentVolve No Partial [76] No Partial[76] Partial [76] Partial [76] Partial[76] Partial [76] Partial [76] Partial [76] No
Opera No No Yes[90] No No No No Yes[91] No No No
BitTorrent 5 / Mainline No No Yes[84] Yes Yes No Yes Yes Yes No No
ABC No No Yes Yes No No No No No No No
Blog Torrent No No Yes No No No No No No No No
MLDonkey No No Yes Yes Yes No No No No Yes No
Tomato Torrent No No Yes No No No Yes No No No No
Acquisition No No No No Yes No No No No No No
Arctic Torrent No No No No No No No Yes No No No
BitLet No No No Yes No No No No No No No
BitLord No No No Yes No Yes No Yes No Yes No
BitThief No No No No No No No No No No No
Bits on Wheels No No No No No No No No No No No
BTG No No No Yes Yes No Yes Yes Yes Yes No
BTPD No No No No No No No No No No No
FlashGet No No No No No No Yes No Yes No No
Folx No No No Yes Yes No Yes Yes No Yes No
Free Download Manager No No No No No No Yes Yes No No No
G3 Torrent No No No No No No No No No No No
Gnome BitTorrent No No No No No No No No No No No
Halite No No No Yes Yes No Yes No Yes No[88] No
QTorrent No No No No No No No No No No No
Rufus No No No No No No No No No No No
SymTorrent No No No N/A N/A N/A No No No No No
Tonido Torrent No No No Yes Yes Yes Yes No No No No
Torium No No No Yes No No Yes No No No No
ZipTorrent No No No Yes Yes No No Yes No No No

 

 

 

 

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Data Visualization: Central Banks

Iron Ore Company of Canada
Image via Wikipedia

Trying to compare the transparency of central banks via the data visualization of two very different central banks.

One is Reserve Bank of India and the other is Federal Reserve Bank of New York

Here are some points-

1) The federal bank gives you a huge clutter of charts to choose from and sometimes gives you very difficult to understand charts.

see http://www.newyorkfed.org/research/global_economy/usecon_charts.html

and http://www.newyorkfed.org/research/directors_charts/us18chart.pdf

us18chart

2) The Reserve bank of India choose Business Objects and gives you a proper drilldown kind  of  graph and tables. ( thats a lot of heavy metal and iron ore China needs from India 😉 😉

Foreign Trade – Export      Time-line: ALL

TIME LINE COUNTRY COMMODITY AMOUNT (US $ MILLION) EXPORT QUANTITY
2010:07 (JUL) – P China IRON ORE (Units: TON) 205.06 1878456
2010:06 (JUN) – P China IRON ORE (Units: TON) 427.68 6808528
2010:05 (MAY) – P China IRON ORE (Units: TON) 550.67 5290450
2010:04 (APR) – P China IRON ORE (Units: TON) 922.46 9931500
2010:03 (MAR) – P China IRON ORE (Units: TON) 829.75 13177672
2010:02 (FEB) – P China IRON ORE (Units: TON) 706.04 10141259
2010:01 (JAN) – P China IRON ORE (Units: TON) 577.13 8498784
2009:12 (DEC) – P China IRON ORE (Units: TON) 545.68 9264544
2009:11 (NOV) – P China IRON ORE (Units: TON) 508.17 9509213
2009:10 (OCT) – P China IRON ORE (Units: TON) 422.6 7691652
2009:09 (SEP) – P China IRON ORE (Units: TON) 278.04 4577943
2009:08 (AUG) – P China IRON ORE (Units: TON) 276.96 4371847
2009:07 (JUL) China IRON ORE (Units: TON) 266.11 4642237
2009:06 (JUN) China IRON ORE (Units: TON) 241.08 4584354

Source : DGCI & S, Ministry of Commerce & Industry, GoI

 

You can see the screenshots of the various visualization tools of the New York Fed Reserve Bank and Indian Reserve Bank- if the US Fed is serious about cutting the debt maybe it should start publishing better visuals

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