Tag: why

Libreoffice 3.3 released

tree of light

What does LibreOffice give you?

http://www.libreoffice.org/features/

WRITER is the word processor inside LibreOffice. Use it for everything, from dashing off a quick letter to producing an entire book with tables of contents, embedded illustrations, bibliographies and diagrams. The while-you-type auto-completion, auto-formatting and automatic spelling checking make difficult tasks easy (but are easy to disable if you prefer). Writer is powerful enough to tackle desktop publishing tasks such as creating multi-column newsletters and brochures. The only limit is your imagination.

CALC tames your numbers and helps with difficult decisions when you’re weighing the alternatives. Analyze your data with Calc and then use it to present your final output. Charts and analysis tools help bring transparency to your conclusions. A fully-integrated help system makes easier work of entering complex formulas. Add data from external databases such as SQL or Oracle, then sort and filter them to produce statistical analyses. Use the graphing functions to display large number of 2D and 3D graphics from 13 categories, including line, area, bar, pie, X-Y, and net – with the dozens of variations available, you’re sure to find one that suits your project.

IMPRESS is the fastest and easiest way to create effective multimedia presentations. Stunning animation and sensational special effects help you convince your audience. Create presentations that look even more professional than the standard presentations you commonly see at work. Get your collegues’ and bosses’ attention by creating something a little bit different.

DRAW lets you build diagrams and sketches from scratch. A picture is worth a thousand words, so why not try something simple with box and line diagrams? Or else go further and easily build dynamic 3D illustrations and special effects. It’s as simple or as powerful as you want it to be.

BASE is the database front-end of the LibreOffice suite. With Base, you can seamlessly integrate into your existing database structures. Based on imported and linked tables and queries from MySQL, PostgreSQL or Microsoft Access and many other data sources, you can build powerful databases containing forms, reports, views and queries. Full integration is possible with the in-built HSQL database.

MATH is a simple equation editor that lets you lay-out and display your mathematical, chemical, electrical or scientific equations quickly in standard written notation. Even the most-complex calculations can be understandable when displayed correctly. E=mc2

Open Documentation just announced release candidate 3 of Libre office.

New Features-

http://www.libreoffice.org/download/new-features/

General

  • Added the LibreColors to the palette;
  • Added Quickstarter for Unix builds;
  • Introduced Linux “Libertine G” and Linux “Biolinum G” fonts;
  • Implement import of alpha channel for RGBA .tiffs [http://bugs.freedesktop.org/show_bug.cgi?id=30472];
  • Show all appropiate formats by default on “Save As” [http://qa.openoffice.org/issues/show_bug.cgi?id=113141];
  • Use radio buttons for mutually exclusive menu options;
  • Replace the “Help Support” menu item by the “License Information” one;
  • Load and save documents in flat XML;
  • Made Help system available via the WikiHelp;
  • Option to enable saving of documents at all times (see Tools -> Options -> LibreOffice -> General -> “Allow to save document…”).

Calc

  • [http://bugs.freedesktop.org/show_bug.cgi?id=30559]: Added new tab page ‘Compatibility’ in the Options dialog;
  • Better default key bindings;
  • Use Ctrl-Shift-D to launch selection list in LibreOffice;
  • Added new image file used in the “insert new sheet” button. This image is not visible in read-only mode;
  • Fix fake small caps resizing factor [http://qa.openoffice.org/issues/show_bug.cgi?id=1526];
  • Added dotted/dashed borders in Calc;
  • Added icons for toggling sheet grids in Calc;
  • Better performance and interoperability on Excel doc import;
  • Better performance on DBF import;
  • Slightly better performance on ODS import;
  • Possibility to use English formula names;
  • Distributed alignment – allows one to specify ‘distributed’ horizontal alignment and ‘justified’ and ‘distributed’ vertical alignments within cells. This is notably useful for CJK locales;
  • Support for 3 different formula syntaxes: Calc A1, Excel A1 and Excel R1C1;
  • Configurable argument and array separators in formula expressions;
  • External reference works within OFFSET function;
  • Hitting TAB during auto-complete commits current selection and moves to the next cell;
  • Shift-TAB cycles through auto-complete selections;
  • Find and replace skips those cells that are filtered out (thus hidden);
  • Protecting sheet provides two additional sheet protection options, to optionally limit cursor placement in protected and unprotected areas;
  • Copying a range highlights the range being copied. It also allows you to paste it by hitting ENTER key. Hitting ESC removes the range highlight;
  • Jumping to and from references in formula cells via “Ctrl-[” and “Ctrl-]”;
  • Cell cursor stays at the original cell during range selection.

Writer

  • AutoCorrections match case of the words that AutoCorrect replaces. (Issuezilla 2838);
  • Ability to turn off number recognition in Writer;
  • RTF export (from GSoc);
  • Port of Lotus Word Pro filter;
  • New dialog box for title page.

Impress/Draw

Math

Base

Development

  • UNO APIs for size and moveProtect of notes;
  • Via Issuezilla bug #i80184: allow addition of drawing documents to gallery via API.

Productivity Enhancements

  • New custom properties handling;
  • Embedding of standard PDF fonts;
  • New “Narrow” font family;
  • Increased document protection in Writer and Calc;
  • Automatic decimals digits for “General” format in Calc;
  • 1 million rows in a spreadsheet;
  • New options for CSV (Comma-Separated Value) importation in Calc;
  • Insert drawing objects in charts;
  • Hierarchical axis labels for charts;
  • Improved slide layout handling in Impress;
  • Manual setting for primary key support for databases;
  • Support of Read-Only database registration;
  • New Math command: ‘nospace’.

Internationalization

  • Additional locale data.

Usability and Interface

  • Common search toolbar;
  • New easier-to-use print interface;
  • More options for changing case;
  • Redesign of thesaurus;
  • Resetting of text to the default language in Writer;
  • Text rendering of form controls in Writer;
  • Changed defaults for charts;
  • Colored sheet tabs in Calc;
  • Adaptation to marked selection for filter area in Calc;
  • Sort dialog box for DataPilot in Calc;
  • Display custom names for DataPilot fields, items and totals in Calc.

Developer Features and Extensibility

  • Grid control enhancements;
  • New MetaData node for database;
  • Extending database drivers using extensions.
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Interview Luis Torgo Author Data Mining with R

Example of k-nearest neighbour classification
Image via Wikipedia

Here is an interview with Prof Luis Torgo, author of the recent best seller “Data Mining with R-learning with case studies”.

Ajay- Describe your career in science. How do you think can more young people be made interested in science.

Luis- My interest in science only started after I’ve finished my degree. I’ve entered a research lab at the University of Porto and started working on Machine Learning, around 1990. Since then I’ve been involved generally in data analysis topics both from a research perspective as well as from a more applied point of view through interactions with industry partners on several projects. I’ve spent most of my career at the Faculty of Economics of the University of Porto, but since 2008 I’m at the department of Computer Science of the Faculty of Sciences of the same university. At the same time I’ve been a researcher at LIAAD / Inesc Porto LA (www.liaad.up.pt).

I like a lot what I do and like science and the “scientific way of thinking”, but I cannot say that I’ve always thought of this area as my “place”. Most of all I like solving challenging problems through data analysis. If that translates into some scientific outcome than I’m more satisfied but that is not my main goal, though I’m kind of “forced” to think about that because of the constraints of an academic career.

That does not mean I’m not passionate about science, I just think there are many more ways of “doing science” than what is reflected in the usual “scientific indicators” that most institutions seem to be more and more obsessed about.

Regards interesting young people in science that is a hard question that I’m not sure I’m qualified to answer. I do tend to think that young people are more sensible to concrete examples of problems they think are interesting and that science helps in solving, as a way of finding a motivation for facing the hard work they will encounter in a scientific career. I do believe in case studies as a nice way to learn and motivate, and thus my book 😉

Ajay- Describe your new book “Data Mining with R, learning with case studies” Why did you choose a case study based approach? who is the target audience? What is your favorite case study from the book

Luis- This book is about learning how to use R for data mining. The book follows a “learn by doing it” approach to data mining instead of the more common theoretical description of the available techniques in this discipline. This is accomplished by presenting a series of illustrative case studies for which all necessary steps, code and data are provided to the reader. Moreover, the book has an associated web page (www.liaad.up.pt/~ltorgo/DataMiningWithR) where all code inside the book is given so that easy copy-paste is possible for the more lazy readers.

The language used in the book is very informal without many theoretical details on the used data mining techniques. For obtaining these theoretical insights there are already many good data mining books some of which are referred in “further readings” sections given throughout the book. The decision of following this writing style had to do with the intended target audience of the book.

In effect, the objective was to write a monograph that could be used as a supplemental book for practical classes on data mining that exist in several courses, but at the same time that could be attractive to professionals working on data mining in non-academic environments, and thus the choice of this more practically oriented approach.

Regards my favorite case study that is a hard question for an author… still I would probably choose the “Predicting Stock Market Returns” case study (Chapter 3). Not only because I like this challenging problem, but mainly because the case study addresses all aspects of knowledge discovery in a real world scenario and not only the construction of predictive models. It tackles data collection, data pre-processing, model construction, transforming predictions into actions using different trading policies, using business-related performance metrics, implementing a trading simulator for “real-world” evaluation, and laying out grounds for constructing an online trading system.

Obviously, for all these steps there are far too many options to be possible to describe/evaluate all of them in a chapter, still I do believe that for the reader it is important to see the overall picture, and read about the relevant questions on this problem and some possible paths that can be followed at these different steps.

In other words: do not expect to become rich with the solution I describe in the chapter !

Ajay- Apart from R, what other data mining software do you use or have used in the past. How would you compare their advantages and disadvantages with R

Luis- I’ve played around with Clementine, Weka, RapidMiner and Knime, but really only playing with teaching goals, and no serious use/evaluation in the context of data mining projects. For the latter I mainly use R or software developed by myself (either in R or other languages). In this context, I do not think it is fair to compare R with these or other tools as I lack serious experience with them. I can however, tell you about what I see as the main pros and cons of R. The main reason for using R is really not only the power of the tool that does not stop surprising me in terms of what already exists and keeps appearing as contributions of an ever growing community, but mainly the ability of rapidly transforming ideas into prototypes. Regards some of its drawbacks I would probably mention the lack of efficiency when compared to other alternatives and the problem of data set sizes being limited by main memory.

I know that there are several efforts around for solving this latter issue not only from the community (e.g. http://cran.at.r-project.org/web/views/HighPerformanceComputing.html), but also from the industry (e.g. Revolution Analytics), but I would prefer that at this stage this would be a standard feature of the language so the the “normal” user need not worry about it. But then this is a community effort and if I’m not happy with the current status instead of complaining I should do something about it!

Ajay- Describe your writing habit- How do you set about writing the book- did you write a fixed amount daily or do you write in bursts etc

Luis- Unfortunately, I write in bursts whenever I find some time for it. This is much more tiring and time consuming as I need to read back material far too often, but I cannot afford dedicating too much consecutive time to a single task. Actually, I frequently tease my PhD students when they “complain” about the lack of time for doing what they have to, that they should learn to appreciate the luxury of having a single task to complete because it will probably be the last time in their professional life!

Ajay- What do you do to relax or unwind when not working?

Luis- For me, the best way to relax from work is by playing sports. When I’m involved in some game I reset my mind and forget about all other things and this is very relaxing for me. A part from sports I enjoy a lot spending time with my family and friends. A good and long dinner with friends over a good bottle of wine can do miracles when I’m too stressed with work! Finally,I do love traveling around with my family.

Luis Torgo

Short Bio: Luis Torgo has a degree in Systems and Informatics Engineering and a PhD in Computer Science. He is an Associate Professor of the Department of Computer Science of the Faculty of Sciences of the University of Porto. He is also a researcher of the Laboratory of Artificial Intelligence and Data Analysis (LIAAD) belonging to INESC Porto LA. Luis Torgo has been an active researcher in Machine Learning and Data Mining for more than 20 years. He has lead several academic and industrial Data Mining research projects. Luis Torgo accompanies the R project almost since its beginning, using it on his research activities. He teaches R at different levels and has given several courses in different countries.

For reading “Data Mining with R” – you can visit this site, also to avail of a 20% discount the publishers have generously given (message below)-

For more information and to place an order, visit us at http://www.crcpress.com.  Order online and apply 20% Off discount code 907HM at checkout.  CRC is pleased to offer free standard shipping on all online orders!

link to the book page  http://www.crcpress.com/product/isbn/9781439810187

Price: $79.95
Cat. #: K10510
ISBN: 9781439810187
ISBN 10: 1439810184
Publication Date: November 09, 2010
Number of Pages: 305
Availability: In Stock
Binding(s): Hardback 

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Assumptions on Guns

This is a very crude yet functional homemade g...
Image via Wikipedia

While sitting in Delhi, India- I sometimes notice that there is one big new worthy gun related incident in the United States every six months (latest incident Gabrielle giffords incident) and the mythical NRA (which seems just as powerful as equally mythical Jewish American or Cuban American lobby ) . As someone who once trained to fire guns (.22 and SLR -rifles actually), comes from a gun friendly culture (namely Punjabi-North Indian), my dad carried a gun sometimes as a police officer during his 30 plus years of service, I dont really like guns (except when they are in a movie). My 3 yr old son likes guns a lot (for some peculiar genetic reason even though we are careful not to show him any violent TV or movie at all).

So to settle the whole guns are good- guns are bad thing I turned to the one resource -Internet

Here are some findings-

1) A lot of hard statistical data on guns is biased by the perspective of the writer- it reminds me of the old saying Lies, True lies and Statistics.

2) There is not a lot of hard data in terms of a universal research which can be quoted- unlike say lung cancer is caused by cigarettes- no broad research which can be definitive in this regards.

3) American , European and Asian attitudes on guns actually seem a function of historical availability , historic crime rates and cultural propensity for guns.

Switzerland and United States are two extreme outlier examples on gun causing violence causal statistics.

4) Lot of old and outdated data quoted selectively.

It seems you can fudge data about guns in the following ways-

1) Use relative per capita numbers vis a vis aggregate numbers

2) Compare and contrast gun numbers with crime numbers selectively

3) Remove drill down of type of firearm- like hand guns, rifles, automatic, semi automatic

Maybe I am being simplistic-but I found it easier to list credible data sources on guns than to summarize all assumptions on guns. Are guns good or bad- i dont know -it depends? Any research you can quote is welcome.

Data Sources on Guns and Firearms and Crime-

1) http://www.justfacts.com/guncontrol.asp

Ownership

* As of 2009, the United States has a population of 307 million people.[5]

* Based on production data from firearm manufacturers,[6] there are roughly 300 million firearms owned by civilians in the United States as of 2010. Of these, about 100 million are handguns.[7]

* Based upon surveys, the following are estimates of private firearm ownership in the U.S. as of 2010:

Households With a Gun Adults Owning a Gun Adults Owning a Handgun
Percentage 40-45% 30-34% 17-19%
Number 47-53 million 70-80 million 40-45 million

[8]

* A 2005 nationwide Gallup poll of 1,012 adults found the following levels of firearm ownership:

Category Percentage Owning 

a Firearm
Households 42%
Individuals 30%
Male 47%
Female 13%
White 33%
Nonwhite 18%
Republican 41%
Independent 27%
Democrat 23%

[9]

* In the same poll, gun owners stated they own firearms for the following reasons:

Protection Against Crime 67%
Target Shooting 66%
Hunting 41%

2) NationMaster.com

http://www.nationmaster.com/graph/cri_mur_wit_fir-crime-murders-with-firearms

VIEW DATA: Totals Per capita
Definition Source Printable version
Bar Graph Pie Chart Map

Showing latest available data.

Rank Countries Amount
# 1 South Africa: 31,918
# 2 Colombia: 21,898
# 3 Thailand: 20,032
# 4 United States: 9,369
# 5 Philippines: 7,708
# 6 Mexico: 2,606
# 7 Slovakia: 2,356
# 8 El Salvador: 1,441
# 9 Zimbabwe: 598
# 10 Peru: 442
# 11 Germany: 269
# 12 Czech Republic: 181
# 13 Ukraine: 173
# 14 Canada: 144
# 15 Albania: 135
# 16 Costa Rica: 131
# 17 Azerbaijan: 120
# 18 Poland: 111
# 19 Uruguay: 109
# 20 Spain: 97
# 21 Portugal: 90
# 22 Croatia: 76
# 23 Switzerland: 68
# 24 Bulgaria: 63
# 25 Australia: 59
# 26 Sweden: 58
# 27 Bolivia: 52
# 28 Japan: 47
# 29 Slovenia: 39
= 30 Hungary: 38
= 30 Belarus: 38
# 32 Latvia: 28
# 33 Burma: 27
# 34 Macedonia, The Former Yugoslav Republic of: 26
# 35 Austria: 25
# 36 Estonia: 21
# 37 Moldova: 20
# 38 Lithuania: 16
= 39 United Kingdom: 14
= 39 Denmark: 14
# 41 Ireland: 12
# 42 New Zealand: 10
# 43 Chile: 9
# 44 Cyprus: 4
# 45 Morocco: 1
= 46 Iceland: 0
= 46 Luxembourg: 0
= 46 Oman: 0
Total: 100,693
Weighted average: 2,097.8

DEFINITION: Total recorded intentional homicides committed with a firearm. Crime statistics are often better indicators of prevalence of law enforcement and willingness to report crime, than actual prevalence.

SOURCE: The Eighth United Nations Survey on Crime Trends and the Operations of Criminal Justice Systems (2002) (United Nations Office on Drugs and Crime, Centre for International Crime Prevention)

3)

Bureau of Justice Statistics

see

http://bjs.ojp.usdoj.gov/dataonline/Search/Homicide/State/RunHomTrendsInOneVar.cfm

or the brand new website (till 2009) on which I CANNOT get gun crime but can get total

http://www.ucrdatatool.gov/

Estimated  murder rate *
Year United States-Total
1960 5.1
1961 4.8
1962 4.6
1963 4.6
1964 4.9
1965 5.1
1966 5.6
1967 6.2
1968 6.9
1969 7.3
1970 7.9
1971 8.6
1972 9.0
1973 9.4
1974 9.8
1975 9.6
1976 8.7
1977 8.8
1978 9.0
1979 9.8
1980 10.2
1981 9.8
1982 9.1
1983 8.3
1984 7.9
1985 8.0
1986 8.6
1987 8.3
1988 8.5
1989 8.7
1990 9.4
1991 9.8
1992 9.3
1993 9.5
1994 9.0
1995 8.2
1996 7.4
1997 6.8
1998 6.3
1999 5.7
2000 5.5
2001 5.6
2002 5.6
2003 5.7
2004 5.5
2005 5.6
2006 5.7
2007 5.6
2008 5.4
2009 5.0
Notes: National or state offense totals are based on data from all reporting agencies and estimates for unreported areas.
* Rates are the number of reported offenses per 100,000 population
  • United States-Total –
    • The 168 murder and nonnegligent homicides that occurred as a result of the bombing of the Alfred P. Murrah Federal Building in Oklahoma City in 1995 are included in the national estimate.
    • The 2,823 murder and nonnegligent homicides that occurred as a result of the events of September 11, 2001, are not included in the national estimates.

     

    		•
    Sources: 
    FBI, Uniform Crime Reports as prepared by the National Archive of Criminal Justice Data
    4) united nation statistics of 2002  were too old in my opinion.
    wikipedia seems too broad based to qualify as a research article but is easily accessible http://en.wikipedia.org/wiki/Gun_violence_in_the_United_States
    to actually buy a gun or see guns available for purchase in United States see
    http://www.usautoweapons.com/
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    Interview Ajay Ohri Decisionstats.com with DMR

    From-

    http://www.dataminingblog.com/data-mining-research-interview-ajay-ohri/

    Here is the winner of the Data Mining Research People Award 2010: Ajay Ohri! Thanks to Ajay for giving some time to answer Data Mining Research questions. And all the best to his blog, Decision Stat!

    Data Mining Research (DMR): Could you please introduce yourself to the readers of Data Mining Research?

    Ajay Ohri (AO): I am a business consultant and writer based out of Delhi- India. I have been working in and around the field of business analytics since 2004, and have worked with some very good and big companies primarily in financial analytics and outsourced analytics. Since 2007, I have been writing my blog at http://decisionstats.com which now has almost 10,000 views monthly.

    All in all, I wrote about data, and my hobby is also writing (poetry). Both my hobby and my profession stem from my education ( a masters in business, and a bachelors in mechanical engineering).

    My research interests in data mining are interfaces (simpler interfaces to enable better data mining), education (making data mining less complex and accessible to more people and students), and time series and regression (specifically ARIMAX)
    In business my research interests software marketing strategies (open source, Software as a service, advertising supported versus traditional licensing) and creation of technology and entrepreneurial hubs (like Palo Alto and Research Triangle, or Bangalore India).

    DMR: I know you have worked with both SAS and R. Could you give your opinion about these two data mining tools?

    AO: As per my understanding, SAS stands for SAS language, SAS Institute and SAS software platform. The terms are interchangeably used by people in industry and academia- but there have been some branding issues on this.
    I have not worked much with SAS Enterprise Miner , probably because I could not afford it as business consultant, and organizations I worked with did not have a budget for Enterprise Miner.
    I have worked alone and in teams with Base SAS, SAS Stat, SAS Access, and SAS ETS- and JMP. Also I worked with SAS BI but as a user to extract information.
    You could say my use of SAS platform was mostly in predictive analytics and reporting, but I have a couple of projects under my belt for knowledge discovery and data mining, and pattern analysis. Again some of my SAS experience is a bit dated for almost 1 year ago.

    I really like specific parts of SAS platform – as in the interface design of JMP (which is better than Enterprise Guide or Base SAS ) -and Proc Sort in Base SAS- I guess sequential processing of data makes SAS way faster- though with computing evolving from Desktops/Servers to even cheaper time shared cloud computers- I am not sure how long Base SAS and SAS Stat can hold this unique selling proposition.

    I dislike the clutter in SAS Stat output, it confuses me with too much information, and I dislike shoddy graphics in the rendering output of graphical engine of SAS. Its shoddy coding work in SAS/Graph and if JMP can give better graphics why is legacy source code preventing SAS platform from doing a better job of it.

    I sometimes think the best part of SAS is actually code written by Goodnight and Sall in 1970’s , the latest procs don’t impress me much.

    SAS as a company is something I admire especially for its way of treating employees globally- but it is strange to see the rest of tech industry not following it. Also I don’t like over aggression and the SAS versus Rest of the Analytics /Data Mining World mentality that I sometimes pick up when I deal with industry thought leaders.

    I think making SAS Enterprise Miner, JMP, and Base SAS in a completely new web interface priced at per hour rates is my wishlist but I guess I am a bit sentimental here- most data miners I know from early 2000’s did start with SAS as their first bread earning software. Also I think SAS needs to be better priced in Business Intelligence- it seems quite cheap in BI compared to Cognos/IBM but expensive in analytical licensing.

    If you are a new stats or business student, chances are – you may know much more R than SAS today. The shift in education at least has been very rapid, and I guess R is also more of a platform than a analytics or data mining software.

    I like a lot of things in R- from graphics, to better data mining packages, modular design of software, but above all I like the can do kick ass spirit of R community. Lots of young people collaborating with lots of young to old professors, and the energy is infectious. Everybody is a CEO in R ’s world. Latest data mining algols will probably start in R, published in journals.

    Which is better for data mining SAS or R? It depends on your data and your deadline. The golden rule of management and business is -it depends.

    Also I have worked with a lot of KXEN, SQL, SPSS.

    DMR: Can you tell us more about Decision Stats? You have a traffic of 120′000 for 2010. How did you reach such a success?

    AO: I don’t think 120,000 is a success. Its not a failure. It just happened- the more I wrote, the more people read.In 2007-2008 I used to obsess over traffic. I tried SEO, comments, back linking, and I did some black hat experimental stuff. Some of it worked- some didn’t.

    In the end, I started asking questions and interviewing people. To my surprise, senior management is almost always more candid , frank and honest about their views while middle managers, public relations, marketing folks can be defensive.

    Social Media helped a bit- Twitter, Linkedin, Facebook really helped my network of friends who I suppose acted as informal ambassadors to spread the word.
    Again I was constrained by necessity than choices- my middle class finances ( I also had a baby son in 2007-my current laptop still has some broken keys :) – by my inability to afford traveling to conferences, and my location Delhi isn’t really a tech hub.

    The more questions I asked around the internet, the more people responded, and I wrote it all down.

    I guess I just was lucky to meet a lot of nice people on the internet who took time to mentor and educate me.

    I tried building other websites but didn’t succeed so i guess I really don’t know. I am not a smart coder, not very clever at writing but I do try to be honest.

    Basic economics says pricing is proportional to demand and inversely proportional to supply. Honest and candid opinions have infinite demand and an uncertain supply.

    DMR: There is a rumor about a R book you plan to publish in 2011 :-) Can you confirm the rumor and tell us more?

    AO: I just signed a contract with Springer for ” R for Business Analytics”. R is a great software, and lots of books for statistically trained people, but I felt like writing a book for the MBAs and existing analytics users- on how to easily transition to R for Analytics.

    Like any language there are tricks and tweaks in R, and with a focus on code editors, IDE, GUI, web interfaces, R’s famous learning curve can be bent a bit.

    Making analytics beautiful, and simpler to use is always a passion for me. With 3000 packages, R can be used for a lot more things and a lot more simply than is commonly understood.
    The target audience however is business analysts- or people working in corporate environments.

    Brief Bio-
    Ajay Ohri has been working in the field of analytics since 2004 , when it was a still nascent emerging Industries in India. He has worked with the top two Indian outsourcers listed on NYSE,and with Citigroup on cross sell analytics where he helped sell an extra 50000 credit cards by cross sell analytics .He was one of the very first independent data mining consultants in India working on analytics products and domestic Indian market analytics .He regularly writes on analytics topics on his web site www.decisionstats.com and is currently working on open source analytical tools like R besides analytical software like SPSS and SAS.

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    Keynote: Five Ways Predictive Analytics Cuts Enterprise Risk  

    Eric Siegel, Ph.D., Program Chair, Predictive Analytics World

    All business is an exercise in risk management. All organizations would benefit from measuring, tracking and computing risk as a core process, much like insurance companies do.

    Predictive analytics does the trick, one customer at a time. This technology is a data-driven means to compute the risk each customer will defect, not respond to an expensive mailer, consume a retention discount even if she were not going to leave in the first place, not be targeted for a telephone solicitation that would have landed a sale, commit fraud, or become a “loss customer” such as a bad debtor or an insurance policy-holder with high claims.

    In this keynote session, Dr. Eric Siegel reveals:

    – Five ways predictive analytics evolves your enterprise to reduce risk

    – Hidden sources of risk across operational functions

    – What every business should learn from insurance companies

    – How advancements have reversed the very meaning of fraud

    – Why “man + machine” teams are greater than the sum of their parts for enterprise decision support

    Length – 00:45:57 | Email to a Colleague

    Price: $195

     

     

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    Play video of session: Platinum Sponsor Presentation, Analytics: The Beauty of Diversity
    Platinum Sponsor Presentation: Analytics – The Beauty of Diversity 

    Anne H. Milley, Senior Director of Analytic Strategy, Worldwide Product Marketing, SAS

    Analytics contributes to, and draws from, multiple disciplines. The unifying theme of “making the world a better place” is bred from diversity. For instance, the same methods used in econometrics might be used in market research, psychometrics and other disciplines. In a similar way, diverse paradigms are needed to best solve problems, reveal opportunities and make better decisions. This is why we evolve capabilities to formulate and solve a wide range of problems through multiple integrated languages and interfaces. Extending that, we have provided integration with other languages so that users can draw on the disciplines and paradigms needed to best practice their craft.

    Length – 20:11 | Email to a Colleague

    Free viewing enabled – no charge

     

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    Play video of session: Gold Sponsor Presentation Predictive Analytics Accelerate Insight for Financial Services
    Gold Sponsor Presentation: Predictive Analytics Accelerate Insight for Financial Services 

    Finbarr Deely, Director of Business Development,ParAccel

    Financial services organizations face immense hurdles in maintaining profitability and building competitive advantage. Financial services organizations must perform “what-if” scenario analysis, identify risks, and detect fraud patterns. The advanced analytic complexity required often makes such analysis slow and painful, if not impossible. This presentation outlines the analytic challenges facing these organizations and provides a clear path to providing the accelerated insight needed to perform in today’s complex business environment to reduce risk, stop fraud and increase profits. * The value of predictive analytics in Accelerating Insight * Financial Services Analytic Case Studies * Brief Overview of ParAccel Analytic Database

    Length – 09:06 | Email to a Colleague

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    TOPIC: BUSINESS VALUE
    Case Study: Monster.com
    Creating Global Competitive Power with Predictive Analytics 

    Jean Paul Isson, Vice President, Globab BI & Predictive Analytics, Monster Worldwide

    Using Predictive analytics to gain a deeper understanding of customer behaviours, increase marketing ROI and drive growth

    – Creating global competitive power with business intelligence: Making the right decisions – at the right time

    – Avoiding common change management challenges in sales, marketing, customer service, and products

    – Developing a BI vision – and implementing it: successful business intelligence implementation models

    – Using predictive analytics as a business driver to stay on top of the competition

    – Following the Monster Worldwide global BI evolution: How Monster used BI to go from good to great

    Length – 51:17 | Email to a Colleague

    Price: $195

     

     

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    TOPIC: SURVEY ANALYSIS
    Case Study: YMCA
    Turning Member Satisfaction Surveys into an Actionable Narrative 

    Dean Abbott, President, Abbott Analytics

    Employees are a key constituency at the Y and previous analysis has shown that their attitudes have a direct bearing on Member Satisfaction. This session will describe a successful approach for the analysis of YMCA employee surveys. Decision trees are built and examined in depth to identify key questions in describing key employee satisfaction metrics, including several interesting groupings of employee attitudes. Our approach will be contrasted with other factor analysis and regression-based approaches to survey analysis that we used initially. The predictive models described are currently in use and resulted in both greater understanding of employee attitudes, and a revised “short-form” survey with fewer key questions identified by the decision trees as the most important predictors.

    Length – 50:19 | Email to a Colleague

    Price: $195

     

     

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    TOPIC: INDUSTRY TRENDS
    2010 Data Minter Survey Results: Highlights     

    Karl Rexer, Ph.D., Rexer Analytics

    Do you want to know the views, actions, and opinions of the data mining community? Each year, Rexer Analytics conducts a global survey of data miners to find out. This year at PAW we unveil the results of our 4th Annual Data Miner Survey. This session will present the research highlights, such as:

    – Analytic goals & key challenges

    – Impact of the economy

    – Regional differences

    – Text mining trends

    Length – 15:20 | Email to a Colleague

    Price: $195

     

     

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    Multiple Case Studies: U.S. DoD, U.S. DHS, SSA
    Text Mining: Lessons Learned 

    John F. Elder, Chief Scientist, Elder Research, Inc.

    Text Mining is the “Wild West” of data mining and predictive analytics – the potential for gain is huge, the capability claims are often tall tales, and the “land rush” for leadership is very much a race.

    In solving unstructured (text) analysis challenges, we found that principles from inductive modeling – learning relationships from labeled cases – has great power to enhance text mining. Dr. Elder highlights key technical breakthroughs discovered while working on projects for leading government agencies, including: Text Mining is the “Wild West” of data mining and predictive analytics – the potential for gain is huge, the capability claims are often tall tales, and the “land rush” for leadership is very much a race.

    – Prioritizing searches for the Dept. of Homeland Security

    – Quick decisions for Social Security Admin. disability

    – Document discovery for the Dept. of Defense

    – Disease discovery for the Dept. of Homeland Security

    – Risk profiling for the Dept. of Defense

    Length – 48:58 | Email to a Colleague

    Price: $195

     

     

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    Play video of session: Keynote: How Target Gets the Most out of Its Guest Data to Improve Marketing ROI
    Keynote: How Target Gets the Most out of Its Guest Data to Improve Marketing ROI 

    Andrew Pole, Senior Manager, Media and Database Marketing, Target

    In this session, you’ll learn how Target leverages its own internal guest data to optimize its direct marketing – with the ultimate goal of enhancing our guests’ shopping experience and driving in-store and online performance. You will hear about what guest data is available at Target, how and where we collect it, and how it is used to improve the performance and relevance of direct marketing vehicles. Furthermore, we will discuss Target’s development and usage of guest segmentation, response modeling, and optimization as means to suppress poor performers from mailings, determine relevant product categories and services for online targeted content, and optimally assign receipt marketing offers to our guests when offer quantities are limited.

    Length – 47:49 | Email to a Colleague

    Free viewing enabled – no charge

     

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    Play video of session: Platinum Sponsor Presentation: Driving Analytics Into Decision Making
    Platinum Sponsor Presentation: Driving Analytics Into Decision Making  

    Jason Verlen, Director, SPSS Product Strategy & Management, IBM Software Group

    Organizations looking to dramatically improve their business outcomes are turning to decision management, a convergence of technology and business processes that is used to streamline and predict the outcome of daily decision-making. IBM SPSS Decision Management technology provides the critical link between analytical insight and recommended actions. In this session you’ll learn how Decision Management software integrates analytics with business rules and business applications for front-line systems such as call center applications, insurance claim processing, and websites. See how you can improve every customer interaction, minimize operational risk, reduce fraud and optimize results.

    Length – 17:29 | Email to a Colleague

    Free viewing enabled – no charge

     

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    TOPIC: DATA INFRASTRUCTURE AND INTEGRATION
    Case Study: Macy’s
    The world is not flat (even though modeling software has to think it is) 

    Paul Coleman, Director of Marketing Statistics, Macy’s Inc.

    Software for statistical modeling generally use flat files, where each record represents a unique case with all its variables. In contrast most large databases are relational, where data are distributed among various normalized tables for efficient storage. Variable creation and model scoring engines are necessary to bridge data mining and storage needs. Development datasets taken from a sampled history require snapshot management. Scoring datasets are taken from the present timeframe and the entire available universe. Organizations, with significant data, must decide when to store or calculate necessary data and understand the consequences for their modeling program.

    Length – 34:54 | Email to a Colleague

    Price: $195

     

     

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    TOPIC: CUSTOMER VALUE
    Case Study: SunTrust
    When One Model Will Not Solve the Problem – Using Multiple Models to Create One Solution 

    Dudley Gwaltney, Group Vice President, Analytical Modeling, SunTrust Bank

    In 2007, SunTrust Bank developed a series of models to identify clients likely to have large changes in deposit balances. The models include three basic binary and two linear regression models.

    Based on the models, 15% of SunTrust clients were targeted as those most likely to have large balance changes. These clients accounted for 65% of the absolute balance change and 60% of the large balance change clients. The targeted clients are grouped into a portfolio and assigned to individual SunTrust Retail Branch. Since 2008, the portfolio generated a 2.6% increase in balances over control.

    Using the SunTrust example, this presentation will focus on:

    – Identifying situations requiring multiple models

    – Determining what types of models are needed

    – Combining the individual component models into one output

    Length – 48:22 | Email to a Colleague

    Price: $195

     

     

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    TOPIC: RESPONSE & CROSS-SELL
    Case Study: Paychex
    Staying One Step Ahead of the Competition – Development of a Predictive 401(k) Marketing and Sales Campaign 

    Jason Fox, Information Systems and Portfolio Manager,Paychex

    In-depth case study of Paychex, Inc. utilizing predictive modeling to turn the tides on competitive pressures within their own client base. Paychex, a leading provider of payroll and human resource solutions, will guide you through the development of a Predictive 401(k) Marketing and Sales model. Through the use of sophisticated data mining techniques and regression analysis the model derives the probability a client will add retirement services products with Paychex or with a competitor. Session will include roadblocks that could have ended development and ROI analysis. Speaker: Frank Fiorille, Director of Enterprise Risk Management, Paychex Speaker: Jason Fox, Risk Management Analyst, Paychex

    Length – 26:29 | Email to a Colleague

    Price: $195

     

     

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    TOPIC: SEGMENTATION
    Practitioner: Canadian Imperial Bank of Commerce
    Segmentation Do’s and Don’ts 

    Daymond Ling, Senior Director, Modelling & Analytics,Canadian Imperial Bank of Commerce

    The concept of Segmentation is well accepted in business and has withstood the test of time. Even with the advent of new artificial intelligence and machine learning methods, this old war horse still has its place and is alive and well. Like all analytical methods, when used correctly it can lead to enhanced market positioning and competitive advantage, while improper application can have severe negative consequences.

    This session will explore what are the elements of success, and what are the worse practices that lead to failure. The relationship between segmentation and predictive modeling will also be discussed to clarify when it is appropriate to use one versus the other, and how to use them together synergistically.

    Length – 45:57 | Email to a Colleague

    Price: $195

     

     

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    TOPIC: SOCIAL DATA
    Thought Leadership
    Social Network Analysis: Killer Application for Cloud Analytics     

    James Kobielus, Senior Analyst, Forrester Research

    Social networks such as Twitter and Facebook are a potential goldmine of insights on what is truly going through customers´minds. Every company wants to know whether, how, how often, and by whom they´re being mentioned across the billowing new cloud of social media. Just as important, every company wants to influence those discussions in their favor, target new business, and harvest maximum revenue potential. In this session, Forrester analyst James Kobielus identifies fruitful applications of social network analysis in customer service, sales, marketing, and brand management. He presents a roadmap for enterprises to leverage their inline analytics initiatives and leverage high-performance data warehousing (DW) clouds and appliances in order to analyze shifting patterns of customer sentiment, influence, and propensity. Leveraging Forrester’s ongoing research in advanced analytics and customer relationship management, Kobielus will discuss industry trends, commercial modeling tools, and emerging best practices in social network analysis, which represents a game-changing new discipline in predictive analytics.

    Length – 48:16 | Email to a Colleague

    Price: $195

     

     

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    TOPIC: HEALTHCARE – INTERNATIONAL TARGETING
    Case Study: Life Line Screening
    Taking CRM Global Through Predictive Analytics 

    Ozgur Dogan,
    VP, Quantitative Solutions Group, Merkle Inc

    Trish Mathe,
    Director of Database Marketing, Life Line Screening

    While Life Line is successfully executing a US CRM roadmap, they are also beginning this same evolution abroad. They are beginning in the UK where Merkle procured data and built a response model that is pulling responses over 30% higher than competitors. This presentation will give an overview of the US CRM roadmap, and then focus on the beginning of their strategy abroad, focusing on the data procurement they could not get anywhere else but through Merkle and the successful modeling and analytics for the UK. Speaker: Ozgur Dogan, VP, Quantitative Solutions Group, Merkle Inc Speaker: Trish Mathe, Director of Database Marketing, Life Line Screening

    Length – 40:12 | Email to a Colleague

    Price: $195

     

     

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    TOPIC: SURVEY ANALYSIS
    Case Study: Forrester
    Making Survey Insights Addressable and Scalable – The Case Study of Forrester’s Technographics Benchmark Survey 

    Nethra Sambamoorthi, Team Leader, Consumer Dynamics & Analytics, Global Consulting, Acxiom Corporation

    Marketers use surveys to create enterprise wide applicable strategic insights to: (1) develop segmentation schemes, (2) summarize consumer behaviors and attitudes for the whole US population, and (3) use multiple surveys to draw unified views about their target audience. However, these insights are not directly addressable and scalable to the whole consumer universe which is very important when applying the power of survey intelligence to the one to one consumer marketing problems marketers routinely face. Acxiom partnered with Forrester Research, creating addressable and scalable applications of Forrester’s Technographics Survey and applied it successfully to a number of industries and applications.

    Length – 39:23 | Email to a Colleague

    Price: $195

     

     

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    TOPIC: HEALTHCARE
    Case Study: UPMC Health Plan
    A Predictive Model for Hospital Readmissions 

    Scott Zasadil, Senior Scientist, UPMC Health Plan

    Hospital readmissions are a significant component of our nation’s healthcare costs. Predicting who is likely to be readmitted is a challenging problem. Using a set of 123,951 hospital discharges spanning nearly three years, we developed a model that predicts an individual’s 30-day readmission should they incur a hospital admission. The model uses an ensemble of boosted decision trees and prior medical claims and captures 64% of all 30-day readmits with a true positive rate of over 27%. Moreover, many of the ‘false’ positives are simply delayed true positives. 53% of the predicted 30-day readmissions are readmitted within 180 days.

    Length – 54:18 | Email to a Colleague

    Price: $195
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    Book Reviews- Hindu Myths- Mere Christianity

    A statue of Hindu deity Shiva in a temple in B...
    Image via Wikipedia

    Over the month long break I took, I was helping firm up my ideas for R for Analytics , I also took a break and read some books. Here are brief reviews of two, three of them-

    1) Hindu Myths

    This is a classical book translated from original Sanskrit written by Professor Wendy O Flaherty of University of Chicago. I found some of the older myths very interesting in terms of contradictions, retelling the same story in a modified way by another classic, the beautiful poetic and fantastic imagery evoked by Hindu myths. Some stories are as relevant in prayers, fasts and religious ceremonies as they were around 11000 years while most have morphed , edited or even distorted.

    It should help the non Indian reader understand why hundreds of millions of conservative Indians worship Shiv Ling ( or literally an idol of the Phallus of Shiva), the Hindu two cents of creation of the universe, and the somewhat fantastic stories on super heroes /gods/ in the ancient world.

    The book suffers from a few drawbacks in my opinion-

    1) Sanskrit is a bit like Latin- you can lose not just the flavor but original meaning of words and situational context. Some of the stories made better sense when i read a more recent Hindi translation.

    2) An excessive emphasis on sexual imagery rather than emotional imagery. The author seems wonder struck to read and translate ancient indians were so matter of fact about physical relationships. However the words were always written in discrete poetic than crass soft pornography.

    3) Almost no drawings or figures. This makes the book a bit dense to read at 300 pages.

    I liked another book on Hindu Myths (Myth= Mithya which I read in 2009) and you can see if you can read it if you find the topic interesting.

    A Handbook of Hindu Mythology

    Hindus have one God.
    They also have 330 million gods: male gods, female gods, personal gods, family gods, household gods, village gods, gods of space and time, gods for specific castes and particular professions, gods who reside in trees, in animals, in minerals, in geometrical patterns and in man-made objects.
    Then there are a whole host of demons.
    But no Devil.


    Mere Christianity by C S Lewis is a classic book on reinterpreting Christianity in modern times. However the author wrote this when World War 2 was on and it seems more like a British or Anglo Saxon interpretation of beliefs of Christ Jesus– who was actually a Jewish teacher born in Middle East Asia.

    While the language and reading makes it much easier to read- it is recommended more at Western audiences, than Eastern ones, as it seems some of the parables are a more palatable re interpretation of the New Testament. The Bible is a deceptively easy book to read, the language is short and beautiful-and the original parables in the Gospels remain powerful easy to understand.

    C S Lewis tends to emphasize morality than religiosity or faith, and there is not much comparison with any other faith or alternative morality. Dumbing down the Bible so as to market it better to reluctant consumers seems to be Mr Lewis intention and it is not as scholarly a work as an exercise in pure prose.

    However it is quite good as a self improvement book and is quite better than the “You Can Win” kind of books or even business concept books.

    Note- I find reading books on religion as good exercises in reading the fountain source of philosophies. As a polytheist- I tend to read more than one faith.

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    A Dare for Analytics Bloggers in 2011

    A new challenge for R , SAS and all techie bloggers-

    http://en.blog.wordpress.com/2010/12/30/challenge-for-2011-want-to-blog-more-often/

    As part of the DailyPost, we’re launching two campaigns:

    • Post a Day 2011: Post something to your blog every single day through 2011
    • Post a Week 2011: Post to your blog at least once a week through 2011

    Signing up is simple – do the following:

    1. Post on your blog, right now, that you’re participating
    2. (You can grab a sample post from dailypost.wordpress.com)
    3. Use the tag postaday2011 or postaweek2011 in your posts (tips on tagging here)
    4. Go to dailypost.wordpress.com
    5. Subscribe to dailypost.wordpress.com– you’ll get reminders and inspirations every day to help you bring your full potential to your WordPress blog!

     

    Do you write a blog or own a website?

    Well how about taking up this challenge?

    Who Dares-    Wins

    Game On!!!

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