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

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.

Jobs in Analytics

Here are some jobs from Vincent Granville, founder Analyticbridge. Please contact him directly- I just thought the Season of Joy should have better jobs than currently.

————————————————————————————–

Several job ads recently posted on DataShaping / AnalyticBridge, across United Sates and in Europe. Use the DataShaping search box to find more opportunities.

Job ads are posted at:

 

Selected opportunities:

Quantitative Modeling Consultants – Agilex (Alexandria, VA)
Sr. Software Development Engineers – Agilex (Alexandria, VA)
Actuary – FBL Financial Group (Des Moines, IA)
Relevance scientist – Yandex Labs (Palo Alto, CA)
Research Engineer, Search Ranking – Chomp (San Francisco, CA)
Mathematical Modeling and Optimization – Exxon (Clinton, NJ)
Data Analyst – DISH Network (Englewood, CO)
Sr Aviation Planning Research & Data Analyst – Port of Seattle (Seattle, WA)
Statistician / Quantitative Analyst – Indeed (Austin, TX)
Statistician – Pratt & Whitney (East Hartford, CT)
Biostatistician – The J. David Gladstone Institutes (San Francisco, CA)
Customer Service Representative (oklahoma, OK)
Program Associate – Cambridge Systematics (Washington D.C., DC)
Sr Risk Analyst – Paypal (Omaha, NE)
Sr. Actuarial Analyst – Farmers (Simi Valley, CA)
Senior Statistician, Data Services – Equifax (Alpharetta, GA)
Business Intelligence Analyst – Burbery (NYC, NY)
Fact Extraction – Amazon (Seattle, WA)
Senior Researcher – Bing (Bellevue, WA)
Senior Statistical Research Analyst – Walt Disney (Lake Buena Vista, FL)
Statistician – Capital One (Nottingham, NH)
Lead Data Analyst – Barclays (Northampton, UK)
Analytical Data Scientist – Aviagen (Huntsville, AL or Edinburgh, UK)
VP of Engineering for Analytics (Bay Area, CA)
Senior Software Engineer – Numenta (Redwood City, CA)
Numenta Internship Program – Numenta (Redwood City, CA)
Director of Analytics – Mozilla Corporation (Mountain View, CA)
Senior Sales Engineer – Statsoft (NY, NY)

Analytical Jobs for Thanksgiving /Christmass

some analytical positions from Analytical Searches.com

WWW's "historical" logo, created by ...
Image via Wikipedia

Relocation is provided for all except NY. H1 transfers are sponsored for the Analytical Manager/Senior Manager and Business Reporting Analyst only. ————————————————————————–

  1. Analytical Manager and Senior Manager/To $160K
  2. Analytics Manager/CA or IL/To $120K
  3. Bank Analytics Manager/IL/To $120K
  4. Business Reporting Analyst/NY/To $90K
  5. Credit Card Risk Analyst/ Illinois/To $95K
  6. Director Analytics/San Diego/To $170K
  7. Director, Operations Strategy and Analytics/San Diego/To $150K
  8. Lead Risk Analyst/CA & TX/To $150K
  9. Manager Modeling & Analytics/CT/To $120K
  10. Marketing Analytics/NY/To$85K
  11. Principal Product Manager, Vertical Markets/$150K/WA
  12. Research Statistician/OH/To $110K
  13. Senior Consultant Marketing Analytics/NY/To $120K
  14. Senior Director Strategic Consulting/To $130K
  15. Senior Manager Decision Science/CA/To $160K
  16. Senior Marketing / Web Analyst /NY/To $100K
  17. Senior Statistician/Modeling Position/VA/$80K
  18. Statistical Director/ Boston or Dallas/To $145K
  19. Statistical Manager/Boston/To $95K

 

Contact Details- Email Use The Referral Code- Santa Clause

Using R for Time Series in SAS

 

Time series: random data plus trend, with best...
Image via Wikipedia

 

Here is a great paper on using Time Series in R, and it specifically allows you to use just R output in Base SAS.

SAS Code

/* three methods: */

/* 1. Call R directly – Some errors are not reported to log */

x “’C:\Program Files\R\R-2.12.0\bin\r.exe’–no-save –no-restore <“”&rsourcepath\tsdiag.r””>””&rsourcepath\tsdiag.out”””;

/* include the R log in the SAS log */7data _null_;

infile “&rsourcepath\tsdiag.out”;

file log;

input;

put ’R LOG: ’ _infile_;

run;

/* include the image in the sas output.Specify a file if you are not using autogenerated html output */

ods html;

data _null_;

file print;

put “<IMG SRC=’” “&rsourcepath\plot.png” “’ border=’0’>”;

put “<IMG SRC=’” “&rsourcepath\acf.png” “’ border=’0’>”;

put “<IMG SRC=’” “&rsourcepath\pacf.png” “’ border=’0’>”;

put “<IMG SRC=’” “&rsourcepath\spect.png” “’ border=’0’>”;

put “<IMG SRC=’” “&rsourcepath\fcst.png” “’ border=’0’>”;

run;

ods html close;

The R code to create a time series plot is quite elegant though-


library(tseries)

air <- AirPassengers #Datasetname

ts.plot(air)

acf(air)

pacf(air)

plot(decompose(air))

air.fit <- arima(air,order=c(0,1,1), seasonal=list(order=c(0,1,1), period=12) #The ARIMA Model Based on PACF and ACF Graphs

tsdiag(air.fit)

library(forecast)

air.forecast <- forecast(air.fit)

plot.forecast(air.forecast)

You can download the fascinating paper from the Analytics NCSU Website http://analytics.ncsu.edu/sesug/2008/ST-146.pdf

About the Author-

Sam Croker has a MS in Statistics from the University of South Carolina and has over ten years of experience in analytics.   His research interests are in time series analysis and forecasting with focus on stream-flow analysis.  He is currently using SAS, R and other analytical tools for fraud and abuse detection in Medicare and Medicaid data. He also has experience in analyzing, modeling and forecasting in the finance, marketing, hospitality, retail and pharmaceutical industries.

Interview John F Moore CEO The Lab

Social Media Landscape

Here is an interview with John F Moore, social media adviser,technologist and founder and CEO of The Lab.

Ajay-  The internet seems to be crowded by social media experts with everyone who spends a lot of time on the internet claiming to be one? How  does a small business owner on a budget distinguish for the correct value proposition that social media can give them. 

John- You’re right.  It seems like everytime I turn around I bump into more social media “experts”.  The majority of these self-proclaimed experts are not adding a great deal of value.  When looking to spend money for help ask the person a few questions about their approach. Things you should be hearing include:

  • The expert should be seeking to fully understand your business, your goals, your available resources, etc..
  • The expert should be seeking to understand current management thinking about social media and related technologies.

If the expert is purely focused on tools they are the wrong person.  Your solution may require tools alone but they cannot know this without first understanding your business.

Ajay- Facebook has 600 million people, with people preferring to play games and connect to old acquaintances rather than use social media for tangible career or business benefit..

John- People are definitely spending time playing games, looking at photos, and catching up with old friends.  However, there are many businesses seeing real value from Facebook (primarily by tying it into their e-mail marketing and using coupons and other incentives).  For example, I recently shared a small case study (http://thejohnfmoore.com/2010/10/07/email-social-media-and-coupons-makes-the-cfo-smile/) where a small pet product company achieved a 22% bump in monthly revenue by combining Facebook and coupons together.  In fact,45% of this bump in revenue came from new clients.  Customer acquisition and increased revenue were accomplished by using Facebook for their business.
Ajay-  How does a new social media convert (individual) go on selecting communities to join (Facebook,Twitter,Linkedin,Ning, Ping,Orkut, Empire Avenue etc etc.
How does a small business owner take the same decision.

John- It always starts with taking the time to define your goals and then determine how much time and effort you are willing to invest.  For example:
  • LinkedIn. A must have for individuals as it is one of the key social networking communities for professional networking.  Individuals should join groups that are relevant to their career and invest an hour a week.  Businesses should ensure they have a business profile completed and up to date.
  • Facebook can be a challenge for anyone trying to walk the personal/professional line.  However, from a business standpoint you should be creating a Facebook page that you can use to compliment your other marketing channels.
  • Twitter.  It is a great network to learn of, to meet, and to interact with people from around the world.  I have met thousands of interesting people, many of which I have had the pleasure to meet with in real life.  Businesses need to invest in listening on twitter to determine if their customers (current or potential) or competitors are already there discussing them, their marketplace, or their offerings.
In all cases I would encourage businesses to setup social media accounts on LinkedIn, Facebook, Twitter, YouTube, and Flickr.  You want to ensure your brand is protected by owning these accounts and ensuring at least the base information is accurate.
Ajay- Name the top 5 points that you think make a social media community successful.  What are the top 5 points for a business to succeed in their social media strategy.

John-
  • Define your goals up front.  Understand why you are building a community and keep this goal in mind.
  • Provide education.  Ideally you want to become a thought leader in your space, the trusted resource that people can turn to even if they are not using your product or services today.
  • Be honest.  We all make mistakes.  When you do, be honest with your community and engage them in any fall-out that may be coming out of your mistake.
  • Listen to them.  Use platforms like BubbleIdeas to gather feedback on what your community is looking for from the relationship.
  • Measure.  Are you on track with your goals?  Do your goals need to change?
Ajay- What is the unique value proposition that “The Lab” offers

John- The Lab understands the strategic importance of leveraging social media, management and leadership best practices, and our understanding of local government and small and medium business to help people in these areas achieve their goals.  Too many consultants come to the table with a predefined solution that really misses the mark as it lacks understanding of the client’s goals.
Ajay-  What is “CityCamp in Boston” all about.

John- CityCamp is a FREE unconference focused on innovation for municipal governments and community organizations (http://www.citycampboston.org/what-is-citycamp-boston/).  It brings together politicians, local municipal employees, citizens, vendors, developers, and journalist to build a common understanding of local government challenges and then works to deliver measurable outcomes following the event.  The key is the focus on change management, driving change as opposed to just in the moment education.
Biography-

John F Moore is the Founder and CEO of The Lab (http://thelabinboston.com).  John has experience working with local governments and small and medium business owners to achieve their goals.  His experience with social media strategies, CRM, and a plethora of other solutions provides immense value to all of our clients.   He has built engineering organizations, learned sales and marketing, run customer service teams, and built and executed strategies for social media thought leadership and branding.  He is also a prolific blogger as you can see by checking out his blog at http://thejohnfmoore.com.

Interview Dean Abbott Abbott Analytics

Here is an interview with noted Analytics Consultant and trainer Dean Abbott. Dean is scheduled to take a workshop on Predictive Analytics at PAW (Predictive Analytics World Conference)  Oct 18 , 2010 in Washington D.C

Ajay-  Describe your upcoming hands on workshop at Predictive Analytics World and how it can help people learn more predictive modeling.

Refer- http://www.predictiveanalyticsworld.com/dc/2010/handson_predictive_analytics.php

Dean- The hands-on workshop is geared toward individuals who know something about predictive analytics but would like to experience the process. It will help people in two regards. First, by going through the data assessment, preparation, modeling and model assessment stages in one day, the attendees will see how predictive analytics works in reality, including some of the pain associated with false starts and mistakes. At the same time, they will experience success with building reasonable models to solve a problem in a single day. I have found that for many, having to actually build the predictive analytics solution if an eye-opener. Seeing demonstrations show the capabilities of a tool, but greater value for an end-user is the development of intuition of what to do at each each stage of the process that makes the theory of predictive analytics real.

Second, they will gain experience using a top-tier predictive analytics software tool, Enterprise Miner (EM). This is especially helpful for those who are considering purchasing EM, but also for those who have used open source tools and have never experienced the additional power and efficiencies that come with a tool that is well thought out from a business solutions standpoint (as opposed to an algorithm workbench).

Ajay-  You are an instructor with software ranging from SPSS, S Plus, SAS Enterprise Miner, Statistica and CART. What features of each software do you like best and are more suited for application in data cases.

Dean- I’ll add Tibco Spotfire Miner, Polyanalyst and Unica’s Predictive Insight to the list of tools I’ve taught “hands-on” courses around, and there are at least a half dozen more I demonstrate in lecture courses (JMP, Matlab, Wizwhy, R, Ggobi, RapidMiner, Orange, Weka, RandomForests and TreeNet to name a few). The development of software is a fascinating undertaking, and each tools has its own strengths and weaknesses.

I personally gravitate toward tools with data flow / icon interface because I think more that way, and I’ve tired of learning more programming languages.

Since the predictive analytics algorithms are roughly the same (backdrop is backdrop no matter which tool you use), the key differentiators are

(1) how data can be loaded in and how tightly integrated can the tool be with the database,

(2) how well big data can be handled,

(3) how extensive are the data manipulation options,

(4) how flexible are the model reporting options, and

(5) how can you get the models and/or predictions out.

There are vast differences in the tools on these matters, so when I recommend tools for customers, I usually interview them quite extensively to understand better how they use data and how the models will be integrated into their business practice.

A final consideration is related to the efficiency of using the tool: how much automation can one introduce so that user-interaction is minimized once the analytics process has been defined. While I don’t like new programming languages, scripting and programming often helps here, though some tools have a way to run the visual programming data diagram itself without converting it to code.

Ajay- What are your views on the increasing trend of consolidation and mergers and acquisitions in the predictive analytics space. Does this increase the need for vendor neutral analysts and consultants as well as conferences.

Dean- When companies buy a predictive analytics software package, it’s a mixed bag. SPSS purchasing of Clementine was ultimately good for the predictive analytics, though it took several years for SPSS to figure out what they wanted to do with it. Darwin ultimately disappeared after being purchased by Oracle, but the newer Oracle data mining tool, ODM, integrates better with the database than Darwin did or even would have been able to.

The biggest trend and pressure for the commercial vendors is the improvements in the Open Source and GNU tools. These are becoming more viable for enterprise-level customers with big data, though from what I’ve seen, they haven’t caught up with the big commercial players yet. There is great value in bringing both commercial and open source tools to the attention of end-users in the context of solutions (rather than sales) in a conference setting, which is I think an advantage that Predictive Analytics World has.

As a vendor-neutral consultant, flux is always a good thing because I have to be proficient in a variety of tools, and it is the breadth that brings value for customers entering into the predictive analytics space. But it is very difficult to keep up with the rapidly-changing market and that is something I am weighing myself: how many tools should I keep in my active toolbox.

Ajay-  Describe your career and how you came into the Predictive Analytics space. What are your views on various MS Analytics offered by Universities.

Dean- After getting a masters degree in Applied Mathematics, my first job was at a small aerospace engineering company in Charlottesville, VA called Barron Associates, Inc. (BAI); it is still in existence and doing quite well! I was working on optimal guidance algorithms for some developmental missile systems, and statistical learning was a key part of the process, so I but my teeth on pattern recognition techniques there, and frankly, that was the most interesting part of the job. In fact, most of us agreed that this was the most interesting part: John Elder (Elder Research) was the first employee at BAI, and was there at that time. Gerry Montgomery and Paul Hess were there as well and left to form a data mining company called AbTech and are still in analytics space.

After working at BAI, I had short stints at Martin Marietta Corp. and PAR Government Systems were I worked on analytics solutions in DoD, primarily radar and sonar applications. It was while at Elder Research in the 90s that began working in the commercial space more in financial and risk modeling, and then in 1999 I began working as an independent consultant.

One thing I love about this field is that the same techniques can be applied broadly, and therefore I can work on CRM, web analytics, tax and financial risk, credit scoring, survey analysis, and many more application, and cross-fertilize ideas from one domain into other domains.

Regarding MS degrees, let me first write that I am very encouraged that data mining and predictive analytics are being taught in specific class and programs rather than as just an add-on to an advanced statistics or business class. That stated, I have mixed feelings about analytics offerings at Universities.

I find that most provide a good theoretical foundation in the algorithms, but are weak in describing the entire process in a business context. For those building predictive models, the model-building stage nearly always takes much less time than getting the data ready for modeling and reporting results. These are cross-discipline tasks, requiring some understanding of the database world and the business world for us to define the target variable(s) properly and clean up the data so that the predictive analytics algorithms to work well.

The programs that have a practicum of some kind are the most useful, in my opinion. There are some certificate programs out there that have more of a business-oriented framework, and the NC State program builds an internship into the degree itself. These are positive steps in the field that I’m sure will continue as predictive analytics graduates become more in demand.

Biography-

DEAN ABBOTT is President of Abbott Analytics in San Diego, California. Mr. Abbott has over 21 years of experience applying advanced data mining, data preparation, and data visualization methods in real-world data intensive problems, including fraud detection, response modeling, survey analysis, planned giving, predictive toxicology, signal process, and missile guidance. In addition, he has developed and evaluated algorithms for use in commercial data mining and pattern recognition products, including polynomial networks, neural networks, radial basis functions, and clustering algorithms, and has consulted with data mining software companies to provide critiques and assessments of their current features and future enhancements.

Mr. Abbott is a seasoned instructor, having taught a wide range of data mining tutorials and seminars for a decade to audiences of up to 400, including DAMA, KDD, AAAI, and IEEE conferences. He is the instructor of well-regarded data mining courses, explaining concepts in language readily understood by a wide range of audiences, including analytics novices, data analysts, statisticians, and business professionals. Mr. Abbott also has taught both applied and hands-on data mining courses for major software vendors, including Clementine (SPSS, an IBM Company), Affinium Model (Unica Corporation), Statistica (StatSoft, Inc.), S-Plus and Insightful Miner (Insightful Corporation), Enterprise Miner (SAS), Tibco Spitfire Miner (Tibco), and CART (Salford Systems).