I always liked Citrix products when I was a member of the Technical Advisory Board at the University of Tennessee. I especially liked enabling SAS software , R software, Matlab software , ONLY from a browser.
Data Mining through cloud computing, yes University of Tennessee’s analytics server http://analytics.utk.edu was way ahead in 2009- all these softwares at one portal no software needed on your own PC, simply upload data and work on any analytics software.
Here is a nice citrix webinar on managing Time (so you can read more webinars! nah. I think Youtube live streaming events with interactive question and answers is the way of the future while webinars are for Baby Boomers- you can do a test and control experiment yourself if you are in the webinar business. its a web2.oinar)
Interruptions are productivity killers – between email, phone calls and back-to-back meetings, how do you find time to work on your top priorities?
Join top time-management guru Kent Curtis and learn how to stop “living in your inbox” and start prioritising tasks, messages and appointments according to what is most important.
This webinar takes the best principles from FranklinCovey’s world-class productivity training and teaches you how to apply them while using Microsoft Outlook as your scheduling tool.
Attend this interactive, one-hour webinar to:
Stay focused every day with a reliable planning system utilising Microsoft Outlook.
Control competing demands such as email, voice mail, meetings and interruptions.
Apply a planning process that gets better business results.
Reduce stress by eliminating low priority activities and distractions.
Register for the Webinar
Please forward this to colleagues who might be interested in learning more.
If you do a Google search for Data Mining Blog- for the past several years one Blog will come on top. data mining blog – Google Search http://bit.ly/kEdPlE
To honor 5 years of Sandro Saitta’s blog (yes thats 5 years!) , we cover an exclusive interview with him where he reveals his unique sauce for cool techie blogging.
Ajay- Describe your journey as a scientist and data miner, from early experiences, to schooling to your work/research/blogging.
Sandro- My first experience with data mining was my master project. I used decision tree to predict pollen concentration for the following week using input data such as wind, temperature and rain. The fact that an algorithm can make a computer learn from experience was really amazing to me. I found it so interesting that I started a PhD in data mining. This time, the field of application was civil engineering. Civil engineers put a lot of sensors on their structure in order to understand how they behave. With all these sensors they generate a lot of data. To interpret these data, I used data mining techniques such as feature selection and clustering. I started my blog, Data Mining Research, during my PhD, to share with other researchers.
I then started applying data mining in the stock market as my first job in industry. I realized the difference between image recognition, where 99% correct classification rate is state of the art, and stock market, where you’re happy with 55%. However, the company ambiance was not as good as I thought, so I moved to consulting. There, I applied data mining in behavioral targeting to increase click-through rates. When you compare the number of customers who click with the ones who don’t, then you really understand what class imbalance mean. A few months ago, I accepted a very good opportunity at SICPA. I’m looking forward to resolving new challenges there.
Ajay- Your blog is the top ranked blog for “data mining blog”. Could you share some tips on better blogging for analytics and technical people
Sandro- It’s always difficult to start a blog, since at the beginning you have no reader. Writing for nobody may seem stupid, but it is not. By writing my first posts during my PhD I was reorganizing my ideas. I was expressing concepts which were not always clear to me. I thus learned a lot and also improved my English level. Of course, it’s still not perfect, but I hope most people can understand me.
Next come the readers. A few dozen each week first. To increase this number, I then started to learn SEO (Search Engine Optimization) by reading books and blogs. I tested many techniques that increased Data Mining Research visibility in the blogosphere. I think SEO is interesting when you already have some content published (which means not at the very beginning of your blog). After a while, once your blog is nicely ranked, the main task is to work on the content of the blog. To be of interest, your content must be particular: original, informative or provocative for example. I also had the chance to have a good visibility thanks to well-known people in the field like Kevin Hillstrom, Gregory Piatetsky-Shapiro, Will Dwinnell / Dean Abbott, Vincent Granville, Matthew Hurst and many others.
Ajay- Whats your favorite statistical software and what are the various softwares that you have worked with. Could you compare and contrast these software as well.
Sandro- My favorite software at this point is SAS. I worked with it for two years. Once you know the language, you can perform ETL and data mining so easily. It’s also very fast compared to others. There are a lot of tools for data mining, but I cannot think of a tool that is as powerful as SAS and, in the same time, has a high-level programming language behind it.
I also worked with R and Matlab. R is very nice since you have all the up-to-date data mining algorithms implemented. However, working in the memory is not always a good choice, especially for ETL. Matlab is an excellent tool for prototyping. It’s not so fast and certainly not done for ETL, but the price is low regarding all the possibilities for data mining. According to me, SAS is the best choice for ETL and a good choice for data mining. Of course, there is the price.
Ajay- What are your favorite techniques and training resources for learning basics of data mining to say statisticians or business management graduates.
Sandro- I’m the kind of guy who likes to read books. I read data mining books one after the other. The fact that the same concepts are explained differently (and by different people) helps a lot in learning a topic like data mining. Of course, nothing replaces experience in the field. You can read hundreds of books, you will still not be a good practitioner until you really apply data mining in specific fields. My second choice after books is blogs. By reading data mining blogs, you will really see the issues and challenges in the field. It’s still not experience, but we are closer. Finally, web resources and networks such as KDnuggets of course, but also AnalyticBridge and LinkedIn.
Ajay- Describe your hobbies and how they help you ,if at all in your professional life.
Sandro- One of my hobbies is reading. I read a lot of books about data mining, SEO, Google as well as Sci-Fi and Fantasy. I’m a big fan of Asimov by the way. My other hobby is playing tennis. I think I simply use my hobbies as a way to find equilibrium in my life. I always try to find the best balance between work, family, friends and sport.
Ajay- What are your plans for your website for 2011-2012.
Sandro- I will continue to publish guest posts and interviews. I think it is important to let other people express themselves about data mining topics. I will not write about my current applications due to the policies of my current employer. But don’t worry, I still have a lot to write, whether it is technical or not. I will also emphasis more on my experience with data mining, advices for data miners, tips and tricks, and of course book reviews!
Standard Disclosure of Blogging- Sandro awarded me the Peoples Choice award for his blog for 2010 and carried out my interview. There is a lot of love between our respective wordpress blogs, but to reassure our puritan American readers- it is platonic and intellectual.
About Sandro S-
Sandro Saitta is a Data Mining Research Engineer at SICPA Security Solutions. He is also a blogger at Data Mining Research (www.dataminingblog.com). His interests include data mining, machine learning, search engine optimization and website marketing.
Parallel Computing Toolbox™ lets you solve computationally and data-intensive problems using multicore processors, GPUs, and computer clusters. High-level constructs—parallel for-loops, special array types, and parallelized numerical algorithms—let you parallelize MATLAB® applications without CUDA or MPI programming. You can use the toolbox with Simulink® to run multiple simulations of a model in parallel.
The toolbox provides eight workers (MATLAB computational engines) to execute applications locally on a multicore desktop. Without changing the code, you can run the same application on a computer cluster or a grid computing service (using MATLAB Distributed Computing Server™). You can run parallel applications interactively or in batch.
The gputools package by Buckner provides several common data-mining algorithms which are implemented using a mixture of nVidia‘s CUDA langauge and cublas library. Given a computer with an nVidia GPU these functions may be substantially more efficient than native R routines. The rpud package provides an optimised distance metric for NVidia-based GPUs.
The cudaBayesreg package by da Silva implements the rhierLinearModel from the bayesm package using nVidia’s CUDA langauge and tools to provide high-performance statistical analysis of fMRI voxels.
The rgpu package (see below for link) aims to speed up bioinformatics analysis by using the GPU.
The magma package provides an interface to the hybrid GPU/CPU library Magma (see below for link).
The gcbd package implements a benchmarking framework for BLAS and GPUs (using gputools).
I tried to search for SAS and GPU and SPSS and GPU but got nothing. Maybe they would do well to atleast test these alternative hardwares-
Also see Matlab on GPU comparison for the product Jacket vs Parallel Computing Toolbox
There is no best software- they are just optimized for various constraints and tangible as well as intangible needs as defined for users.
There is no best software- they are just optimized for various constraints and tangible as well as intangible needs as defined for users. ( Image below Citation- support.sas.com )
Price in products is defined as Demand divided by Supply. Sometimes this is Expected Demand over Expected Supply ( see Oil Prices) Everyone grumbles over prices but we pay what we think is fair. ( citation http://bm2.genes.nig.ac.jp/RGM2/index.php?ctv=Survival
Prices in services are defined by value creation as well- Value= Benefit Divided by Cost Benefits are tangible as in how much money it saves in fraud as well as intangible – how easy it is to start using JMP versus R Commander Costs are Tangible- How much do we have to pay using our cheque book for this annual license or perpetual license or one time license or maintain contract or application support.Intangible costs are how long I have to hold the phone while talking to customer support and how much time it takes me to find the best solution using the website on my own without a sales person bothering me with frequent calls. (citation- http://academic.udayton.edu/gregelvers/psy216/spss/graphs.htm#tukey
All sales people ( especially in the software industry) spam you with frequent calls, email reminders and how their company is the best company ever with the best software in the history of mankind. That is their job and they are pushed by sales quotas and pulled by their own enthusiasm to sell more to same customer. If you ever bought three licences and found out you just needed two at the end of the year- forgive the salesman. As Arthur Miller said’ All Salesmen are Dreamers (Citation of STATA graph below http://www.ats.ucla.edu/stat/Stata/library/GraphExamples/code/grbartall.htm)
Technology moves faster than you can say Jackie Robinson. and it is getting faster. Research and Development ( R and D) will always move slower than the speed at which Marketing thinks they can move. See http://www.dilbert.com for more insights on this. You either build a Billion Dollar in house lab ( like Palo Alto – remember) or you go for total outsourcing (like semi conductors and open source do). Or you go for a mix and match. ( Citation- http://people.sc.fsu.edu/~burkardt/html/matlab_graphics/matlab_graphics.html )
Based on the above parameters the best statistical software for 2009 continues to be the software that uses a mixture of Genetic Algorithms, Time Series Based Regression and Sampling – it is the software that runs in the head of the statistical /mathematical / customer BRAIN
DECISION STATS 