A review of R for Cloud Computing is on at Journal of Statistical Software
This is a lively book on a timely topic – or rather, a pair of topics, as the book is as much about R as it is on cloud computing. It should prove useful for those interested in the confluence of the two subject areas
The book features a number of interviews with prominent figures in data science. Though arguably a bit out of place, I believe that most readers will find them interesting and worth inclusion. This book should be of interest to anyone who is new to data storage and analysis in the cloud, especially with R, and even veteran users will find something new here and there.
and areas where the author needs to work much much harder
The book aims to provide step-by-step instructions for painlessly and quickly getting the novice user into the cloud. It does succeed in this for the most part, but any such effort will not be 100% painless after all. Readers who lack background in the cloud may feel overwhelmed at times at the beginning, given all the possible choices and myriad terms. In fact, some terms seem to be undefined, and there is no index (though there is a good bibliography). The figures are inline rather than referenced via numbers, and in some cases they are rather distant from the associated text. The font size in the figures may be too small for comfortable reading for some people.
Read the full review here http://www.jstatsoft.org/v66/b04/paper
and get a look at the full book here http://www.springer.com/book/9781493917013
Many thanks to the encouragement from Dr Matloff.
I may have been forced to drop out of U Tennessee Knoxville MS Stats on health grounds in 2010 but I get by with hard work and chutzpah.
In a previous post, I said that many corporate are trying to benefit from the demand for data science as applied to their sector or company but not many are doing enough to improve the supply of data scientists.
In anecdotal arguments for students In India and USA , many have argued that many training companies are charging exorbitant amounts and misguided promises to essentially teach tools and techniques but not the essential analytical mindset for splicing and dicing of data as well as enough information to reach balance between the three skills for data scientists- statistics, programming and business perspective.
Added to this, many people building tools for data scientists have not worked in data science consulting them self but are addicted to one platform or product due to commercial or intellectual compulsions.
Here is what I think could be a supply side solution to the problem of demand of data scientists hindering actual data science benefits to humanity regardless of commercial or social sectors.
- Build up a pool of curated best practice training
- Get them validated and verified across different business sectors by industry experts
- Add hardware or cloud training to software training
- Offer them on accessible platforms like mobile, tablet and web
- Offer them on accessible languages like Spanish Swahili Chinese Arabic as well
- Gamify some of the content to make it interesting, basically start creating data science hackers at an earlier age than just post graduate students
- Tie up with industry to offer internships that are fair balanced and demand equal commitment
- Tie in soft skill training for better professionalism
- Offer all this for free but use data generated for improving this not only on a human intervention basis but computer adaptive training and testing
- Monetize only after you reach a huge scale not prematurely
- Make it interactive using videos, 15 minute weekly personalized help on Skype from support, webinars but capture data continuously to drive engagement metrics
Do you want to just make money on the demand (uncertain) for data science but do you want to make more money on the supply side of data science too?
People all over tell me how big the demand for data science is, and how much of a shortage of data scientists they see.
and a new survey by MIT (sponsored by SAS) points out to this looming shortage between the demand and supply of data scientists (side note- still surprised why companies insist on registration in this era of OpenID for download of white papers like these
The Sloan Paper is very nice and points to this- the image above is from it . You can look here
People like IBM, Oracle, SAP, HP, SAS , Revolution Analytics, RStudio , Cloudera, Continiuum Analytics are focussing more on capturing on the demand for data science as it is very lucrative. They do so by providing enough resources in marketing to help explain their offerings, sponsoring though leaders , white papers. Training remains a back end activity- considered non critical to a software vendor in data science. Quite disappointingly these training are often expensive and lack customization for international audiences. Why not capture your training on videos and sell them for $20 , dear people.
But here lies the catch, if you train data scientists in your platform early on , you own them for life.
Perhaps software vendors can focus on their core competencies of data science demand satisfaction and invest in training collateral too.
Some thoughts on this-
- People need a human touch. Not everything can be automated via apps, videos, quizes. That is partly why Coursera has a low pass rate.
- Demand for data science teachers is even more tough than demand for plain data scientists
- If you train people in your platform they champion that software wherever they go
- Increasingly people want to be trained in multiple software to hedge risk to their career.
- Independent cross platform trainers are even fewer than trainers who can train in one language or data science platform
- Most training tends to be in English including MOOCs. This leaves out a big chunk of humanity who could have helped create the necessary data scientists including Chinese Arabic and Spanish speaking people
- Governments have helped improve literacy but are ignorant on data science skill shortage. Partly because Governments find it even more tough to attract people skilled enough who can make data science policy.
- The country with the best and maximum number of data scientists would win the race in the next few decades or atleast have a superb edge in innovation
- Ask not what you can get from data science, ask what you can do to make more copies of yourself as a superb data scientist. This goes out to the data science celebrities
- Machine learning continues to be woefully under taught in colleges especially in Asia (and I suspect in USA)
- Many many Universities struggle to keep professors with tenure for life, updated for skills and new languages pertinent to data science
- Some parts of the data science ecosystem remain prone to corruption and self centred tactics including influencing data science writers or analysts . The sum of many local optima (vendors in software or training education) is not a global optima (for the industry, country, humanity)
everybody wants to use data science but nobody wants to help create more data scientists. do you agree or do you disagree?