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The Little Book of DATA SCIENCE, 2012 Edition, by Noreen Burlingame
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NOTE: The publisher is continuing to make the 2012 edition of this book available for all those who might want it. However, we wish to let all potential buyers know that the new, updated 2013 edition of the work, re-titled A SIMPLE INTRODUCTION TO DATA SCIENCE, is now (as of November 6th, 2012) available from Amazon in both paper and Kindle editions.
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In THE LITTLE BOOK OF DATA SCIENCE, 2012 EDITION, Noreen Burlingame provides an elegant, concise, 15,000-word introduction to all aspects of the field. Burlingame addresses the various skills required, the key steps in the data science process, software technology related to the effective practice of data science, and the best rising academic programs for training in the field.
To say the least, demand is high for "data scientists" - the rock stars of the rising generation in analytics: Big Data analytics. Ninety-seven percent of 2012 Master of Science in Analytics (MSA) graduates from North Carolina State's Institute for Advanced Analytics received one or more offers of employment before graduation, at an average base salary of $89,300.
Vendors such as Cloudera, Hortonworks, MapR and others are commercializing Big Data software technologies, in effect making them easier to deploy and manage. The key challenge for organizations, therefore, is be to find the right talent to analyze the data.
According to Forrester Research's James Kobielus: "[Organizations will] have to ... hire statistical modelers, text mining professionals, people who specialize in sentiment analysis."
These people - who must also be skilled in developing adaptive software models, data cleaning and mining, and related tasks - are in short supply. By 2018, the United States alone could face a shortage of 140,000 to 190,000 professionals with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions. This is all per a recent McKinsey and Company estimate.
In a no-nonsense, concise, and elegantly-clear manner, Burlingame lays out the skills required, and all elements in the process of data science. Her book is the ideal introduction.
CONTENTS - Introduction * Data Science in Brief * What is Big Data? * Hadoop * Data Management * Data Cleaning * Data Modeling for Unstructured Data * Predictive Analysis * Creativity and Intuition (or Posing the Right Question, at the Right Time, for the Right Data) * Data Visualization (or Telling the Story) * Cassandra, Solr and DataStax Enterprise * Vendors * Academic Programs
ABOUT THE AUTHOR
Noreen Burlingame is a leading IT consultant. Her publications include New Street's THE LITTLE BOOK OF BIG DATA, 2012 EDITION and HYBRID CLOUD ESSENTIALS.
- Sales Rank: #904974 in eBooks
- Published on: 2012-04-03
- Released on: 2012-04-03
- Format: Kindle eBook
Most helpful customer reviews
3 of 3 people found the following review helpful.
Good content but laborious to read
By Richard
I did finish the book because the content itself was well selected and the topic fascinating.
The author also did a good job of breaking down each aspect of Data science and explaining them in a logical sequence.
The book fall shorts in readability. Certain chapters (like the one on data modeling) are 80% composed of quotes from industry experts. While I appreciate the approach of collecting and sharing industry views rather than the author's own opinion, I think it could have been handled differently. The purpose of these little books is to provide a quick introduction to non domain experts, so a chapter full of jargon from industry experts is not helpful.
An excerpt:
Dichmann posits that the data model, if viewed as "an abstraction of the physical representation of the database structures," clearly declines in value in face of the schema-less [data] or the constantly changing schemes. "But, if it is the abstraction of the conceptual representation of the information, we see a rise in importance. "
The above paragraph requires at least two reads. Other chapters that rely less on quotes are generally clear and better organized. My recommendation to authors of these little books would be to maintain their focus on aggregating industry expertise, but utilize more paraphrasing and less quoting. Their books will read better for it.
Edit: Typos from the original phone written review cleaned up.
3 of 3 people found the following review helpful.
On point, enjoyable lecture
By Amazon Customer
It took me about 1.5 hours to go through the whole material. Well written and on point.
I appreciated the clear guidelines for a systematic enterprise approach.
Valuable bullet points on issues such as data cleansing and data modeling.
After 30 years and more of Data Hype, there is still no magic rabbit in this area:
80% of work will continue to be about shoveling in the Dirty Data stables.
Data modeling continues to be a matter of taste.
It could be a very useful lecture/ gift for those in corner offices.
1 of 1 people found the following review helpful.
I got better information from Wiki
By LeeTrue
Don't buy this book unless you have money to burn. It was mostly just information that can be gleaned from a quick search on the internet. There was no in depth look at any of the concepts that make up data science..
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