Monthly Archives: August 2013

A Great Introduction to Data Science and Big Data.

SimpleIntroDataScienceFor those of you foraying into the world of Big Data (BD) and Data Science (DS), it can be challenging to find a single resource to help paint a meaningful high-level picture of what this stuff is all about.  Personally, I always like to start with a 30,000 foot view of the challenge or endeavor before me.  I find that it helps frame the important concepts better enabling the consumption and digestion of the details to follow.  This tiered approach is especially important to the disciplines of BD and DS.  A book I read in less than 45 minutes completely satisfied my 30,000 foot criteria.  The key to this book’s success is the organic progression of each chapter, the breadth of topics introduced and its overall brevity.  The authors (in a mere 65 pages) walk you through a summary of data science, a working definition of big data, the new technologies necessitated by big data, aspects of the data analytics lifecycle, key characteristics of a data scientist and approaches to effective communication as a data scientist.  If you have an interest in DS or BD, get your hands on this book.  It provides a simple overview of the complicated disciplines of data science and big data.

Louis V. Frolio

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The Inaugural Post.


Welcome to DataTechBlog. My name is Louis and I am a data professional.  I espouse all data: big, small, structured, semi-structured, unstructured, dark, sensor, I do not discriminate. For the past 20 years I have gained expertise in many aspects of data including, analytics, management, operations, architecture, technology, administration, and engineering.
Over the past several years the terms “data science” and “big data” have become commonplace. My goal is to help other data and database professionals learn about the emerging disciplines of data science and big data analytics. Here you will find tutorials, how to’s and topic discussions on various dimensions of these disciplines including data mining, exploratory data analysis, data prep/scrubbing, data engineering, tools (e.g. Greenplum, R, MADlib, Hadoop, Hive, Pig, etc.), visualizations, and much more.
Coming from a traditional data architecture background, I can help bridge the gap for people who work with RDBMS technologies who are interested in learning more about data science and big data analytics.

Regards, Louis.


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