What Every Manager Should Know About Big Data and Data Science

Noreen Burlingame,PH D Robert Masters, PH D,Lars Nielsen

What Every Manager Should Know About Big Data and Data Science
Format
Paperback
Publisher
New Street Communications, LLC
Published
7 March 2016
Pages
128
ISBN
9780692662090

What Every Manager Should Know About Big Data and Data Science

Noreen Burlingame,PH D Robert Masters, PH D,Lars Nielsen

The need for precise, actionable, real-time Business Intelligence (BI) lies at the heart of the role of Big Data in modern commerce. In turn, the art of Data Science lies at the nexus of Big Data and BI, providing the essential methods by which BI can be extracted from Big Data’s great black mass of constantly flowing, unstructured information. This combination has a created a new profession: an elite and specialized class of highly-compensated professionals specially skilled at data cleaning, analysis, and visualization. We call them Data Scientists, and their evolving role in the organization is one about which managers must have a clear understanding.

As shown in WHAT EVERY MANAGER SHOULD KNOW ABOUT BIG DATA AND DATA SCIENCE, integrating data scientists (and the general practice of Data Science) with the organization is often a delicate process involving the redefinition of traditional roles within the enterprise as well as dealing with issues of territoriality amongst peers.

To accomplish this task, managers need a firm knowledge of the what and whys of Data Science, the specific BI needs it is uniquely positioned to service, and the processes by which it functions.

Such is the knowledge this important book provides.

CONTENTS:

Preface
The Vital Triangle
What Is Big Data? Creativity and Intuition
Making Something Out of Nothing
Introducing Data Science to the Enterprise
Avoiding Friction Between Old & New: Dealing with the TCQ Triangle
The Evolving Role of the Data Scientist
Data Science Ethics and Privacy Concerns
The Art of Seeing Things Data Management Artificial Intelligence, Machine-Learning, and Deep-Learning in Data Science
Data Curation and the Tribal Knowledge Problem
Data Cleaning
Data Modeling for Unstructured Data
Predictive Analysis
A Bit More On Data Visualization
Cassandra Hadoop
Orchestrating Change in the Workplace

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