Applying Analytics: A Practical Introduction

Overview

Newcomers to quantitative analysis need practical guidance on how to analyze data in the real world yet most introductory books focus on lengthy derivations and justifications instead of practical techniques. Covering the technical and professional skills needed by analysts in the academic, private, and public sectors, Applying Analytics: A Practical Introduction systematically teaches novices how to apply algorithms to real data and how to recognize potential pitfalls. It offers one of the first textbooks for ...

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Overview

Newcomers to quantitative analysis need practical guidance on how to analyze data in the real world yet most introductory books focus on lengthy derivations and justifications instead of practical techniques. Covering the technical and professional skills needed by analysts in the academic, private, and public sectors, Applying Analytics: A Practical Introduction systematically teaches novices how to apply algorithms to real data and how to recognize potential pitfalls. It offers one of the first textbooks for the emerging first course in analytics.

The text concentrates on the interpretation, strengths, and weaknesses of analytical techniques, along with challenges encountered by analysts in their daily work. The author shares various lessons learned from applying analytics in the real world. He supplements the technical material with coverage of professional skills traditionally learned through experience, such as project management, analytic communication, and using analysis to inform decisions. Example data sets used in the text are available for download online so that readers can test their own analytic routines.

Suitable for beginning analysts in the sciences, business, engineering, and government, this book provides an accessible, example-driven introduction to the emerging field of analytics. It shows how to interpret data and identify trends across a range of fields.

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Editorial Reviews

From the Publisher
"The book is well written and accessible. The analytics experience of the author shines through in his discussion and in valuable advice … The examples are chosen to demonstrate the technique being introduced and do so well. … the book is an easy-to-read introduction to analytics, and is especially suited for those focusing on datasets limited to fewer variables."
The American Statistician, May 2014
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Product Details

  • ISBN-13: 9781466557185
  • Publisher: Taylor & Francis
  • Publication date: 7/5/2013
  • Pages: 290
  • Product dimensions: 6.40 (w) x 9.30 (h) x 0.80 (d)

Meet the Author

Dr. Evan S. Levine is an analytics professional specializing in homeland security and counterterrorism. His work has been published in Science, Risk Analysis, Analytics magazine, and several other peer-reviewed journals. He is a member of the American Association for the Advancement of Science (AAAS), the Institute For Operations Research and Management Science (INFORMS), and the Society for Risk Analysis (SRA). He earned a Ph.D. in astrophysics from the University of California, Berkeley, and a Master of Advanced Study (MASt) in mathematics from Cambridge University.

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Table of Contents

Introduction
What Is Analytics?
What Makes an Analyst?
How Is Analytics Done?
Analytics in Groups
Onward!

Elements
Introduction
Stevens’ Levels of Measurement
Continuous and Discrete Scales
Attributes
Data Sets

Lists
Introduction
Two Example Lists
Elementary Analyses
Additional Techniques
The Normal Distribution and the Height List
Hypothesis Testing and Frequency Distributions
Significance of Results
Discarding Data from Normally Distributed Data Sets

Uncertainty and Error
Introduction
Definitions, Descriptions, and Discussion
Quantification of Uncertainty
Propagation of Uncertainty
Uncertainty in the Mean
Why Is This So Unsettling?

1-Dimensional Data Sets
Introduction
Element Spacing
Three Example 1-Dimensional Data Sets
Interpolation
Smoothing
Fitting 1-Dimensional Data Sets
Increasing Local Contrast
Frequency Analysis
Filtering
Wavelet Analysis (Advanced Topic)

Related Lists and 1-Dimensional Data Sets
Introduction
Jointly Measured Data Sets
Example 1-Dimensional Linked Data Sets
Correlation
Ratios
Fitting Related Lists and 1-Dimensional Data Sets
Correlation Functions

2-Dimensional Data Sets
Introduction
Example 2-Dimensional Data Sets
Restructuring Data Sets
Analogs of 1-Dimensional Techniques
Analogs of 1-Dimensional Techniques for Related 2-Dimensional Data Sets
Higher-Dimensional Data Sets

Unstructured Data Sets
Introduction
Extracting Structure from Unstructured Data
Text Mining
Bayesian Techniques

Prescriptive Decision Analysis
Introduction
Understanding the Decision Environment
Determining and Structuring Objectives
Identifying Alternatives
Utility Functions
Decision Trees
Game Theory
Negotiations

Project Management
Introduction
How Analytic Projects Are Typically Managed
A Methodology for Project Management
Hastening Project Completion
Project Management of Analytic Projects
Other Project Management Methodologies

Communicating Analytic Findings
Introduction
Fundamental Principles of Analytic Communication
Medium
Audience
Tips for Data Visualization

What to Do Next

Bibliography

Index

Exercises appear at the end of each chapter.

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