Levine E.S. Applying Analytics: A Practical Introduction

Boca Raton: Chapman & Hall/CRC, 2013. - 284 p.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.What Makes an Analyst?
How Is Analytics Done?
Analytics in Groups
Onward!ElementsStevens' Levels of Measurement
Continuous and Discrete Scales
Attributes
Data SetsListsTwo 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 SetsUncertainty and ErrorDefinitions, Descriptions, and Discussion
Quantification of Uncertainty
Propagation of Uncertainty
Uncertainty in the Mean
Why Is This So Unsettling?1-Dimensional Data SetsElement 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 SetsJointly Measured Data Sets
Example 1-Dimensional Linked Data Sets
Correlation
Ratios
Fitting Related Lists and 1-Dimensional Data Sets
Correlation Functions2-Dimensional Data SetsExample 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 SetsUnstructured Data SetsExtracting Structure from Unstructured Data
Text Mining
Bayesian TechniquesPrescriptive Decision AnalysisIntroduction Understanding the Decision Environment
Determining and Structuring Objectives
Identifying Alternatives
Utility Functions
Decision Trees
Game Theory
NegotiationsProject ManagementHow Analytic Projects Are Typically Managed
A Methodology for Project Management
Hastening Project Completion
Project Management of Analytic Projects
Other Project Management MethodologiesCommunicating Analytic FindingsFundamental Principles of Analytic Communication
MediumAudienceTips for Data Visualization
What to Do Next