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Fire Data Analysis Handbook- 2nd Edition

Fire Data Analysis Handbook- 2nd Edition
Author: Federal Emergency Management Agency
Publisher: CreateSpace
Total Pages: 74
Release: 2013-03-08
Genre:
ISBN: 9781482725988
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This handbook's primary objective is to describe statistical techniques for analyzing data typically collected in fire departments. Motivation for the handbook comes from the belief that fire departments collect an immense amount of data, but do very little with it. A compelling reason for collecting data is a legal requirement for documenting incidents; however, incident reports provide a more beneficial service to fire departments by providing insight into the nature of fires and injuries.

Fire Data Analysis Handbook

Fire Data Analysis Handbook
Author: Federal Emergency Management Agency. United States Fire Administration
Publisher:
Total Pages: 67
Release: 2004
Genre:
ISBN:
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The fire service exists today in an environment constantly inundated with data, but data are seen of little use in the everyday, real world in which first responders live and work. This is no accident. By themselves, pieces of data are of little use to anyone. Information, on the other hand, is very useful indeed. What's the difference? At sporting events, people in stadiums hold up individual, multi-colored squares of cardboard to form a giant image or text, which could be recognized only from a distance. This is a good analogy for data and information. The individual squares of cardboard are like data. They are very numerous and they all look similar taken by themselves. The big image formed from the organization of thousands of those cards is like information. It is what can be built from many pieces of data. Information then is an organization of data that makes a point about something. The fire service of today is changing. More and more, it is not fighting fires as much as it is doing EMS, HAZMAT, inspections, investigations, prevention, and other nontraditional but important tasks which are vital to the community. Balancing limited resources and justifying daily operations and finances in the face of tough economic times is a scenario that is familiar to every department. Turning data into information is neither simple nor easy. It requires some knowledge of the tools and techniques used for this purpose. Historically, the fire service has had few of these tools at its disposal and none of them has been designed with the fire service in mind. This book changes that. It was designed solely for the use of the fire service. The examples were developed from the most recent fire data collected from departments all over the Nation. This book also was designed to be modular in form. Many departments' information needs can be met by using only the first few chapters. Others with a more statistical leaning may want to go further. The point is, it's up to the reader to decide. This handbook is just another tool, like a pumper or a ladder, to help do the job. In this revised edition, the use of statistical symbols and formulas has been eliminated for ease of use and understanding. The problems at the end of each chapter also have been left out. The philosophy behind this is not to discourage anyone seeking immediate results, and to encourage those with a desire for more indepth knowledge of statistical analysis tools.

Fire Data Analysis Handbook

Fire Data Analysis Handbook
Author: Federal Emergency Management Agency. United States Fire Administration
Publisher:
Total Pages: 193
Release: 1995
Genre: Fire investigations
ISBN:
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Fire Data Analysis Handbook

Fire Data Analysis Handbook
Author: Federal Emergency Management Agency
Publisher: Createspace Independent Publishing Platform
Total Pages: 0
Release: 2013-03-08
Genre:
ISBN: 9781482725940
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This book is designed solely for the use of the fire service and is modular in form. Many departments' information needs can be met by studying only the first few chapters, while others with a more statistical bent may want to dig deeper.

Hands-On Data Analysis with Pandas

Hands-On Data Analysis with Pandas
Author: Stefanie Molin
Publisher: Packt Publishing Ltd
Total Pages: 788
Release: 2021-04-29
Genre: Computers
ISBN: 1800565917
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Get to grips with pandas by working with real datasets and master data discovery, data manipulation, data preparation, and handling data for analytical tasks Key Features Perform efficient data analysis and manipulation tasks using pandas 1.x Apply pandas to different real-world domains with the help of step-by-step examples Make the most of pandas as an effective data exploration tool Book DescriptionExtracting valuable business insights is no longer a ‘nice-to-have’, but an essential skill for anyone who handles data in their enterprise. Hands-On Data Analysis with Pandas is here to help beginners and those who are migrating their skills into data science get up to speed in no time. This book will show you how to analyze your data, get started with machine learning, and work effectively with the Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will learn how to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding chapters, you will explore some applications of anomaly detection, regression, clustering, and classification using scikit-learn to make predictions based on past data. This updated edition will equip you with the skills you need to use pandas 1.x to efficiently perform various data manipulation tasks, reliably reproduce analyses, and visualize your data for effective decision making – valuable knowledge that can be applied across multiple domains.What you will learn Understand how data analysts and scientists gather and analyze data Perform data analysis and data wrangling using Python Combine, group, and aggregate data from multiple sources Create data visualizations with pandas, matplotlib, and seaborn Apply machine learning algorithms to identify patterns and make predictions Use Python data science libraries to analyze real-world datasets Solve common data representation and analysis problems using pandas Build Python scripts, modules, and packages for reusable analysis code Who this book is for This book is for data science beginners, data analysts, and Python developers who want to explore each stage of data analysis and scientific computing using a wide range of datasets. Data scientists looking to implement pandas in their machine learning workflow will also find plenty of valuable know-how as they progress. You’ll find it easier to follow along with this book if you have a working knowledge of the Python programming language, but a Python crash-course tutorial is provided in the code bundle for anyone who needs a refresher.

Handbook of Statistical Analysis and Data Mining Applications

Handbook of Statistical Analysis and Data Mining Applications
Author: Robert Nisbet
Publisher: Elsevier
Total Pages: 822
Release: 2017-11-09
Genre: Mathematics
ISBN: 0124166458
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Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation. The handbook helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application. This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques and discusses their application to real problems in ways accessible and beneficial to practitioners across several areas—from science and engineering, to medicine, academia and commerce. Includes input by practitioners for practitioners Includes tutorials in numerous fields of study that provide step-by-step instruction on how to use supplied tools to build models Contains practical advice from successful real-world implementations Brings together, in a single resource, all the information a beginner needs to understand the tools and issues in data mining to build successful data mining solutions Features clear, intuitive explanations of novel analytical tools and techniques, and their practical applications

Data Analysis with R, Second Edition

Data Analysis with R, Second Edition
Author: Anthony Fischetti
Publisher: Packt Publishing Ltd
Total Pages: 570
Release: 2018-03-28
Genre: Computers
ISBN: 1788397339
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Learn, by example, the fundamentals of data analysis as well as several intermediate to advanced methods and techniques ranging from classification and regression to Bayesian methods and MCMC, which can be put to immediate use. Key Features Analyze your data using R – the most powerful statistical programming language Learn how to implement applied statistics using practical use-cases Use popular R packages to work with unstructured and structured data Book Description Frequently the tool of choice for academics, R has spread deep into the private sector and can be found in the production pipelines at some of the most advanced and successful enterprises. The power and domain-specificity of R allows the user to express complex analytics easily, quickly, and succinctly. Starting with the basics of R and statistical reasoning, this book dives into advanced predictive analytics, showing how to apply those techniques to real-world data though with real-world examples. Packed with engaging problems and exercises, this book begins with a review of R and its syntax with packages like Rcpp, ggplot2, and dplyr. From there, get to grips with the fundamentals of applied statistics and build on this knowledge to perform sophisticated and powerful analytics. Solve the difficulties relating to performing data analysis in practice and find solutions to working with messy data, large data, communicating results, and facilitating reproducibility. This book is engineered to be an invaluable resource through many stages of anyone’s career as a data analyst. What you will learn Gain a thorough understanding of statistical reasoning and sampling theory Employ hypothesis testing to draw inferences from your data Learn Bayesian methods for estimating parameters Train regression, classification, and time series models Handle missing data gracefully using multiple imputation Identify and manage problematic data points Learn how to scale your analyses to larger data with Rcpp, data.table, dplyr, and parallelization Put best practices into effect to make your job easier and facilitate reproducibility Who this book is for Budding data scientists and data analysts who are new to the concept of data analysis, or who want to build efficient analytical models in R will find this book to be useful. No prior exposure to data analysis is needed, although a fundamental understanding of the R programming language is required to get the best out of this book.

Python for Data Analysis

Python for Data Analysis
Author: Wes McKinney
Publisher: "O'Reilly Media, Inc."
Total Pages: 676
Release: 2017-09-25
Genre: Computers
ISBN: 1491957611
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Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples