Data Sanity Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of Data Sanity book. This book definitely worth reading, it is an incredibly well-written.
Author : Balestracci Davis Publisher : Medical Group Management Association/Center for Research in Ambulatory Health Care Administration Page : 406 pages File Size : 55,9 Mb Release : 2015-04 Category : Business & Economics ISBN : 1568294387
Forget everything you've learned in any "statistics" courses you've ever attended. This ground-breaking book will introduce a "mind set," not a "tool set" to effectively utilize data to improve your practice. This totally revised and streamlined edition presents an everyday organizational language to understand the many guises of variation and a framework to react appropriately. A deeper understanding of variation is far more important than statistical techniques when solving quality improvement problems in a healthcare practice. While defined projects are still necessary for organizational improvement, but they must be seen in the context of cultural transformation. Data Sanity will catalyze this process and the use of everyday data to create the time to make effective, more strategic projects. You will learn elegantly simple -- and counterintuitive -- techniques that can create group consensus in seconds. As a result, you can lead deeper, more productive conversations about data and quality issues, which can be the surprising catalyst for true organizational excellence.
Data Science in Societal Applications by Siddharth Swarup Rautaray,Manjusha Pandey,Nhu Gia Nguyen Pdf
The book provides an insight into the practical applications and theoretical foundation of data science. The book discusses new ways of embracing agile approaches to various facets of data science, including machine learning and artificial intelligence, data mining, data visualization, and communication. The book includes contributions from academia and industry experts detailing the shortfalls of current tools and techniques used and generating the blueprint of the new technologies. The topics covered in the book range from theoretical and foundational research, platforms, methods, applications, and tools in data science. The chapters in the book add a social, geographical, and temporal dimension to data science research. The papers included are application-oriented that prepare and use data in discovery research. This book will provide researchers and practitioners with a detailed snapshot of current progress in data science. Moreover, it will stimulate new study, research, and the development of new applications.
Case-Based Reasoning Research and Development by Ashok Goel,M Belén Díaz-Agudo,Thomas Roth-Berghofer Pdf
This book constitutes the refereed proceedings of the 24th International Conference on Case-Based Reasoning Research and Development, ICCBR 2016, held in Atlanta, GA, USA, in October/November 2016. The 14 revised full papers presented were carefully reviewed and selected from 44 submissions. The papers cover a wide range of CBR topics that are of interest both to researchers and practitioners from foundations of Case-Based Reasoning; over CBR systems for specific tasks and related fields; up to CBR systems, applications and lessons learned in specific areas of expertise such as health; e-science; finance; energy, logistics, traffic; game/AI; cooking; diagnosis, technical support; as well as knowledge and experience management.
Managing Money, Measurement and Marketing in the Allied Health Professions by Robert Jones,Fiona Jenkins Pdf
High-quality Allied Health delivery through a motivated, committed and expert workforce depends on strong management and leadership. To provide this, Allied Health Profession managers need solid, evidence-based business skills just as much as clinical knowledge and ability. This book focuses on the key management areas of money, measurement and marketing as applied to the Allied Health Professions. Bringing together nationally and internationally acknowledged and recognised experts from around the world, it explains the finances of healthcare, particularly in a cash-strapped environment, information and information management, and the marketing of services - in the broadest sense - based on a robust foundation of business planning and business-case development, project management, service level agreements and specification. Report writing and presentation skills are also covered, along with editors' quality and leadership evaluation framework, the Management Quality Matrix. The information, background and practical techniques covered in this book will make it a thought-provoking and indespensible resource both for managers and leaders of Allied Health Professionals and for those training future managers and leaders.
Enterprise data is growing at a much faster rate than traditional technologies allow. New enterprise architectures combining existing technologies are desperately needed. This book suggests a way forward by applying new techniques of the World Wide Web to enterprise information systems. Linking Enterprise Data is an edited volume contributed by worldwide leaders in Semantic Web and Linked Data research, standards development and adoption. Linking enterprise data is the application of World Wide Web architecture principles to real-world information management issues faced by commercial, not-for-profit and government enterprises. This book is divided into four sections: Benefits of applying Linked Data principles in enterprise settings, enterprise approval and support of Linked Data projects, specific Linked Data techniques and a number of real-world success stories from early enterprise adopters. Linking Enterprise Data targets professionals working as CTOs, CIOs, enterprise architects, project managers and application developers in commercial, not-for-profit and government organizations concerned with scalability, flexibility and robustness of information management systems. Computer science graduate students and researchers focusing on enterprise information integration will also benefit.
Python Data Mining Quick Start Guide by Nathan Greeneltch Pdf
Explore the different data mining techniques using the libraries and packages offered by Python Key FeaturesGrasp the basics of data loading, cleaning, analysis, and visualizationUse the popular Python libraries such as NumPy, pandas, matplotlib, and scikit-learn for data miningYour one-stop guide to build efficient data mining pipelines without going into too much theoryBook Description Data mining is a necessary and predictable response to the dawn of the information age. It is typically defined as the pattern and/ or trend discovery phase in the data mining pipeline, and Python is a popular tool for performing these tasks as it offers a wide variety of tools for data mining. This book will serve as a quick introduction to the concept of data mining and putting it to practical use with the help of popular Python packages and libraries. You will get a hands-on demonstration of working with different real-world datasets and extracting useful insights from them using popular Python libraries such as NumPy, pandas, scikit-learn, and matplotlib. You will then learn the different stages of data mining such as data loading, cleaning, analysis, and visualization. You will also get a full conceptual description of popular data transformation, clustering, and classification techniques. By the end of this book, you will be able to build an efficient data mining pipeline using Python without any hassle. What you will learnExplore the methods for summarizing datasets and visualizing/plotting dataCollect and format data for analytical workAssign data points into groups and visualize clustering patternsLearn how to predict continuous and categorical outputs for dataClean, filter noise from, and reduce the dimensions of dataSerialize a data processing model using scikit-learn’s pipeline featureDeploy the data processing model using Python’s pickle moduleWho this book is for Python developers interested in getting started with data mining will love this book. Budding data scientists and data analysts looking to quickly get to grips with practical data mining with Python will also find this book to be useful. Knowledge of Python programming is all you need to get started.
R: Mining spatial, text, web, and social media data by Bater Makhabel,Pradeepta Mishra,Nathan Danneman,Richard Heimann Pdf
Create data mining algorithms About This Book Develop a strong strategy to solve predictive modeling problems using the most popular data mining algorithms Real-world case studies will take you from novice to intermediate to apply data mining techniques Deploy cutting-edge sentiment analysis techniques to real-world social media data using R Who This Book Is For This Learning Path is for R developers who are looking to making a career in data analysis or data mining. Those who come across data mining problems of different complexities from web, text, numerical, political, and social media domains will find all information in this single learning path. What You Will Learn Discover how to manipulate data in R Get to know top classification algorithms written in R Explore solutions written in R based on R Hadoop projects Apply data management skills in handling large data sets Acquire knowledge about neural network concepts and their applications in data mining Create predictive models for classification, prediction, and recommendation Use various libraries on R CRAN for data mining Discover more about data potential, the pitfalls, and inferencial gotchas Gain an insight into the concepts of supervised and unsupervised learning Delve into exploratory data analysis Understand the minute details of sentiment analysis In Detail Data mining is the first step to understanding data and making sense of heaps of data. Properly mined data forms the basis of all data analysis and computing performed on it. This learning path will take you from the very basics of data mining to advanced data mining techniques, and will end up with a specialized branch of data mining—social media mining. You will learn how to manipulate data with R using code snippets and how to mine frequent patterns, association, and correlation while working with R programs. You will discover how to write code for various predication models, stream data, and time-series data. You will also be introduced to solutions written in R based on R Hadoop projects. Now that you are comfortable with data mining with R, you will move on to implementing your knowledge with the help of end-to-end data mining projects. You will learn how to apply different mining concepts to various statistical and data applications in a wide range of fields. At this stage, you will be able to complete complex data mining cases and handle any issues you might encounter during projects. After this, you will gain hands-on experience of generating insights from social media data. You will get detailed instructions on how to obtain, process, and analyze a variety of socially-generated data while providing a theoretical background to accurately interpret your findings. You will be shown R code and examples of data that can be used as a springboard as you get the chance to undertake your own analyses of business, social, or political data. This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products: Learning Data Mining with R by Bater Makhabel R Data Mining Blueprints by Pradeepta Mishra Social Media Mining with R by Nathan Danneman and Richard Heimann Style and approach A complete package with which will take you from the basics of data mining to advanced data mining techniques, and will end up with a specialized branch of data mining—social media mining.
If you were handed two different but related sets of data, what tools would you use to find the matches? What if all you had was SQL SELECT access to a database? In this practical book, author Jim Lehmer provides best practices, techniques, and tricks to help you import, clean, match, score, and think about heterogeneous data using SQL. DBAs, programmers, business analysts, and data scientists will learn how to identify and remove duplicates, parse strings, extract data from XML and JSON, generate SQL using SQL, regularize data and prepare datasets, and apply data quality and ETL approaches for finding the similarities and differences between various expressions of the same data. Full of real-world techniques, the examples in the book contain working code. You'll learn how to: Identity and remove duplicates in two different datasets using SQL Regularize data and achieve data quality using SQL Extract data from XML and JSON Generate SQL using SQL to increase your productivity Prepare datasets for import, merging, and better analysis using SQL Report results using SQL Apply data quality and ETL approaches to finding similarities and differences between various expressions of the same data
Mastering Predictive Analytics with Python by Joseph Babcock Pdf
Exploit the power of data in your business by building advanced predictive modeling applications with Python About This Book Master open source Python tools to build sophisticated predictive models Learn to identify the right machine learning algorithm for your problem with this forward-thinking guide Grasp the major methods of predictive modeling and move beyond the basics to a deeper level of understanding Who This Book Is For This book is designed for business analysts, BI analysts, data scientists, or junior level data analysts who are ready to move from a conceptual understanding of advanced analytics to an expert in designing and building advanced analytics solutions using Python. You're expected to have basic development experience with Python. What You Will Learn Gain an insight into components and design decisions for an analytical application Master the use Python notebooks for exploratory data analysis and rapid prototyping Get to grips with applying regression, classification, clustering, and deep learning algorithms Discover the advanced methods to analyze structured and unstructured data Find out how to deploy a machine learning model in a production environment Visualize the performance of models and the insights they produce Scale your solutions as your data grows using Python Ensure the robustness of your analytic applications by mastering the best practices of predictive analysis In Detail The volume, diversity, and speed of data available has never been greater. Powerful machine learning methods can unlock the value in this information by finding complex relationships and unanticipated trends. Using the Python programming language, analysts can use these sophisticated methods to build scalable analytic applications to deliver insights that are of tremendous value to their organizations. In Mastering Predictive Analytics with Python, you will learn the process of turning raw data into powerful insights. Through case studies and code examples using popular open-source Python libraries, this book illustrates the complete development process for analytic applications and how to quickly apply these methods to your own data to create robust and scalable prediction services. Covering a wide range of algorithms for classification, regression, clustering, as well as cutting-edge techniques such as deep learning, this book illustrates not only how these methods work, but how to implement them in practice. You will learn to choose the right approach for your problem and how to develop engaging visualizations to bring the insights of predictive modeling to life Style and approach This book emphasizes on explaining methods through example data and code, showing you templates that you can quickly adapt to your own use cases. It focuses on both a practical application of sophisticated algorithms and the intuitive understanding necessary to apply the correct method to the problem at hand. Through visual examples, it also demonstrates how to convey insights through insightful charts and reporting.
This guide teaches data mining from the perspective of IT professionals using Microsoft data management and e-commerce technologies. The book explains major new data mining capabilities in the forthcoming SQL Server 2000, Microsoft Commerce Server, and other products, and details the new Microsoft standard, "OLE DB for Data Mining".
"This book teaches you and your organization how to achieve unprecedented quality by using a common organizational language based in process and understanding variation to motivate more productive daily conversations for everyone on your team"--Provided by publisher.
Author : J. George Shanthikumar,David D. Yao,W.H.M. Zijm Publisher : Springer Science & Business Media Page : 413 pages File Size : 41,9 Mb Release : 2012-12-06 Category : Business & Economics ISBN : 9781461503736
Stochastic Modeling and Optimization of Manufacturing Systems and Supply Chains by J. George Shanthikumar,David D. Yao,W.H.M. Zijm Pdf
This volume originates from two workshops, both focusing on themes that are reflected in the title of the volume. The first workshop took place at Eindhoven University of Technology, April 24-26, 2001, on the occasion of the University granting a doctorate honoris causa to Profes sor John A. Buzacott. The second workshop was held on June 15, 2002 at Cornell University (preceding the annual INFORMSjMSOM Confer ence), honoring John's retirement and his lifetime contributions. Each of the two workshops consisted of about a dozen technical presentations. The objective of the volume, however, is not to simply publish the proceedings of the two workshops. Rather, our objective is to put to gether a select set of articles, each organized into a well-written chapter, focusing on a timely topic. Collected into a single volume, these chapters aim to serve as a useful reference for researchers and practitioners alike, and also as reading materials for graduate courses or seminars.
Business Analytics with SAS Studio by Rajinder Kr. Chitoria Pdf
Analyzing business data points with greater precision, efficiency, and speed KEY FEATURES ● Exposure to work on large datasets, data mining techniques, and SAS built-in functions. ● Exciting examples and a step-by-step guide to the entire field of business analytics. ● Additional support of sophisticated SQL queries and the creation of strong visualization reports. DESCRIPTION This book teaches readers how to properly use SAS (R) Studio to enhance business analytics summaries and graphical reports to make more informed business decisions. Since the examples in the book are laid out in a logical sequence, no prior knowledge is required to get started with implementing what you learn in them. The book begins with configuring your SAS® OnDemand instance, complete with sample datasets and scripts. The book explains programming syntaxes before delving into sophisticated programming principles for managing data values and concludes with creating graphical reports for business data values. It explores how to implement datasets, read external files, execute conditional statements, loops, formats, text, date, numeric functions, and arrays. The book also helps writing SQL Statements such as joins, sets, index, views, etc. with Proc SQL, Univariate (PROC MEAN, PROC FREQ), Multivariate (PROC FREQ), and Design Graphs with the help of PROC SGPLOT. After reading this book, readers will be able to evaluate business data values and create excellent visualizations that will assist enterprises in making more informed business decisions. Readers will become confident to use SAS Studio's rich interface and develop analytical programs. WHAT YOU WILL LEARN ● Configuring Online SAS® OnDemand for Academics. ● Writing BASE SAS Programming, and writing conditional, looping-based programs. ● Implementing SAS built-in text, date, numeric functions, and reading external data files. ● Using the SQL Statement with PROC SQL Processing. ● Plotting attractive data visualization using PROC SGPLOT. ● End-to-end case study on Employee Skill Development’s Data Analytics and Visualization. WHO THIS BOOK IS FOR This book is for those who wish to learn how to use SAS Studio's rich interface and develop an analytical program that helps make better decisions. Having a basic understanding of how data analytics works helps. TABLE OF CONTENTS 1. Introducing SAS Environment 2. Starting with SAS Programming 3. Data Mining and Storage Techniques in SAS 4. Controlling the Program Flow in SAS 5. Using SAS Built-In Functions 6. Working with Advanced Data Input Techniques 7. Getting Started with SQL Processing in SAS 8. Managing Database Tables in SAS 9. Working with Dataset in SAS 10. Data Visualization and Macro Programming with SAS 11. Case Study
Advanced Verification Techniques by Leena Singh,Leonard Drucker,Neyaz Khan Pdf
"As chip size and complexity continues to grow exponentially, the challenges of functional verification are becoming a critical issue in the electronics industry. It is now commonly heard that logical errors missed during functional verification are the most common cause of chip re-spins, and that the costs associated with functional verification are now outweighing the costs of chip design. To cope with these challenges engineers are increasingly relying on new design and verification methodologies and languages. Transaction-based design and verification, constrained random stimulus generation, functional coverage analysis, and assertion-based verification are all techniques that advanced design and verification teams routinely use today. Engineers are also increasingly turning to design and verification models based on C/C++ and SystemC in order to build more abstract, higher performance hardware and software models and to escape the limitations of RTL HDLs. This new book, Advanced Verification Techniques, provides specific guidance for these advanced verification techniques. The book includes realistic examples and shows how SystemC and SCV can be applied to a variety of advanced design and verification tasks." - Stuart Swan
Handbook of Psychology, Forensic Psychology by Irving B. Weiner,Randy K. Otto Pdf
Psychology is of interest to academics from many fields, as well as to the thousands of academic and clinical psychologists and general public who can't help but be interested in learning more about why humans think and behave as they do. This award-winning twelve-volume reference covers every aspect of the ever-fascinating discipline of psychology and represents the most current knowledge in the field. This ten-year revision now covers discoveries based in neuroscience, clinical psychology's new interest in evidence-based practice and mindfulness, and new findings in social, developmental, and forensic psychology.
