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Guerrilla Analytics : A Practical Approach to Working with Data

By: Ridge, Enda.
Material type: TextTextSeries: eBooks on Demand.Publisher: Burlington : Elsevier Science, 2014Description: 1 online resource (276 p.).ISBN: 9780128005033.Subject(s): Data analysis (Computer science) | Data mining | Information theory | Mathematical analysis | StatisitcsGenre/Form: Electronic books.Additional physical formats: Print version:: Guerrilla Analytics : A Practical Approach to Working with DataDDC classification: 006.76 LOC classification: QA76.9. D24 R53 2014Online resources: Click here to view this ebook.
Contents:
Cover; Title Page; Copyright Page; Contents; List of Figures; Table of War Stories; Preface; Why this book?; What this book is and what it is not; Who should read this book?; How this book is organized; Disclaimer; Part 1 - Principles; Chapter 1 - Introducing Guerrilla Analytics; 1.1 - What is data analytics?; 1.1.1 - Data Analytics Definition; 1.1.2 - Examples of Data Analytics; 1.2 - Types of data analytics projects; 1.3 - Introducing Guerrilla Analytics projects; 1.4 - Guerrilla Analytics definition; 1.4.1 - Changing Data; 1.4.2 - Changing Requirements; 1.4.3 - Changing Resource
1.4.4 - Limited Time1.4.5 - Limited Toolsets; 1.4.6 - Analytics Results Must be Reproducible; 1.4.7 - Work Products must be easily explained; 1.5 - Example Guerrilla Analytics projects; 1.6 - Some terminology; 1.7 - Wrap up; Chapter 2 - Guerrilla Analytics: Challenges and Risks; 2.1 - The Guerrilla Analytics workflow; 2.2 - Challenges of managing analytics projects; 2.2.1 - Tracking Multiple Data Inputs; 2.2.2 - Versioning Multiple Data Inputs; 2.2.3 - Tracking Multiple Data Work Products; 2.2.4 - Data Generated by People; 2.2.5 - External Data; 2.2.6 - Version Control of Analytics
2.2.7 - Creating Analytics that is Reproducible2.2.8 - Testing and Reviewing Analytics; 2.2.9 - Foreign Data Environment; 2.2.10 - Upskilling a Team Quickly; 2.2.11 - Reskilling a Team Quickly; 2.3 - Risks; 2.3.1 - Losing the Link Between Data Received and its Storage Location; 2.3.2 - Losing the Link Between Raw Data and Derived Data; 2.3.3 - Inability to Reproduce Work Products Because Source Datasets have Disappeared or been Modified; 2.3.4 - Inability to Easily Navigate the Analytics Environment; 2.3.5 - Conflicting Changes to Datasets; 2.3.6 - Changing of Raw Data
2.3.7 - Out of Date Documentation Misleads the Team2.3.8 - Failure to Communicate Updates to Team Knowledge; 2.3.9 - Multiple Copies of Files and Work Products; 2.3.10 - Fragmented Code that Cannot be Executed Without the Author's Input; 2.3.11 - Inability to Identify the Source of a Dataset; 2.3.12 - Lack of Clarity Around Derivation of an Analysis; 2.3.13 - Multiple Versions of Tools and Libraries; 2.4 - Impact of failure to address analytics risks; 2.5 - Wrap up; Chapter 3 - Guerrilla Analytics Principles; 3.1 - Maintain data provenance despite disruptions; 3.2 - The principles
3.2.1 - Overview3.2.2 - Principle 1: Space is Cheap, Confusion is Expensive; 3.2.3 - Principle 2: Prefer Simple, Visual Project Structures Over Heavily Documented and Project-specific Rules; 3.2.4 - Principle 3: Prefer Automation with Program Code Over Manual Graphical Methods; 3.2.5 - Principle 4: Maintain a Link Between Data on the File ­System, in the Analytics Environment, and in Work Products; 3.2.6 - Principle 5: Version Control Changes to Data and Program Code; 3.2.7 - Consolidate Team Knowledge in Version-controlled Builds
3.2.8 - Principle 7: Prefer Analytics Code that Runs from Start to Finish
Summary: Doing data science is difficult. Projects are typically very dynamic with requirements that change as data understanding grows. The data itself arrives piecemeal, is added to, replaced, contains undiscovered flaws and comes from a variety of sources. Teams also have mixed skill sets and tooling is often limited. Despite these disruptions, a data science team must get off the ground fast and begin demonstrating value with traceable, tested work products. This is when you need Guerrilla Analytics. In this book, you will learn about: The Guerrilla Analytics Principles: simple rules of thumb
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QA76.9.C66 S619 2009 Social Computing and Virtual Communities. QA76.9.C66 S884 2011 The net effect : QA76.9.C66 .W384 2013 Data Security Breaches and Privacy in Europe. QA76.9. D24 R53 2014 Guerrilla Analytics : QA76.9 .D26 Data Warehouse Designs : QA76.9.D26 -- .M67 1993 Information Structure Design for Databases : QA76.9.D26 -- D38 2009 Database Design :

Cover; Title Page; Copyright Page; Contents; List of Figures; Table of War Stories; Preface; Why this book?; What this book is and what it is not; Who should read this book?; How this book is organized; Disclaimer; Part 1 - Principles; Chapter 1 - Introducing Guerrilla Analytics; 1.1 - What is data analytics?; 1.1.1 - Data Analytics Definition; 1.1.2 - Examples of Data Analytics; 1.2 - Types of data analytics projects; 1.3 - Introducing Guerrilla Analytics projects; 1.4 - Guerrilla Analytics definition; 1.4.1 - Changing Data; 1.4.2 - Changing Requirements; 1.4.3 - Changing Resource

1.4.4 - Limited Time1.4.5 - Limited Toolsets; 1.4.6 - Analytics Results Must be Reproducible; 1.4.7 - Work Products must be easily explained; 1.5 - Example Guerrilla Analytics projects; 1.6 - Some terminology; 1.7 - Wrap up; Chapter 2 - Guerrilla Analytics: Challenges and Risks; 2.1 - The Guerrilla Analytics workflow; 2.2 - Challenges of managing analytics projects; 2.2.1 - Tracking Multiple Data Inputs; 2.2.2 - Versioning Multiple Data Inputs; 2.2.3 - Tracking Multiple Data Work Products; 2.2.4 - Data Generated by People; 2.2.5 - External Data; 2.2.6 - Version Control of Analytics

2.2.7 - Creating Analytics that is Reproducible2.2.8 - Testing and Reviewing Analytics; 2.2.9 - Foreign Data Environment; 2.2.10 - Upskilling a Team Quickly; 2.2.11 - Reskilling a Team Quickly; 2.3 - Risks; 2.3.1 - Losing the Link Between Data Received and its Storage Location; 2.3.2 - Losing the Link Between Raw Data and Derived Data; 2.3.3 - Inability to Reproduce Work Products Because Source Datasets have Disappeared or been Modified; 2.3.4 - Inability to Easily Navigate the Analytics Environment; 2.3.5 - Conflicting Changes to Datasets; 2.3.6 - Changing of Raw Data

2.3.7 - Out of Date Documentation Misleads the Team2.3.8 - Failure to Communicate Updates to Team Knowledge; 2.3.9 - Multiple Copies of Files and Work Products; 2.3.10 - Fragmented Code that Cannot be Executed Without the Author's Input; 2.3.11 - Inability to Identify the Source of a Dataset; 2.3.12 - Lack of Clarity Around Derivation of an Analysis; 2.3.13 - Multiple Versions of Tools and Libraries; 2.4 - Impact of failure to address analytics risks; 2.5 - Wrap up; Chapter 3 - Guerrilla Analytics Principles; 3.1 - Maintain data provenance despite disruptions; 3.2 - The principles

3.2.1 - Overview3.2.2 - Principle 1: Space is Cheap, Confusion is Expensive; 3.2.3 - Principle 2: Prefer Simple, Visual Project Structures Over Heavily Documented and Project-specific Rules; 3.2.4 - Principle 3: Prefer Automation with Program Code Over Manual Graphical Methods; 3.2.5 - Principle 4: Maintain a Link Between Data on the File ­System, in the Analytics Environment, and in Work Products; 3.2.6 - Principle 5: Version Control Changes to Data and Program Code; 3.2.7 - Consolidate Team Knowledge in Version-controlled Builds

3.2.8 - Principle 7: Prefer Analytics Code that Runs from Start to Finish

Doing data science is difficult. Projects are typically very dynamic with requirements that change as data understanding grows. The data itself arrives piecemeal, is added to, replaced, contains undiscovered flaws and comes from a variety of sources. Teams also have mixed skill sets and tooling is often limited. Despite these disruptions, a data science team must get off the ground fast and begin demonstrating value with traceable, tested work products. This is when you need Guerrilla Analytics. In this book, you will learn about: The Guerrilla Analytics Principles: simple rules of thumb

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