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The Resource HBR guide to data analytics basics for managers

HBR guide to data analytics basics for managers

Label
HBR guide to data analytics basics for managers
Title
HBR guide to data analytics basics for managers
Title variation
  • Harvard Business Review guide to data analytics basics for managers
  • Data analytics basics for managers
Subject
Language
eng
Summary
Don't let a fear of numbers hold you back. Today's business environment brings with it an onslaught of data, but leaving the analysis to others in your company just won't cut it. Now more than ever, managers must know how to tease insight from data--to understand where it comes from, make sense of the numbers, and use those findings to inform their toughest decisions. But how do you get started? Whether you're working with data experts or running your own tests, you'll find answers in the HBR Guide to Data Analytics Basics for Managers. --
Member of
Assigning source
Provided by publisher
Cataloging source
MH/DLC
Dewey number
658.4/033
Illustrations
  • illustrations
  • charts
Index
index present
LC call number
HD30.215
LC item number
.H37 2018
Literary form
non fiction
Series statement
Harvard Business Review guides
http://library.link/vocab/subjectName
  • Management
  • Quantitative research
  • Decision making
  • Decision support systems
  • Information visualization
Label
HBR guide to data analytics basics for managers
Instantiates
Publication
Note
Includes index
Carrier category
volume
Carrier MARC source
rdacarrier
Content category
text
Content type MARC source
rdacontent
Contents
Introduction: Why you need to understand data analytics -- Section 1. Getting started: Keep up with your quants: an innumerate's guide to navigating big data / by Thomas H. Davenport -- A simple exercise to help you think like a data scientist: an easy way to learn the process of data analytics / by Thomas C. Redman -- Section 2. Gather the right information: Do you need all that data?: questions to ask for a focused search / by Ron Ashkenas -- How to ask your data scientists for data and analytics: factors to keep in mind to get the information you need / by Michael Li, Madina Kassengaliyeva, and Raymond Perkins -- How to design a business experiment: tips for using the scientific method / by Oliver Hauser and Michael Luca -- Know the difference between your data and your metrics: understand what you're measuring / by Jeff Bladt and Bob Filbin -- The fundamentals of A/B testing: how it works and mistakes to avoid / by Amy Gallo -- Can your data be trusted?: gauge whether your data is safe to use / by Thomas C. Redman -- Section 3. Analyze the data: A predictive analytics primer: look to the future by looking at the past / by Thomas H. Davenport -- Understanding regression analysis: evaluate the relationship between variables / by Amy Gallo -- When to act on a correlation, and when not to: assess your confidence in your findings and the risk of being wrong / by David Ritter -- Can machine learning solve your business problem?: steps to take before investing in AI / by Anastassia Fedyk -- A refresher on statistical significance: check if your results are real or just luck / by Amy Gallo -- Linear thinking in a nonlinear world: a common mistake that leads to errors in judgment / by Bart de Langhe, Stefano Puntoni, and Richard Larrick -- Pitfalls of data-driven decisions: the cognitive traps to avoid / by Megan MacGarvie and Kristina McElheran -- Don't let your analytics cheat the truth: always ask for the outliers / by Michael Schrage -- Section 4. Communicate your findings: Data is worthless if you don't communicate it: tell people what it means / by Thomas H. Davenport -- When data visualization works, and when it doesn't: not all data is worth the effort / by Jim Stikeleather -- How to make charts that pop and persuade: questions to help give your numbers meaning / by Nancy Duarte -- Why it's so hard for us to communicate uncertainty: illustrating -- and understanding -- the likelihood of events: an interview with Scott Berinato / by Nicole Torres -- Responding to someone who angrily challenges your data: ensure the data is thorough, then make them an ally / by Jon M. Jachimowicz -- Decisions don't start with data: influence others through story and emotion / by Nick Morgan
Dimensions
23 cm
Extent
x, 231 pages
Isbn
9781633694286
Lccn
2017048270
Media category
unmediated
Media MARC source
rdamedia
Other physical details
illustrations, charts
System control number
  • (OCoLC)1013997280
  • 2583467
Label
HBR guide to data analytics basics for managers
Publication
Note
Includes index
Carrier category
volume
Carrier MARC source
rdacarrier
Content category
text
Content type MARC source
rdacontent
Contents
Introduction: Why you need to understand data analytics -- Section 1. Getting started: Keep up with your quants: an innumerate's guide to navigating big data / by Thomas H. Davenport -- A simple exercise to help you think like a data scientist: an easy way to learn the process of data analytics / by Thomas C. Redman -- Section 2. Gather the right information: Do you need all that data?: questions to ask for a focused search / by Ron Ashkenas -- How to ask your data scientists for data and analytics: factors to keep in mind to get the information you need / by Michael Li, Madina Kassengaliyeva, and Raymond Perkins -- How to design a business experiment: tips for using the scientific method / by Oliver Hauser and Michael Luca -- Know the difference between your data and your metrics: understand what you're measuring / by Jeff Bladt and Bob Filbin -- The fundamentals of A/B testing: how it works and mistakes to avoid / by Amy Gallo -- Can your data be trusted?: gauge whether your data is safe to use / by Thomas C. Redman -- Section 3. Analyze the data: A predictive analytics primer: look to the future by looking at the past / by Thomas H. Davenport -- Understanding regression analysis: evaluate the relationship between variables / by Amy Gallo -- When to act on a correlation, and when not to: assess your confidence in your findings and the risk of being wrong / by David Ritter -- Can machine learning solve your business problem?: steps to take before investing in AI / by Anastassia Fedyk -- A refresher on statistical significance: check if your results are real or just luck / by Amy Gallo -- Linear thinking in a nonlinear world: a common mistake that leads to errors in judgment / by Bart de Langhe, Stefano Puntoni, and Richard Larrick -- Pitfalls of data-driven decisions: the cognitive traps to avoid / by Megan MacGarvie and Kristina McElheran -- Don't let your analytics cheat the truth: always ask for the outliers / by Michael Schrage -- Section 4. Communicate your findings: Data is worthless if you don't communicate it: tell people what it means / by Thomas H. Davenport -- When data visualization works, and when it doesn't: not all data is worth the effort / by Jim Stikeleather -- How to make charts that pop and persuade: questions to help give your numbers meaning / by Nancy Duarte -- Why it's so hard for us to communicate uncertainty: illustrating -- and understanding -- the likelihood of events: an interview with Scott Berinato / by Nicole Torres -- Responding to someone who angrily challenges your data: ensure the data is thorough, then make them an ally / by Jon M. Jachimowicz -- Decisions don't start with data: influence others through story and emotion / by Nick Morgan
Dimensions
23 cm
Extent
x, 231 pages
Isbn
9781633694286
Lccn
2017048270
Media category
unmediated
Media MARC source
rdamedia
Other physical details
illustrations, charts
System control number
  • (OCoLC)1013997280
  • 2583467

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