HBR guide to data analytics basics for managers

By:

Harvard Business Review Press

Material type: Text

TextPublication details: Boston x, 231p, Harvard Business Review Press 2018ISBN:

9781633694286(pbk.)

Subject(s):

DDC classification:

658.4033 HAR

Summary: Don't let a fear of numbers hold you back. Today's business environment brings with it an onslaught of data. Now more than ever, managers must know how to tease insight from data--to understand where the numbers come from, make sense of them, and use them to inform tough decisions. 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." This book describes three key steps in the data analysis process, so you can get the information you need, study the data, and communicate your findings to others. You'll learn how to: Identify the metrics you need to measure; Run experiments and A/B tests; Ask the right questions of your data experts; Understand statistical terms and concepts; Create effective charts and visualizations; Avoid common mistakes

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Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode
Book	Plaksha University Library	Management	658.4033 HAR (Browse shelf(Opens below))	Available		004392

https://store.hbr.org/product/hbr-guide-to-data-analytics-basics-for-managers/10185

Don't let a fear of numbers hold you back. Today's business environment brings with it an onslaught of data. Now more than ever, managers must know how to tease insight from data--to understand where the numbers come from, make sense of them, and use them to inform tough decisions. 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." This book describes three key steps in the data analysis process, so you can get the information you need, study the data, and communicate your findings to others. You'll learn how to: Identify the metrics you need to measure; Run experiments and A/B tests; Ask the right questions of your data experts; Understand statistical terms and concepts; Create effective charts and visualizations; Avoid common mistakes

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