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Data Analytics: A Practical Guide To Data Analytics For Business, Beginner To Expert

Posted By: naag
Data Analytics: A Practical Guide To Data Analytics For Business, Beginner To Expert

Data Analytics: A Practical Guide To Data Analytics For Business, Beginner To Expert(Data Analytics, Prescriptive Analytics, Statistics, Big Data, Intelligence, … Master Data, Data Science, Data Mining)
English | 2017 | ISBN-10: 1547156996 | 50 pages | AZW3, PDF, EPUB (conv) | 0.6 Mb

Understand Data Analytics and Implement it in Your Business Today
Do you want improve your revenue and stop missing out on profit?
Do you want to learn about how data analytics in a style and approach that is suitable for you, regardless of your current knowledge?
This book not only provides step-by-step guide to data analytics, but teaches you actionable steps to improve your analysis in all environments! Are you ready to learn? If so, Data Analytics: The Ultimate Step-by-Step guide to master emotional intelligence, interpersonal skills, relationships, self-awareness, habits and increase your workplace success.by James Fahl is THE book for you! It covers the most essential topics you must learn to become a master of Data Analytics.
What Separates This Book From The Rest?
What separates this book from the rest? The unique way you will learn with examples and steps. Many books leave you more confused than before you picked them up, not this book, it’s clear concise and implementable. We make it our goal to write this book in plain easy to understand English that anyone can understand. Gone are the days of highly technical language. This allows you to quickly learn topics, and use your new skills immediately. To aid you in learning the topics quickly and effectively this book has been designed to be the ultimate step-by-step guide. Making sure that you’re confident and clear with each topic before moving on!
You Will Learn The Following:
What is Data Analytics?
Why use Data Analytics
The importance of Data Analytics
Types of Data Analytics
Explanations of Different models
Collecting Data
Mistakes to avoid