Data science for dummies

Monetize your company's data and data science expertise without spending a fortune on hiring independent strategy consultants to help What if there was one simple, clear process for ensuring that all your company's data science projects achieve a high a return on investment? What if you could validate your ideas for future data science projects, and select the one idea that's most prime for achieving profitability while also moving your company closer to its business vision? There is. Industry-acclaimed data science consultant, Lillian Pierson, shares her proprietary STAR Framework - A simple, proven process for leading profit-forming data science projects. Not sure what data science is yet? Don't worry! Parts 1 and 2 of Data Science For Dummies will get all the bases covered for you. And if you're already a data science expert? Then you really won't want to miss the data science strategy and data monetization gems that are shared in Part 3 onward throughout this book. Data Science For Dummies demonstrates: The only process you'll ever need to lead profitable data science projects Secret, reverse-engineered data monetization tactics that no one's talking about The shocking truth about how simple natural language processing can be How to beat the crowd of data professionals by cultivating your own unique blend of data science expertise Whether you're new to the data science field or already a decade in, you're sure to learn something new and incredibly valuable from Data Science For Dummies. Discover how to generate massive business wins from your company's data by picking up your copy today.

<P><b>Introduction</b><b> 1</b></p> <p>About This Book 3</p> <p>Foolish Assumptions 3</p> <p>Icons Used in This Book 4</p> <p>Beyond the Book 4</p> <p>Where to Go from Here 4</p> <p><b>Part 1: Getting Started with Data Science</b><b> 5</b></p> <p><b>Chapter 1: Wrapping Your Head Around Data Science</b><b> 7</b></p> <p>Seeing Who Can Make Use of Data Science 8</p> <p>Inspecting the Pieces of the Data Science Puzzle 10</p> <p>Collecting, querying, and consuming data 11</p> <p>Applying mathematical modeling to data science tasks 12</p> <p>Deriving insights from statistical methods 12</p> <p>Coding, coding, coding -- it's just part of the game 13</p> <p>Applying data science to a subject area 13</p> <p>Communicating data insights 14</p> <p>Exploring Career Alternatives That Involve Data Science 15</p> <p>The data implementer 16</p> <p>The data leader 16</p> <p>The data entrepreneur 17</p> <p><b>Chapter 2: Tapping into Critical Aspects of Data Engineering</b><b> 19</b></p> <p>Defining Big Data and the Three Vs 19</p> <p>Grappling with data volume 21</p> <p>Handling data velocity 21</p> <p>Dealing with data variety 22</p> <p>Identifying Important Data Sources 23</p> <p>Grasping the Diff…