Monetize your companys 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 companys 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 thats 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? Dont worry! Parts 1 and 2 ofData Science For Dummies will get all the bases covered for you. And if youre already a data science expert? Then you really wont 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 youll ever need to lead profitable data science projectsSecret, reverse-engineered data monetization tactics that no ones talking aboutThe shocking truth about how simple natural language processing can beHow to beat the crowd of data professionals by cultivating your own unique blend of data science expertise

Whether youre new to the data science field or already a decade in, youre sure to learn something new and incredibly valuable fromData Science For Dummies. Discover how to generate massive business wins from your companys data by picking up your copy today.

Lillian Pierson is the CEO of Data-Mania, where she supports data professionals in transforming into world-class leaders and entrepreneurs. She has trained well over one million individuals on the topics of AI and data science. Lillian has assisted global leaders in IT, government, media organizations, and nonprofits.

Introduction 1

Part 1: Getting Started with Data Science 5

Chapter 1: Wrapping Your Head Around Data Science 7

Chapter 2: Tapping into Critical Aspects of Data Engineering 19

Part 2: Using Data Science to Extract Meaning from Your Data 37

Chapter 3: Machine Learning Means Using a Machine to Learn from Data 39

Chapter 4: Math, Probability, and Statistical Modeling 51

Chapter 5: Grouping Your Way into Accurate Predictions 77

Chapter 6: Coding Up Data Insights and Decision Engines 103

Chapter 7: Generating Insights with Software Applications 137

Chapter 8: Telling Powerful Stories with Data 161

Part 3: Taking Stock of Your Data Science Capabilities 187

Chapter 9: Developing Your Business Acumen 189

Chapter 10: Improving Operations 205

Chapter 11: Making Marketing Improvements 229

Chapter 12: Enabling Improved Decision-Making 245

Chapter 13: Decreasing Lending Risk and Fighting Financial Crimes 265

Chapter 14: Monetizing Data and Data Science Expertise 275

Part 4: Assessing Your Data Science Options 289

Chapter 15: Gathering Important Information about Your Company 291

Chapter 16: Narrowing In on the Optimal Data Science Use Case 311

Chapter 17: Planning for Future Data Science Project Success 327

Chapter 18: Blazing a Path to Data Science Career Success 341

Part 5: The Part of Tens 367

Chapter 19: Ten Phenomenal Resources for Open Data 369

Chapter 20: Ten Free or Low-Cost Data Science Tools and Applications 381

Index 397