Data Science For Dummies (eBook)
432 Seiten
John Wiley & Sons (Verlag)
978-1-119-81161-9 (ISBN)
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.
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
| Erscheint lt. Verlag | 20.8.2021 |
|---|---|
| Sprache | englisch |
| Themenwelt | Mathematik / Informatik ► Informatik ► Datenbanken |
| Informatik ► Office Programme ► Outlook | |
| Schlagworte | Computer Science • Data Mining & Knowledge Discovery • Data Mining u. Knowledge Discovery • Data Science • Informatik |
| ISBN-10 | 1-119-81161-9 / 1119811619 |
| ISBN-13 | 978-1-119-81161-9 / 9781119811619 |
| Haben Sie eine Frage zum Produkt? |
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