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Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

MRP: Rs.950.00
Price in points: 950 points
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Through exercises in each chapter, you'll try out programming concepts as you learn them. Think Python is ideal for students at the high school or college level, as well as self-learners, home-schooled students, and professionals who need to learn programming basics. Beginners just getting their feet wet will learn how to start with Python in a browser.

About the Authors

Foster Provost

Foster Provost is Professor and NEC Faculty Fellow at the NYU Stern School of Business where he teaches in the MBA, Business Analytics, and Data Science programs. He previously was Editor-in-Chief of the journal Machine Learning. His award-winning research is read and cited broadly. Prof. Provost has co-founded several successful companies focusing on data science for marketing and advertising.

Tom Fawcett

Tom Fawcett holds a Ph.D. in machine learning and has worked in industry R&D for more than two decades (GTE Laboratories, NYNEX/Verizon Labs, HP Labs, etc.). His published work has become standard reading in data science both on methodology (e.g., evaluating data mining results) and on applications (e.g., fraud detection and spam filtering).

Table of Contents

Chapter 1 Introduction: Data-Analytic Thinking

Chapter 2 Business Problems and Data Science Solutions

Chapter 3 Introduction to Predictive Modeling: From Correlation to Supervised Segmentation

Chapter 4 Fitting a Model to Data

Chapter 5 Overfitting and Its Avoidance

Chapter 6 Similarity, Neighbors, and Clusters

Chapter 7 Decision Analytic Thinking I: What Is a Good Model?

Chapter 8 Visualizing Model Performance

Chapter 9 Evidence and Probabilities

Chapter 10 Representing and Mining Text

Chapter 11 Decision Analytic Thinking II: Toward Analytical Engineering

Chapter 12 Other Data Science Tasks and Techniques

Chapter 13 Data Science and Business Strategy

Chapter 14 Conclusion


Author 1:
Foster Provost, Tom Fawcett
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