Overview

Boost your understanding of data science techniques to solve real-world problems Data science is an exciting, interdisciplinary field that extracts insights from data to solve business problems. This book introduces common data science techniques and methods and shows you how to apply them in real-world case studies. From data preparation and exploration to model assessment and deployment, this book describes every stage of the analytics life cycle, including a comprehensive overview of unsupervised and supervised machine learning techniques. The book guides you through the necessary steps to pick the best techniques and models and then implement those models to successfully address the original business need. No software is shown in the book, and mathematical details are kept to a minimum. This allows you to develop an understanding of the fundamentals of data science, no matter what background or experience level you have.

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9781953329608

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Reviews

We've all heard the term Data Science, but what does it mean? Obviously, it involves both data and science, but doesn't everything? Yes! But it's not as easy as it sounds. Luckily, the authors of this book have swept in to rescue us from this modern-day thought paradox by gently walking us through the maze of research, development, theory, and application that is the world of machine learning and data science. Geared to anyone interested in learning more about this intricate study, this book will help answer your most pressing questions, while opening up the doors of possibility to many more. A must-have for the desk of anyone even remotely involved in research! --Deanna (DeDe) Naomi Schreiber-Gregory, MS, Co-owner, Operations and Finance, Independent Consultant - Statistics & Data Management, Juxdapoze, LLC A comprehensive and nifty book that takes the reader through the analytics lifecycle, discussing many data science methods, statistical approaches and machine learning techniques. The variety of case studies and tips along the way enhances your knowledge and capability, forming a practical understanding of data science. --Michelle Homes, Metacoda, Australia

Author Information

Dr. Carlos Andre Reis Pinheiro is a Principal Data Scientist at SAS and a Visiting Professor at Data ScienceTech Institute in France. He has been working in analytics since 1996 for some of the largest telecommunications providers in Brazil in multiple roles from technical to executive. He worked as a Senior Data Scientist for EMC in Brazil on network analytics, optimization, and text analytics projects, and as a Lead Data Scientist for Teradata on machine learning projects. Dr. Pinheiro has a BSc in Applied Mathematics and Computer Science, an MSc in Computing, and a DSc in Engineering from the Federal University of Rio de Janeiro. Carlos has completed a series of postdoctoral research terms in different fields, including Dynamic Systems at IMPA, Brazil; Social Network Analysis at Dublin City University, Ireland; Transportation Systems at Université de Savoie, France; Dynamic Social Networks and Human Mobility at Katholieke Universiteit Leuven, Belgium; and Urban Mobility and Multi-modal Traffic at Fundação Getúlio Vargas, Brazil. He has published several papers in international journals and conferences, and he is author of Social Network Analysis in Telecommunications and Heuristics in Analytics: A Practical Perspective of What Influence Our Analytical World, both published by John Wiley and Sons, Inc. Michael Patetta has been a statistical instructor for SAS since 1994. He teaches a variety of courses including Supervised Machine Learning Procedures Using SAS Viya in SAS Studio, Predictive Modeling Using Logistic Regression, Introduction to Data Science Statistical Methods, and Regression Methods Using SAS Viya. Before coming to SAS, Michael worked in the North Carolina State Health Department for 10 years as a health statistician and program manager. He has authored or co-authored 10 published papers since 1983. Michael has a BA from the University of Notre Dame and a MA from the University of North Carolina at Chapel Hill. In his spare time, he loves to hike in National Parks.

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