Learn the basics of Data Science through an easy to understand conceptual framework and immediately practice using RapidMiner platform. Whether you are brand new to data science or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions.
Data Science has become an essential tool to extract value from data for any organization that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, engineers, and analytics professionals and for anyone who works with data.
You'll be able to:
Gain the necessary knowledge of different data science techniques to extract value from data.
Master the concepts and inner workings of 30 commonly used powerful data science algorithms.
Implement step-by-step data science process using using RapidMiner, an open source GUI based data science platform
Data Science techniques covered: Exploratory data analysis, Visualization, Decision trees, Rule induction, k-nearest neighbors, Naive Bayesian classifiers, Artificial neural networks, Deep learning, Support vector machines, Ensemble models, Random forests, Regression, Recommendation engines, Association analysis, K-Means and Density based clustering, Self organizing maps, Text mining, Time series forecasting, Anomaly detection, Feature selection and more...
Auflage
Sprache
Verlagsort
Verlagsgruppe
Elsevier Science & Technology
Zielgruppe
Für Beruf und Forschung
Business and analytics professionals who use data in everyday work settings: Data analysts, business intelligence, data warehousing, business analysts, IT leadership teams, finance and sales operations; students and instructors of data science courses; users of RapidMiner
Maße
Höhe: 235 mm
Breite: 191 mm
Gewicht
ISBN-13
978-0-12-814761-0 (9780128147610)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
Vijay Kotu is Vice President of Analytics at ServiceNow. He leads the implementation of large-scale data platforms and services to support the company's enterprise business. He has led analytics organizations for over a decade with focus on data strategy, business intelligence, machine learning, experimentation, engineering, enterprise adoption, and building analytics talent. Prior to joining ServiceNow, he was Vice President of Analytics at Yahoo. He worked at Life Technologies and Adteractive where he led marketing analytics, created algorithms to optimize online purchasing behavior, and developed data platforms to manage marketing campaigns. He is a member of the Association of Computing Machinery and a member of the Advisory Board at RapidMiner. Dr. Deshpande has extensive experience in working with companies ranging from startups to Fortune 5 in fields ranging from automotive, aerospace, retail, food, and manufacturing verticals delivering business analysis; designing and developing custom data products for implementing business intelligence, data science, and predictive analytics solutions. He was the Founder of SimaFore, a predictive analytics consulting company which was acquired by Soliton Inc., a provider of testing solutions for the semiconductor industry. He was also the Founding Co-chair of the annual Predictive Analytics World-Manufacturing conference. In his professional career he has worked with Ford Motor Company on their product development, with IBM at their IBM Watson Center of Competence, and with Domino's Pizza at their data science and artificial intelligence groups. He has a Ph.D. from Carnegie Mellon and an MBA from Ross School of Business, Michigan.
Autor*in
Vice President of Analytics at ServiceNow
Founder, SimaFore
1. Introduction
2. Data Science Process
3. Data Exploration
4. Classification
5. Deep Learning
6. Regression Methods
7. Association Analysis
8. Recommendation Engines
9. Clustering
10. Text Mining (renamed to: Natural Language Processing)
11. Time Series Forecasting
12. Anomaly Detection
13. Feature Selection
14. Model Evaluation
15. Efficient Model Execution
16. Getting Started with RapidMiner
