Data Science. Edition No. 2 - Product Image

Data Science. Edition No. 2

  • ID: 4540031
  • Book
  • 550 Pages
  • Elsevier Science and Technology
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Data science is about finding useful patterns in data. Rapid expansion in the volume of information collected by organizations entails a critical need for a framework and toolset to analyze and extract meaningful knowledge from data. Data science offers a set of techniques to uncover hidden patterns and relationships in data, in order to aid decision-making. Data Science presents the basic concepts behind many data science techniques in an easy to follow manner and prepares anyone with a basic grasp of mathematics to implement these techniques in their business, without the need to write programming code. The book uses an open source, GUI based data science tool to illustrate the concepts so that readers can follow the concepts and implement data science algorithms in parallel. The tool is open source, which means that learning data science with this tool is virtually cost free. The content and practical use cases described in this book are geared towards business and analytics professionals who use data. The reader of the book will acquire a comprehensive understanding of different data science techniques, and be prepared to select the right technique for a given data problem and to create a general purpose analytics process
  • Contains fully updated content on data science, including tactics on how to mine business data for information
  • Presents simple explanations for over twenty powerful data science techniques
  • Enables the practical use of data science algorithms without the need for programming
  • Demonstrates processes with practical use cases
  • Introduces each algorithm or technique and explains the workings of a data science algorithm in plain language
  • Describes the commonly used setup options for the open source tool RapidMiner
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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
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Kotu, Vijay
Vijay Kotu is Senior Director of Analytics at Yahoo. He leads the implementation of large-scale data and analytics systems to support the company's online business. He has practiced Analytics for over a decade with focus on business intelligence, data mining, web analytics, experimentation, information design, data warehousing, data engineering and developing analytical teams. Prior to joining Yahoo, he worked at Life Technologies and Adteractive where he led marketing analytics, created algorithms to optimize online purchase behaviors, and developed data platforms to manage marketing campaigns. He is a member of Association of Computing Machinery and is a certified Six Sigma Black Belt from American Society of Quality.
Deshpande, Bala
Bala Deshpande is the founder of SimaFore, a custom analytics app development and consulting company. He has more than 20 years of experience in using analytical techniques in a wide range of application areas. His first exposure to predictive models and analytics was in the field of biomechanics - in identifying correlations and building multiple regression models. He began his career as an engineering consultant following which he spent several years analyzing data from automobile crash tests and helping to build safer cars at Ford Motor Company. He is the co-chair of Predictive Analytics World - Manufacturing, an annual conference focused on promoting and evangelizing predictive analytics in the industry. He blogs regularly about data mining and predictive analytics for his company at www.simafore.com/blog. He holds a PhD in Bioengineering from Carnegie Mellon University and an MBA from Ross School of Business (Michigan).
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