Practical Data Science Cookbook: Data pre-processing, analysis and visualization using R and Python (Prabhanjan Tattar, et al)

 
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Practical Data Science Cookbook: Data pre-processing, analysis and visualization using R and Python (Prabhanjan Tattar, et al)

This book is for you if you wish to understand data science and numerical programming principles through practical, real-world project examples and you are an aspiring data scientist.

You will gain knowledge from studying the organization of real-world data science projects and the programming examples in R and Python, regardless of how experienced you are with data science.

This book covers the fundamentals of setting up your numerical programming environment, walks you through the data science pipeline, and provides step-by-step instructions for a number of data projects.

  • Learn and understand the installation procedure and environment required for R and Python on various platforms
  • Prepare data for analysis by implementing various data science concepts such as acquisition, cleaning, and munging through R and Python
  • Build a predictive model and an exploratory model
  • Analyze the results of your model and create reports on the acquired data
  • Build various tree-based methods and Build a random forest

Over 85 recipes to help you complete real-world data science projects in R and Python

Ebook Details

About the Authors
  • Prabhanjan Tattar has worked as a statistical analyst for nine years. His primary goal has been to clarify the intricacies of the underlying mathematics by elegantly programming explanations of statistics and machine learning approaches.
  • Tony Ojeda is a skilled data scientist and entrepreneur with over ten years of experience developing and implementing cutting-edge data products and solutions. He specializes in business process optimization.
  • Sean Patrick Murphy worked for 15 years as a senior scientist at the Applied Physics Laboratory of Johns Hopkins University, where he concentrated on high-performance cloud computing, modeling and simulation, signal processing, and machine learning.
  • For the past eight years, Benjamin Bengfort has worked as a data scientist and Python developer in the government, private sector, and academic institutions. At the University of Maryland, College Park, he is working toward his Ph.D. in computer science while conducting research in metacognition and natural language processing.
  • Abhijit Dasgupta is a data consultant with years of experience in business analytics, bioinformatics, and consulting for bioengineering who works in the surrounding DC-Maryland-Virginia area.
Published
Published Date / Year
2nd Revised edition edition (June 29, 2017)
eBook Format
PDF
Language
English
ISBN-10
1787129624
ISBN-13
978-1787129627

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