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Data science using python and R

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Data Science Using Python and R will get you plugged into the world’s two most widespread open-source platforms for data science: Python and R.
Data Science Using Python and R is written for the general reader with no previous analytics or programming experience. An entire chapter is dedicated to learning the basics of Python and R. Then, each chapter presents step-by-step instructions and walkthroughs for solving data science problems using Python and R.
Those with analytics experience will appreciate having a one-stop shop for learning how to do data science using Python and R. Topics covered include data preparation, exploratory data analysis, preparing to model the data, decision trees, model evaluation, misclassification costs, naïve Bayes classification, neural networks, clustering, regression modeling, dimension reduction, and association rules mining.

Contents
Chapter 1: Introduction to data science
Chapter 2: The basics of python and R
Chapter 3: Data preparation
Chapter 4: Exploratory data analysis
Chapter 5: Preparing to model the data
Chapter 6: Decision trees
Chapter 7: Model evaluation
Chapter 8: Naïve bayes classification
Chapter 9: Neural networks
Chapter 10: Clustering
Chapter 11: Regression modeling
Chapter 12: Dimension reduction
Chapter 13: Generalized linear models
Chapter 14: Association rules
Index

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Availability

Detail Information

Series Title

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Call Number

005.262 LAR d

Publisher

Hoboken : Wiley., 2019

Collation

xvii, 238 hlm.; ilus.; 23 cm

Language

English

ISBN/ISSN

9781119526841

Classification

005.262

Content Type

-

Media Type

-

Carrier Type

-

Edition

-

Subject(s)

Python (Bahasa pemrograman)
Python (Computer program language)
Bahasa R

Specific Detail Info

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Statement of Responsibility

Chantal D. Larose and Daniel T. Larose

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