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Data Science with R: A Step by Step Guide with Visual Illustrations & Examples

Data Science with R: A Step by Step Guide with Visual Illustrations & Examples

          
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About the Book

A Step By Step Guide with Visual Illustrations and ExamplesThe Data Science field is expected to continue growing rapidly over the next several years and Data Scientist is consistently rated as a top career.Data Science with R gives you the necessary theoretical background to start your Data Science journey and shows you how to apply the R programming language through practical examples in order to extract valuable knowledge from data. Professor Andrew Oleksy guides you through all important concepts of data science including the R programming language, Data Mining, Clustering, Classification and Prediction, Hadoop framework and more.

Table of Contents

  • Introduction to Data Mining
    • Data Science
    • Knowledge Discovery in Databases (KDD)
    • Model Types
    • Examples and Counterexamples
    • Classification of Data Mining methods
    • Applications
    • Challenges
    • The R Programming Language
    • Basic Concepts, Definitions and Notations
    • Tool Installation
  • Introduction to R
    • Data Types
    • Basic Tasks
    • Control Structures
    • Functions
    • Scoping Rules
    • Iterated Functions
    • Help from the console and Package Installation
  • Types, Quality and Data Preprocessing
    • Categories and Types of Variables
    • Preprocessing processes
    • dplyr and tidyr packages
  • Summary Statistics and Visualization
    • Measures of Position
    • Measures of Dispersion
    • Visualization of Qualitative Data
    • Visualization of Quantitative Data
  • Classification and Prediction
    • Classification
    • Prediction
    • Overfitting and Regularization
  • Clustering
    • Unsupervised Learning
    • Concept of Cluster
    • K-means algorithm
    • Hierarchical Clustering Algorithms
    • DBSCAN Algorithm
  • Mining of Frequent Itemsets and Association Rules
    • Introduction
    • Theoretical Background
    • Apriori Algorithm
    • Frequent Itemsets Types
    • Positive and Negative Border of Frequent Itemsets
    • Association Rules Mining
    • Alternative Methods for Large Itemsets generation
    • FP-Growth Algorithm
    • Arules Package
  • Computational Methods for Big Data Analysis (Hadoop and MapReduce)
    • Introduction
    • Advantages of Hadoop's Distributed File System
    • Hadoop Users
    • Hadoop Architecture
    • The Hadoop Cluster Architecture
    • Hadoop Java API
    • List Loops & Generic Classes and Methods


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Product Details
  • ISBN-13: 9781729017456
  • Publisher: Independently Published
  • Publisher Imprint: Independently Published
  • Height: 235 mm
  • No of Pages: 268
  • Spine Width: 14 mm
  • Width: 191 mm
  • ISBN-10: 1729017452
  • Publisher Date: 17 Nov 2018
  • Binding: Paperback
  • Language: English
  • Returnable: N
  • Weight: 467 gr


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