Data Science with R: A Step By Step Guide With Visual Illustrations & Examples by Andrew Oleksy - PDF free download eBook

3.6 from 137 reviews

A Step By Step Guide with Visual Illustrations and Examples The Data Science field is expected to continue growing rapidly over...

Looking for data science with step step pdf to download for free? Use our file search system, download the e-book for computer, smartphone or online reading.

Search & Download

Details of Data Science with R: A Step By Step Guide With Visual Illustrations & Examples

Exact title of the book
Data Science with R: A Step By Step Guide With Visual Illustrations & Examples
Book author
Andrew Oleksy
Book edition
Kindle Edition
Number of pages
276 pages
ISBN (ASIN)
B07KNBFGFZ
Published
November 16th 2018
File size (in PDF)
1104 kB
Data Science with R: A Step By Step Guide With Visual Illustrations & Examples

Some brief overview of book

A Step By Step Guide with Visual Illustrations and Examples The 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 necessery 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.