![]() We recommend reading this tutorial, in the sequence listed in the left menu. In this chapter, we provide a quick and easy introduction to R programming. How you can use R to easily create a graph with numbers from 1 to 10 on both the x and y axis: plot (1:10) Result: Try it Yourself ». Getting to know MapReduce – MapReduce Execution Pipeline – Runtime Coordination and Task Management – MapReduce Application – Hadoop Word Count Implementation. Easy Guides R software R Basics: Quick and Easy R Basics: Quick and Easy R is a free and powerful statistical software for analyzing and visualizing data. R is fast becoming the leading language to use in data science and statistics, making it an extremely valuable skill to have. UNIT V – PROCESSING YOUR DATA WITH MAPREDUCE (6 hours) HDFS Architecture – HDFS Concepts – Blocks – NameNode – Secondary NameNode – DataNode – HDFS Federation – Basic File System Operations – Data Flow – Anatomy of File Read – Anatomy of File Write. Understanding the data to make better decisions and finding the final result. Visualizing the data to get a better perspective. ![]() Modeling the data using various complex and efficient algorithms. In this progression of courses, we will help both new and existing R users master R and expand their data science skills. In short, we can say that data science is all about: Asking the correct questions and analyzing the raw data. In part, R owes its popularity to its open source distribution and massive user community. R is a programming language and environment commonly used in statistical computing, data analytics and scientific research. ![]() UNIT IV – HADOOP DISTRIBUTED FILE SYSTEM ARCHITECTURE (6 hours) R is one of the fastest growing programming languages and tool of choice for analysts and data scientists. Big data from Technology Perspective: History of Hadoop-Components of Hadoop-Application Development in Hadoop-Getting your data in Hadoop-other Hadoop Component. UNIT III-BIG DATA FROM DIFFERENT PERSPECTIVES (6 hours)īig data from business Perspective: Introduction of big data-Characteristics of big data-Data in the warehouse and data in Hadoop- Importance of Big data- Big data Use cases: Patterns for Big data deployment. Overview of Clustering – K-means – Use Cases – Overview of the Method – Perform a K-means Analysis using R – Classification – Decision Trees – Overview of a Decision Tree – Decision Tree Algorithms – Evaluating a Decision Tree – Decision Tree in R – Bayes’ Theorem – Naïve Bayes Classifier – Smoothing – Naïve Bayes in R. UNIT II – ADVANCED ANALYTICAL THEORY AND METHODS (6 hours) Introduction of Data Science – Basic Data Analytics using R – R Graphical User Interfaces – Data Import and Export – Attribute and Data Types – Descriptive Statistics – Exploratory Data Analysis – Visualization Before Analysis – Dirty Data – Visualizing a Single Variable – Examining Multiple Variables – Data Exploration Versus Presentation. UNIT I – INTRODUCTION TO DATA SCIENCE (6 hours) ![]() In this book, you will find a practicum of skills for data science. UB 812, Information Technology, SRM IT1110_Lesson-plan | This book will teach you how to do data science with R: You’ll learn how to get your data into R, get it into the most useful structure, transform it, visualise it and model it. ![]()
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