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. UNIT IV – HADOOP DISTRIBUTED FILE SYSTEM ARCHITECTURE (6 hours) The fact that key points are written under the video is quite helpful if you need a quick reminder when. We recommend reading this tutorial, in the sequence listed in the left menu. In this progression of courses, we will help both new and existing R users master R and expand their data science skills. In part, R owes its popularity to its open source distribution and massive user community. Big data from Technology Perspective: History of Hadoop-Components of Hadoop-Application Development in Hadoop-Getting your data in Hadoop-other Hadoop Component. The course is well structured, video lectures are good. 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 ». R is one of the fastest growing programming languages and tool of choice for analysts and data scientists.
Basic data science in r professional#
All of our trainers are working as Data Scientists with over 15+ years of professional experience. 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. ExcelR is the training delivery partner in the space of Data Science for 5 universities and 40+ premier educational institutions like IIM, BITS Pilani, Woxen School of Business, University of Malaysia, etc. Practical Data Science with R, Second Edition takes a practice-oriented approach to explaining basic principles in the ever expanding field of data science. Learn R for data science and explore the world of data analysis including the basic functions and methods of R programming and tools such as RStudio and. 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) It explains basic principles without the theoretical mumbo-jumbo and jumps right to the real use cases. UB 812, Information Technology, SRM IT1110_Lesson-plan | Practical Data Science with R lives up to its name.
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