R Programming for Data Science Training Course
Intellipaat
Course Summary
Intellipaat Data Analytics course with R Programming will help you be master in Data Manipulation with R programming, Data visualization, advance analytics topics like regressions, data mining using RStudio. You will work on real life projects and assignments to master data analytics.

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Course Description
About R Online Certification Course
What you will learn in this R Programming Training?
 Learn Data Science concepts of R and functioning of RCalculator
 Understand various functions like Stack, Merge and Strsplit
 Learn to create Pie charts, plots and vectors
 Assign value to variables, generate repeat and factor levels
 Performing sorting, analyze variance and the cluster
 ODBC Tables reading, linear and logistic regression
 Understand database connectivity
 Deploy R programming for Hadoop applications
Who should take this R Data Scientist Training Course?
 Software engineers and data analysts
 Business intelligence professionals
 SAS developers wanting to learn open source technology
 Those aspiring for a career in data science
What are the prerequisites for learning R programming?
We don’t expect any prior knowledge from your side while designing this course. A basic knowledge of programming language can be helpful.Why you should take R Programming training?
 70% of companies say analytics is integral to making decisions – IBM Study
 19% is annual growth rate of the Analytics market – Pringle & Company
 R programmers can earn excess of $110,000 per year – O’Reilly Survey

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Course Syllabus
R Programming Course Content
Introduction to RR language for statistical programming, the various features of R, introduction to R Studio, the statistical packages, familiarity with different data types and functions, learning to deploy them in various scenarios, use SQL to apply ‘join’ function, components of R Studio like code editor, visualization and debugging tools, learn about Rbind.RPackagesR Functions, code compilation and data in welldefined format called RPackages, learn about RPackage structure, Package metadata and testing, CRAN (Comprehensive R Archive Network), Vector creation and variables values assignment.Sorting DataframeR functionality, Rep Function, generating Repeats, Sorting and generating Factor Levels, Transpose and Stack Function.Matrices and VectorsIntroduction to matrix and vector in R, understanding the various functions like Merge, Strsplit, Matrix manipulation, rowSums, rowMeans, colMeans, colSums, sequencing, repetition, indexing and other functions.Reading data from external filesUnderstanding subscripts in plots in R, how to obtain parts of vectors, using subscripts with arrays, as logical variables, with lists, understanding how to read data from external files.Generating plotsGenerate plot in R, Graphs, Bar Plots, Line Plots, Histogram, components of Pie Chart.Analysis of Variance (ANOVA)Understanding Analysis of Variance (ANOVA) statistical technique, working with Pie Charts, Histograms, deploying ANOVA with R, one way ANOVA, two way ANOVA.Kmeans ClusteringKMeans Clustering for Cluster & Affinity Analysis, Cluster Algorithm, cohesive subset of items, solving clustering issues, working with large datasets, association rule mining affinity analysis for data mining and analysis and learning cooccurrence relationships.Association Rule MiningIntroduction to Association Rule Mining, the various concepts of Association Rule Mining, various methods to predict relations between variables in large datasets, the algorithm and rules of Association Rule Mining, understanding single cardinality.Regression in RUnderstanding what is Simple Linear Regression, the various equations of Line, Slope, YIntercept Regression Line, deploying analysis using Regression, the least square criterion, interpreting the results, standard error to estimate and measure of variation.Analyzing Relationship with RegressionScatter Plots, Two variable Relationship, Simple Linear Regression analysis, Line of best fitAdvance RegressionDeep understanding of the measure of variation, the concept of coefficient of determination, FTest, the test statistic with an Fdistribution, advanced regression in R, prediction linear regression.Logistic RegressionLogistic Regression Mean, Logistic Regression in R.Advance Logistic RegressionAdvanced logistic regression, understanding how to do prediction using logistic regression, ensuring the model is accurate, understanding sensitivity and specificity, confusion matrix, what is ROC, a graphical plot illustrating binary classifier system, ROC curve in R for determining sensitivity/specificity tradeoffs for a binary classifier.Receiver Operating Characteristic (ROC)Detailed understanding of ROC, area under ROC Curve, converting the variable, data set partitioning, understanding how to check for multicollinearlity, how two or more variables are highly correlated, building of model, advanced data set partitioning, interpreting of the output, predicting the output, detailed confusion matrix, deploying the HosmerLemeshow test for checking whether the observed event rates match the expected event rates.Kolmogorov Smirnov ChartData analysis with R, understanding the WALD test, MC Fadden’s pseudo Rsquared, the significance of the area under ROC Curve, Kolmogorov Smirnov Chart which is nonparametric test of one dimensional probability distribution.Database connectivity with RConnecting to various databases from the R environment, deploying the ODBC tables for reading the data, visualization of the performance of the algorithm using Confusion Matrix.Integrating R with HadoopCreating an integrated environment for deploying R on Hadoop platform, working with R Hadoop, RMR package and R Hadoop Integrated Programming Environment, R programming for MapReduce jobs and Hadoop execution.R Case StudiesLogistic Regression Case Study In this case study you will get a detailed understanding of the advertisement spends of a company that will help to drive more sales. You will deploy logistic regression to forecast the future trends, detect patterns, uncover insights and more all through the power of R programming. Due to this the future advertisement spends can be decided and optimized for higher revenues.Multiple Regression Case Study You will understand how to compare the miles per gallon (MPG) of a car based on the various parameters. You will deploy multiple regression and note down the MPG for car make, model, speed, load conditions, etc. It includes the model building, model diagnostic, checking the ROC curve, among other things.Receiver Operating Characteristic (ROC) case studyYou will work with various data sets in R, deploy data exploration methodologies, build scalable models, predict the outcome with highest precision, diagnose the model that you have created with various real world data, check the ROC curve and more.R Programming ProjectsProject 1Domain – Restaurant Revenue PredictionData set – SalesProject Description – This project involves predicting the sales of a restaurant on the basis of certain objective measurements. This project will give real time industry experience on handling multiple use cases and derive the solution. This project gives insights about feature engineering and selection.Project 2Domain – Data AnalyticsObjective – To predict about the class of a flower using its petal’s dimensionsProject 3Domain – FinanceObjective – The project aims to find the most impacting factors in preferences of prepaid model, also identifies which are all the variables highly correlated with impacting factorsProject 4Domain – Stock MarketObjective – This project focuses on Machine Learning by creating predictive data model to predict future stock prices
This course is listed under
Development & Implementations
and Data & Information Management
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