Learn Data Science with R Programming You would invariably find a lot of ways to learn R programming for data science from the courses floating in the market. But what is it that makes this course stand apart from the rest. I will give you certain points about this course and its features which will help you decide. Welcome to R programming. R is an open-source programming language used for statistical computing and graphics supported by the R Foundation for Statistical Computing. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. R and its libraries are used for implementing statistical and graphical techniques, including linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, and others. R is easily extensible through functions and extensions, and the R community is noted for its active contributions in terms of packages Data Science with R certification course has been designed keeping in mind about learners who have zero to some level of exposure to R. Any ideal session in this course would dedicate a good amount of time understanding the theoretical part after which we will be moving on to the application of theoretical concepts by doing hands-on these statistical techniques. You would be provided with a lot of data set to practice and implement statistical techniques during the session and also to practice later on in the form of selfstudy which will help you in your journey to learn data science with R programming. The three main pillars to learn data science with R programming are 1. Application of mathematical and statistical concepts 2. Expressing them using a programming language or a tool/platform 3. Particular business domain When learners learn data science with R programming modules, they will understand the number of focuses that have been put on various use cases, some of the very famous applications/services which use R, and then we gradually move to understand data science workflow using R theoretically. We will help you understand the basic components of any data science model, right from fetching your data from your database to building a model that is in a deployable form. What are the key deliverables As you will progress in the Data Science with R certification program, you will acquire the below skills Introduction and implementation of Statistical techniques Understanding the data with respect to a business problem Data wrangling techniques Data representation/visualization for insight generation Understanding and building machine learning workflows Understanding various model parameters and their role Hyper tuning statistical models Deploying statistical models Maintaining statistical models With respect to the above steps, you will also learn how to use data science specific libraries in R e.g. Frequently used libraries in data cleaning like plyr, dplyr, tidyr, stronger, etc; data plotting libraries like ggplot2, lattice; machine learning-based modules for building various regression and classification based algorithms like CART, randomForest, e1071, Rpart, etc. These will help learners to learn data science with R programming.