R online course has been designed after consulting some of the best professionals in the industry and the faculties teaching at the best of the universities.

R online course has been designed after

consulting some of the best professionals in the industry and the

faculties teaching at the best of the universities. The reason we

have done this is because we wanted to embed the topics and techniques

which are practiced in the industry, conduct them with the help of pedagogy

which is followed across universities – kind of practical data science with R

implementation. In doing so, we prepare our learners to learn data science with

R programming in a more industry/job ready fashion. IgmGuru’s Data Science with

R certification course is the gateway towards your Data Science career.

R online course

Data Science with R certification course has been designed after consulting some of the best professionals in the industry and the faculties teaching at the best of the universities. The reason we have done this is because we wanted to embed the topics and techniques which are practiced in the industry, conduct them with the help of pedagogy which is followed across universities – kind of practical data science with R implementation. In doing so, we prepare our learners to learn data science with R programming in a more industry/job ready fashion. IgmGuru’s Data Science with R certification course is the gateway towards your Data Science career.

- Home
- Data Science & BI
- Learn Data Science with R Programming

## R for Data Science Course Overview

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 self-study 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

- Application of mathematical and statistical concepts
- Expressing them using a programming language or a tool/platform
- 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.

A good amount of content has also been dedicated to Natural Language Processing techniques and various web scraping methodologies. Of late, NLP is gaining a lot of popularity owing to use in our day to day life e.g. Mails, tweets, FB posts, WhatsApp chats are ideal input for any NLP based models. You are very like to experience NLP based openings which are nowadays considered to be a specialty within the Machine Learning branch. These are all instances that you could experience while you learn data science with R programming.

Hence assessing the market-based demands, we have specifically designed modules to upskill you in this area as well – mostly to learn data science with R programming. A very significant model in the area of NLP is Sentiment Analysis which is something we will be building to start things of and will move on to build much complex algorithms in this area.

Data Science with R certification course has been designed after consulting some of the best professionals in the industry and the faculties teaching at the best of the universities. The reason we have done this is because we wanted to embed the topics and techniques which are practiced in the industry, conduct them with the help of pedagogy which is followed across universities – kind of practical data science with R implementation. In doing so, we prepare our learners to learn data science with R programming in a more industry/job ready fashion. IgmGuru’s Data Science with R certification course is the gateway towards your Data Science career.

- Home
- Data Science & BI
- Learn Data Science with R Programming

## R for Data Science Course Overview

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 self-study 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

- Application of mathematical and statistical concepts
- Expressing them using a programming language or a tool/platform
- 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.

A good amount of content has also been dedicated to Natural Language Processing techniques and various web scraping methodologies. Of late, NLP is gaining a lot of popularity owing to use in our day to day life e.g. Mails, tweets, FB posts, WhatsApp chats are ideal input for any NLP based models. You are very like to experience NLP based openings which are nowadays considered to be a specialty within the Machine Learning branch. These are all instances that you could experience while you learn data science with R programming.

Hence assessing the market-based demands, we have specifically designed modules to upskill you in this area as well – mostly to learn data science with R programming. A very significant model in the area of NLP is Sentiment Analysis which is something we will be building to start things of and will move on to build much complex algorithms in this area.

Data Science with R certification course has been designed after consulting some of the best professionals in the industry and the faculties teaching at the best of the universities. The reason we have done this is because we wanted to embed the topics and techniques which are practiced in the industry, conduct them with the help of pedagogy which is followed across universities – kind of practical data science with R implementation. In doing so, we prepare our learners to learn data science with R programming in a more industry/job ready fashion. IgmGuru’s Data Science with R certification course is the gateway towards your Data Science career.

- Home
- Data Science & BI
- Learn Data Science with R Programming

## R for Data Science Course Overview

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 self-study 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

- Application of mathematical and statistical concepts
- Expressing them using a programming language or a tool/platform
- 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.

A good amount of content has also been dedicated to Natural Language Processing techniques and various web scraping methodologies. Of late, NLP is gaining a lot of popularity owing to use in our day to day life e.g. Mails, tweets, FB posts, WhatsApp chats are ideal input for any NLP based models. You are very like to experience NLP based openings which are nowadays considered to be a specialty within the Machine Learning branch. These are all instances that you could experience while you learn data science with R programming.

Hence assessing the market-based demands, we have specifically designed modules to upskill you in this area as well – mostly to learn data science with R programming. A very significant model in the area of NLP is Sentiment Analysis which is something we will be building to start things of and will move on to build much complex algorithms in this area.

- Home
- Data Science & BI
- Learn Data Science with R Programming

## R for Data Science Course Overview

The three main pillars to learn data science with R programming are

- Application of mathematical and statistical concepts
- Expressing them using a programming language or a tool/platform
- Particular business domain

What are the key deliverables

- 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

- Home
- Data Science & BI
- Learn Data Science with R Programming

## R for Data Science Course Overview

The three main pillars to learn data science with R programming are

- Application of mathematical and statistical concepts
- Expressing them using a programming language or a tool/platform
- Particular business domain

What are the key deliverables

- 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

- Home
- Data Science & BI
- Learn Data Science with R Programming

## R for Data Science Course Overview

The three main pillars to learn data science with R programming are

- Application of mathematical and statistical concepts
- Expressing them using a programming language or a tool/platform
- Particular business domain

What are the key deliverables

- 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

- Home
- Data Science & BI
- Learn Data Science with R Programming

## R for Data Science Course Overview

The three main pillars to learn data science with R programming are

- Application of mathematical and statistical concepts
- Expressing them using a programming language or a tool/platform
- Particular business domain

What are the key deliverables

- 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