Data Science with R Training Online and Certification Course

igmGuru's Data Science with R Online program is designed by some of the best professionals in the industry and the faculties teaching at the best of the universities globally. The aim of the course is to help the learner gain knowledge of essential techniques and topics practiced in the industry. igmGuru's Data Science with R course is aimed at preparing our learners to comprehend and use Data Science with R programming in a more industry-ready fashion. igmGuru’s Data Science with R Training online program is the gateway towards your Data Science career and crack the Data Science with R Certification Exam.

Our Data Science with R course online covers the following topics:

1. Introduction to R programming: covers the basics of R programming, including data types, variables, control structures, and functions.

2. Data Wrangling: covers techniques for cleaning, transforming, and manipulating data in R, including the use of the dplyr and tidyr packages.

3. Exploratory Data Analysis: covers techniques for visualizing and summarizing data in R, including the use of the ggplot2 and lattice packages.

4. Statistical modeling: covers the basics of statistical modeling in R, including linear regression, logistic regression, and other model types.

5. Machine learning: covers the basics of machine learning in R, including supervised and unsupervised learning, and the use of popular packages such as caret and randomForest.

6. Data visualization: covers advanced data visualization techniques in R, including the use of the ggvis, plotly, and shiny packages.

7. Big data: covers techniques for working with large datasets in R, including the use of the dplyr, data.table and ff packages.

There are numerous ways of learning R programming for Data Science as there are endless courses floating around in the market. But what is it that makes this course stand apart from the rest. Here are a few points about this training program and its features which will help you decide.

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 globally used by data miners and statisticians to develop data analysis and statistical software. R and its libraries are being used for implementing graphical and statistical techniques, including classical statistical tests, linear and nonlinear modeling, classification, time-series analysis, clustering, and others. R can easily be extended via extensions and functions. Additionally, 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. Theoretical as well as application of the theoretical concepts through hands-on these statistical techniques are covered in our training course. The candidates are provided with various data sets for practicing and implementing statistical techniques during the session. These data sets can also be used to practice later on in the form of self-study, ultimately helping 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 need to focus on various use cases and some common applications/services of R. Once you gain theoretical knowledge, the course moves towards theoretically understanding Data Science workflow using R. The course will aid you in understanding the basic components of a Data Science model, from fetching data from your DB to building a model that is in a deployable form.

What are the key deliverables of the Data Science with R Course Online

As you will progress in the Data Science with R certification Training 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
  

- You will learn to use Data Science-specific libraries in R. For instance, frequently used libraries in data cleaning like plyr, dplyr, tidyr, stronger, etc.
- Data plotting libraries like ggplot2, lattice
- ML-based modules to build various regression and classification based algorithms like CART, randomForest, e1071, Rpart, etc.