Data Science with R Training Certification Course Online

SKU: 8410
10 Lesson
24 Hours
If you wish to become an expert in data analytics with the R programming language, then igmGuru’s Data Science with R training course is a boon for you. Our Data Science with R online program covers a wide array of topics including predictive analytics, descriptive analytics, data visualization, and data exploration techniques with the R language. Prepare for Data Science with R certification as you learn to import and export data in R, cluster analysis, and forecasting.

Data Science with R Course Overview

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 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 and 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:

  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

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.

  • A good amount of content has also been dedicated to Natural Language Processing (NLP) techniques and various web scraping methodologies. NLP has been gaining a lot of popularity lately, owing to its use in our day-to-day life. FB posts, tweets, Mails, and WhatsApp chats are apt input for any NLP-based model. You will become experienced in NLP-based openings, which are nowadays considered to be a specialty within the Machine Learning branch.

    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.

Key Features

Data Science with R Training Modules

1. Overview
2. Business Decisions and Analytics
3. Types of Business Analytics
4. Applications of Business Analytics
5. Data Science Overview

1. Overview
2. Importance of R
3. Data Types and Variables in R
4. Operators in R
5. Conditional Statements in R
6. Loops in R
7. R script
8. Functions in R

1. Overview
2. Identifying Data Structures
3. Demo: Identifying Data Structures
4. Assigning Values to Data Structures
5. Data Manipulation
6. Demo: Assigning values and applying functions

1. Overview
2. Introduction to Data Visualization
3. Data Visualization using Graphics in R
4. ggplot2
5. File Formats of Graphic Outputs

1. Introduction to Hypothesis
2. Types of Hypothesis
3. Data Sampling
4. Confidence and Significance Levels

1. Overview
2. Hypothesis Test
3. Parametric Test
4. Non-Parametric Test
5. Hypothesis Tests about Population Means
6. Hypothesis Tests about Population Variance
7. Hypothesis Tests about Population Proportions

1. Overview
2. Introduction to Regression Analysis
3. Types of Regression Analysis Models
4. Linear Regression
5. Demo: Simple Linear Regression
6. Non-Linear Regression
7. Demo: Regression Analysis with Multiple Variables
8. Cross Validation
9. Non-Linear to Linear Models
10. Principal Component Analysis
11. Factor Analysis

1. Overview
2. Classification and Its Types
3. Logistic Regression
4. Support Vector Machines
5. Demo: Support Vector Machines
6. K-Nearest Neighbours
7. Naive Bayes Classifier
8. Demo: Naive Bayes Classifier
9. Decision Tree Classification
10. Demo Decision Tree Classification
11. Random Forest Classification
12. Evaluating Classifier Models
13. Demo: K-Fold Cross Validation

1. Overview
2. Introduction to Clustering
3. Clustering Methods
4. Demo: K-means Clustering
5. Demo: Hierarchical Clustering

1. Overview
2. Association Rule
3. Apriori Algorithm
4. Demo: Apriori Algorithm

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Data Science with R Course Fees


US $ 199.00
Refund Policy
  • Duration : 24 hrs
  • Lifetime Free Upgrade
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Online Class Room Program

US $ 599.00
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  • Duration : 24 Hrs
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Classes Starting From
  • Fast Track Batch 22 Jul 2024
  • Weekday Batch 22 Jul 2024
  • Weekend Batch 27 Jul 2024

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Data Science with R Certification Exam

A Data Science with R certification exam is a test that assesses a candidate's knowledge and skills in using R for data science tasks. The exam typically covers topics such as data wrangling, exploratory data analysis, statistical modeling, machine learning, and data visualization.

The format of the exam can vary depending on the organization offering the certification. Some exams may be taken online and consist of multiple-choice questions, while others may be taken in-person and consist of a combination of multiple-choice and open-ended questions.

To prepare for a Data Science with R certification exam, it is recommended to have a solid understanding of the R programming language, as well as experience working with data in R. Practicing with sample exam questions, working on data science projects, and reviewing relevant materials such as tutorials and documentation can also help in preparing for the exam.

It's important to note that different certification exams might have different focus and requirements, you should check with the organization offering the certification for more information.

Data Science with R Certification Exam

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