Data Science with R Training Course

Data Science with R online training course covers foundational data science tools and techniques, including getting, cleaning, and exploring data, programming in R, and conducting reproducible research.

  • 50000
  • 55000
  • Course Includes
  • Live Class Practical Oriented Training
  • 90 + Hrs Instructor LED Training
  • 45 + Hrs Practical Exercise
  • 25 + Hrs Project Work & Assignment
  • Timely Doubt Resolution
  • Dedicated Student Success Mentor
  • Certification & Job Assistance
  • Free Access to Workshop & Webinar
  • No Cost EMI Option


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What you will learn

  • Explore R data structures and syntaxes
  • Read and write data from a local file to a cloud-hosted database
  • Work with data, get summaries, and transform them to fit your needs
  • Explore R language fundamentals, including basic syntax, variables, and types
  • Create functions and use control flow
  • Learn to program in R at a good level
  • Learn how to build and use matrices in R
  • Understand the Normal distribution
  • Foundational R programming concepts such as data types, vectors arithmetic, and indexing
  • How to perform operations in R including sorting, data wrangling using dplyr, and making plots

Requirements

  • Know the fundamentals of programming. Know the basics of SQL. Familiar with the basic math and statistic concepts

Description

|| About Data Science with R Training Course

The Data Science with R programming certification online training covers data exploration, data visualization, predictive analytics, and descriptive analytics techniques with the R language. You will learn about R packages, how to import and export data in R, data structures in R, various statistical concepts, cluster analysis, and forecasting. The training on Data Science with R provides the skills required to work with real data sets and provide an opportunity to use data to provide data-driven strategic and tactical recommendations. This training will provide some insights on techniques such as linear and logistic regression, ANOVA, Segmentation, Ensemble models, SVM and machine learning in big data. In addition to technical skills, the program also allows students to build effective leadership and communication skills to advance their career upon graduation.

 

The Data Science with R covers data exploration, data visualization, predictive analytics, and descriptive analytics techniques with the R language. You will learn about R packages, how to import and export data in R, data structures in R, various statistical concepts, cluster analysis, and forecasting. R programming training builds the proficiency in using R programming language for statistical computing and graphics. R, a language and environment, is gaining popularity in getting insight in complex data. The business analyst and other professionals dealing in large amount of data can derive results using the ready-made functions.

Course Content

Lecture-1 R Fundamentals

·      History of  R

·      Introduction to R

·      The R environment

·      What is Statistical Programming?

·      Why use a command line? 

·      Your first R session

·      Practical Exercise              

Lecture-2 Basics of R

·      Recording your work 

·      Basic features of R

·      Calculating with R 

·      Named storage

·      Functions 

·      Exact or approximate? 

·      R is case-sensitive 

·      Listing the objects in the workspace 

·      Vectors

·      Extracting elements from vectors 

·      Vector arithmetic 

·      Simple patterned vectors 

·      Missing values and other special values

·      Character vectors 

·      Factors 

·      More on extracting elements from vectors 

·      Matrices and arrays 

·      Data frames

·      Dates and times

·      Practical Exercise              

Lecture-3 Import and Export data in R

·      Importing data in to R

·      CSV File

·      Excel File

·      Import data from text table

·      SAS and SPSS datasets

·      Exporting Data from R

·      CSV File

·      Text Table

·      Excel File

·      SAS dataset

·      Practical Exercise              

Lecture-4 Merge / Join

·      Inner Join

·      Left Join

·      Right Join

·      Full Join

·      Anti Join

·      Semi Join

·      Practical Exercise              

Lecture-5 Programming statistical graphics

·      High-level plots

·      Bar charts and dot charts

·      Pie charts

·      Histograms

·      Box plots

·      Scatterplots

·      QQ plots

·      Density Plot

·      Choosing a high-level graphic

·      Low-level graphics functions

·      The plotting region and margins

·      Adding to plots

·      Setting graphical parameters

·      Practical Exercise              

Lecture-6 Programming with R

·      Flow control

·      The for() loop

·      The if() statement

·      The while() loop

·      The repeat loop, and the break and next statements

·      Apply

·      Sapply

·      Lapply

·      Managing complexity through functions • What are functions? 

·      Scope of variables

·      Practical Exercise