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People with knowledge of R (programming language) are highly sought in today’s day and age where decisions are based on solid insights. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. Robust knowledge of R can provide professionals with lucrative opportunities and rewarding careers.

Our module has been designed by Subject Matter Experts who have exhaustively covered R (programming language). It will help you learn concepts of statistics along with wide variety of tools, functions and techniques in R. The course content equips students with not just theoretical knowledge but job-ready skills. Using data from the industry, our online R training course gives candidate exposure to real-life situations and opportunity to apply learning.

  • Gain a foundational understanding of business analytics
  • Master the R programming and understand how various statements are executed in R
  • Gain an in-depth understanding of data structure used in R and learn to import/export data in R
  • Define, understand and use the various apply functions and DPLYP functions
  • Understand and use the various graphics in R for data visualization
  • Gain understanding of statistical concepts, hypothesis testing method and regression models.
  • Learn and use clustering methods including K-means, DBSCAN, and hierarchical clustering

All professionals and students who want to learn from the best institute for online training. Every person who wants have a job/career in Analytics, Business Intelligence, Data Management, Actuarial Science or Research will benefit from this course.

  • FRM Certified Course
  • Downloadable Videos gives you 24/7 offline access
  • Downloadable PDFs for Lifetime
  • Prepared by Corporate trainers
  • 100% Placement Assistance provided
  • Course Completion Certificate will be provided

  • Lecture 1.1 : Introduction
  • Lecture 1.2 : R As Calculator
  • Lecture 1.3 : Functions and Packages
  • Lecture 1.4 : Objects
  • Lecture 1.5 : Vectors
  • Lecture 1.6 : Factors
  • Lecture 1.7 : Matrices and Arrays
  • Lecture 1.8 : Dataframes
  • Lecture 1.9 : Dataframes and DPLYR Package
  • Lecture 1.10 : DPLYR Package
  • Lecture 1.11 : If Condition Part 1
  • Lecture 1.12 : If Condition Part 2
  • Lecture 1.13 : Looping in R
  • Lecture 1.14 : User Defined Functions in R
  • Lecture 1.15 : Graphs
  • Lecture 1.16 : Measures of Central Tendency
  • Lecture 1.17 : Measures of Dispersion, Moments
  • Lecture 1.18 : Counting Techniques
  • Lecture 1.19 : Baiscs In Probability
  • Lecture 1.20 : Random Variables
  • Lecture 1.21 : Discrete Distributions
  • Lecture 1.22 : Continuous Distributions Part 1
  • Lecture 1.23 : Continuous Distributions Part 2
  • Lecture 1.24 : Sampling Theory
  • Lecture 1.25 : Estimation
  • Lecture 1.26 : Hypothesis Testing
  • Lecture 1.27 : Correlation and Regression
  • Lecture 1.28 : ANOVA
  • Lecture 1.29 : t-Test in R
  • Lecture 1.30 : Chi Square Test in R
  • Lecture 1.31 : ANOVA One Way
  • Lecture 1.32 : Two Sample t-Test in R
  • Lecture 1.33 : ANOVA Two Way
  • Lecture 1.34 : Z Population Proportions Test
  • Lecture 1.35 : Introduction to Charts and Graphs in R
  • Lecture 1.36 : GGPlot2 Part 1
  • Lecture 1.37 : GGPlot2 Part 2
  • Lecture 1.38 : GGPlot2 Part 3
  • Lecture 1.39 : GGPlot Themes
  • Lecture 1.40 : Corrplot and Corrgram
  • Lecture 1.41 : Date Time Functions in R
  • Lecture 1.42 : Tidyr Package
  • Lecture 1.43 : Stringr Package
  • Lecture 1.44 : Some Useful Functions in R
  • Lecture 1.45 : Machine Learning:Linear regression: lm()
  • Lecture 1.46 : Machine Learning:Linear Regression: Case Study
  • Lecture 1.47 : Machine Learning:Logistic regression: glm()
  • Lecture 1.48 : Machine Learning:Logistic regression Case Study
  • Lecture 1.49 : Machine Learning: K Mean Clustering
  • Lecture 1.50 : Machine Learning: Decision Tree
  • Lecture 1.51 : Machine Learning: Neural Nets

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Supported Operating Systems
  • Microsoft Windows 7 (or higher)
  • OSX (Last two major releases)
  • Most Linux Distributions
Supported Mobile Operating Systems
  • iOS 9 (or higher)
  • Android 6.0 (or higher)
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  • Android : Default browser in version 6.0 and above
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Course Curriculum

R Analytics
R Analytics 15:00:00
TAKE THIS COURSE
  • $225.00 $120.00
  • 45 Days

Instructors

1 STUDENTS ENROLLED