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Data Science
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R for Data Analysis, Statistics and Data Science

Data Analysis & Data Science using R : Descriptive & Inferential Statistics, Data Visualization, Hypothesis Testing

     
  • 3.9
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  • Reviews ( 77 )
₹1999
R for Data Analysis, Statistics and Data Science

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  • iconudemy
  • icon3.9 (77 reviews )
  • icon8 total hours
  • iconenglish
  • iconOnline - Self Paced
  • iconcourse
  • iconUdemy

About R for Data Analysis, Statistics and Data Science

Welcome to this course of R for Data Analysis, Statistics, and Data Science, and become an R Professional which is one of the most favored skills, that employers need.

Whether you are new to statistics and data analysis or have never programmed before in R Language, this course is for you! This course covers the Statistical Data Analysis Using R programming language. This course is self-paced. There is no need to rush, you can learn on your own schedule.

This course will help anyone who wants to start a саrееr as a Data Analyst or Data Scientist.

This course begins with the introduction to R that will help you write R code in no time. This course will provide you with everything you need to know about Statistics.

In this course we will cover the following topics:

· R Programming Fundamentals

· Vectors, Matrices & Lists in R

· Data Frames

· Importing Data in Data Frame

· Data Wrangling using dplyr package

· Qualitative and Quantitative Data

· Descriptive and Inferential Statistics

· Hypothesis Testing

· Probability Distribution

This course teaches Data Analysis and Statistics in a practical manner with hands-on experience with coding screen-cast.

Once you complete this course, you will be able to perform Data Analysis to solve any complex Analysis with ease.

What You Will Learn?

  • About Qualitative, Quantitative, Bivariate and Multivariate Data.
  • Descriptive Statistics ie of Mean, Median, Quartiles, Quantiles, Variance and Standard Deviation.
  • Correlation and Covariance.
  • Applications of Descriptive Statistics on Stock Price Data.
  • Probability Distributions.
  • Inferential Statistics - Hypothesis Testing.
  • Fundamentals of R Programming & Work with RStudio.
  • Use Vectors, Matrices, Lists, Data Frames.
  • Importing and Handling CSV files.
  • Using dplyr Package for Data Wrangling or Handling.
  • Data Visualization in R.