Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: aac, 44100 Hz
Language: English | VTT | Size: 2.08 GB | Duration: 8 hours

What you'll 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
Requirements
No prior knowledge or technical backgrounds is required
Description
Welcome to this course of Statistical Data Analysis Using R, and become an R Professional which is one of the most favored skills, that employer's 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 learn on your own schedule.
This course will help anyone who wants to start a саrееr as a Data Analyst.
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:
В· R Programming Fundamentals
В· Vectors, Matrices & Lists in R
В· Data Frames
В· Importing Data in Data Frame
В· Data Wrangling using dplyr package
В· Qualitative and Quantitative Data
В· Descriptive and Inferential Statistics
В· Hypothesis Testing
В· 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.
Who this course is for:
Beginner who wants to apply R for Statistics and Data Analysis