R programming with Statistics for Data science

h264, yuv420p, 1280×720 |ENGLISH, aac, 48000 Hz, 2 channels, s16 | 8h 03 mn | 3.9 GB
Instructors: Hands-On System

Learn Hands-On applied statistics and data manipulation using real case studies

Learn Hands-On applied statistics and data manipulation using real case studies

What you’ll learn

Learn R programming from scratch
Use of R Studio
Principles of programming
Concept of vectors in R
Create your own variable
Data types in R
Know the use of while() and for()
Build and use matrices in R
Use matrix() function, learn rbind() and cbind()
Install packages in R
Understand the Normal distribution
Practice working with statistical data in R
Add your own functions into apply statements
R functions
Create your own function

Requirements

No prior knowledge of programming is required. Just a basic knowledge of computer applications is enough for this course

Description

R is most popular and the leading open source language in data science and statistics. Today, R language is the choice for most data science professionals in every industry and academics.

This course is thoroughly described R programming, Statistics and Data Science for beginners using real life examples.

Let’s parse that.

This course does not require a prior quantitative or mathematics background. It starts fundamental concepts of R programming, introducing basic concepts such as the mean, median etc and eventually covers all aspects of an analytics (or) data science career from analyzing and preparing raw data to visualizing your findings.

This course is an introduction to Data Science and Statistics using the R programming language. It covers both the theoretical aspects of Statistical concepts and the practical implementation using R.

Real life examples: Every concept is explained with the help of examples, case studies and source code in R wherever necessary.

Course material in the form for articles include in this program

Data Analysis with R: Datatype and Data structures in R, Vectors, Arrays, Matrices, Lists, Data Frames, Reading data from files, Aggregating, Sorting & Merging Data Frames.

Linear Regression: Regression, Simple Linear Regression in Excel, Simple Linear Regression in R, Multiple Linear Regression in R, Categorical variables in regression, Robust regression, Parsing regression diagnostic plots

Descriptive Statistics: Mean, Median, Mode, Standard Deviation, Frequency Distributions,

Inferential Statistics: Hypothesis testing, Test statistic, Test of significance.

Who this course is for:

Anyone who want to explore R programming
This course is designed for Beginner to professional

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