R for Data Analysis Statistics and Data Science

Data Analysis & Data Science using R : Descriptive & Inferential Statistics, Data Visualization, Hypothesis Testing
R for Data Analysis Statistics and Data Science
File Size :
2.08 GB
Total length :
8h 9m



Syed Mohiuddin


Last update




R for Data Analysis Statistics and Data Science

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

R for Data Analysis Statistics and Data Science


No prior knowledge or technical backgrounds is required


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 DistributionThis 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.


Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Population Vs Sample

Lecture 3 Statistics Introduction

Section 2: Basic R Programming Fundamentals

Lecture 4 Installing R on Windows

Lecture 5 Installing RStudio & Look around RStudio Interface

Lecture 6 First R Program & Basic Mathematical Operations

Lecture 7 Data Types & Variables

Lecture 8 Relational & Logical Operators

Section 3: Vectors, Matrices, Lists and Dataframes

Lecture 9 Creating Vectors

Lecture 10 Logical Vectors

Lecture 11 Factors

Lecture 12 Creating Matrices & diag Function

Lecture 13 Creating Lists

Lecture 14 What are Data Frames

Lecture 15 Creating Data Frames

Lecture 16 Subseting Data Frame

Lecture 17 Import Data from Text & CSV Files

Lecture 18 Missing Data in Data Frames

Section 4: Data Handling using dplyr Package

Lecture 19 dplyr Package

Lecture 20 dplyr select() – Select Columns of Data Frame

Lecture 21 dplyr filter() – Extract Rows from Data Frame

Lecture 22 dplyr arrange – Sort or Reorder rows of Data Frame

Lecture 23 dplyr rename() – Renaming Columns of Data Frame

Lecture 24 dplyr mutate() – Mutate Data Frames

Lecture 25 dplyrgroup_by() – Generate Summary Statistics

Lecture 26 dplyr %% – Pipeline Operator

Section 5: Data Visualization in R

Lecture 27 Bar Plots

Lecture 28 Histograms

Lecture 29 Scatter & Line Plots

Lecture 30 Box Plots

Lecture 31 Multiple Plots in a Layout

Section 6: Qualitative and Quantitative Data

Lecture 32 Qualitative Data

Lecture 33 Visualizing Qualitative Data

Lecture 34 Quantitative Data

Lecture 35 Visualizing Quantitative Data

Lecture 36 Visualizing Stock Price Quantitative Data

Section 7: Descriptive Statistics

Lecture 37 Min, Max, Sum, Prod and Sort functions on Quantitative Data

Lecture 38 Mean or Arithmetic Mean

Lecture 39 Geometric Mean

Lecture 40 Applications of Geometric Mean

Lecture 41 Harmonic Mean

Lecture 42 Median and Mode

Lecture 43 Outliers

Lecture 44 Quartiles and Quantiles

Lecture 45 Variance and Standard Deviation

Lecture 46 Stock Price Data – Variance and Standard Deviation

Lecture 47 Correlation and Covariance

Lecture 48 Stock Price Data – Correlation and Covariance

Section 8: Bivariate and Multivariate Data

Lecture 49 Bivariate Qualitative Data

Lecture 50 Bivariate Quantitative Data

Lecture 51 Multivariate Data

Section 9: Probability Distributions

Lecture 52 Probability Distribution

Lecture 53 Uniform Distribution

Lecture 54 Normal Distribution

Section 10: Inferential Statistics – Hypothesis Testing

Lecture 55 p-value – Statistical Hypothesis

Lecture 56 Degrees of Freedom

Lecture 57 Confidence Interval

Lecture 58 Hypothesis Testing

Lecture 59 Chi-squared test

Section 11: More on – R Programming Fundamentals

Lecture 60 Sequences Operator

Lecture 61 Replicate Function

Lecture 62 Conditional Control Statements

Lecture 63 Loops or Iterative Statements

Lecture 64 Functions

Section 12: More on – Vectors

Lecture 65 Subsetting Vectors

Lecture 66 Vector Matching Operator & Methods

Lecture 67 Vector Arithmetic & Mathematical Functions

Lecture 68 Vector – Implicit & Explicit Coercion

Section 13: More on – Matrix and Lists

Lecture 69 Matrix – Naming & Binding Rows-Columns

Lecture 70 Subsetting Matrix

Lecture 71 Matrix Operations & Functions

Lecture 72 Subsetting List

Lecture 73 List – Naming, Subset Operator & Concatenation

Section 14: More on – Data Frames

Lecture 74 Data Frame subset() function

Lecture 75 Data Frame rbind() and cbind()

Lecture 76 Data Frame edit() function

Section 15: More on – Data Import and Export

Lecture 77 Import Data from RDS Files

Lecture 78 Import Data from Internet

Lecture 79 Exporting Data to CSV Files

Beginner who wants to apply R for Statistics and Data Analysis

Course Information:

Udemy | English | 8h 9m | 2.08 GB
Created by: Syed Mohiuddin

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