R for Data Analysis, Statistics and Data Science

R for Data Analysis, Statistics and Data Science, available at $19.99, has an average rating of 4.3, with 79 lectures, based on 78 reviews, and has 2452 subscribers.

You will learn about 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 This course is ideal for individuals who are Beginner who wants to apply R for Statistics and Data Analysis It is particularly useful for Beginner who wants to apply R for Statistics and Data Analysis.

Enroll now: R for Data Analysis, Statistics and Data Science

Summary

Title: R for Data Analysis, Statistics and Data Science

Price: $19.99

Average Rating: 4.3

Number of Lectures: 79

Number of Published Lectures: 79

Number of Curriculum Items: 79

Number of Published Curriculum Objects: 79

Original Price: $89.99

Quality Status: approved

Status: Live

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

Who Should Attend

Beginner who wants to apply R for Statistics and Data Analysis

Target Audiences

Beginner who wants to apply R for Statistics and Data Analysis

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 Statisticsin 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.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Population Vs Sample

Lecture 3: Statistics Introduction

Chapter 2: Basic R Programming Fundamentals

Lecture 1: Installing R on Windows

Lecture 2: Installing RStudio & Look around RStudio Interface

Lecture 3: First R Program & Basic Mathematical Operations

Lecture 4: Data Types & Variables

Lecture 5: Relational & Logical Operators

Chapter 3: Vectors, Matrices, Lists and Dataframes

Lecture 1: Creating Vectors

Lecture 2: Logical Vectors

Lecture 3: Factors

Lecture 4: Creating Matrices & diag Function

Lecture 5: Creating Lists

Lecture 6: What are Data Frames

Lecture 7: Creating Data Frames

Lecture 8: Subseting Data Frame

Lecture 9: Import Data from Text & CSV Files

Lecture 10: Missing Data in Data Frames

Chapter 4: Data Handling using dplyr Package

Lecture 1: dplyr Package

Lecture 2: dplyr select() – Select Columns of Data Frame

Lecture 3: dplyr filter() – Extract Rows from Data Frame

Lecture 4: dplyr arrange – Sort or Reorder rows of Data Frame

Lecture 5: dplyr rename() – Renaming Columns of Data Frame

Lecture 6: dplyr mutate() – Mutate Data Frames

Lecture 7: dplyrgroup_by() – Generate Summary Statistics

Lecture 8: dplyr %% – Pipeline Operator

Chapter 5: Data Visualization in R

Lecture 1: Bar Plots

Lecture 2: Histograms

Lecture 3: Scatter & Line Plots

Lecture 4: Box Plots

Lecture 5: Multiple Plots in a Layout

Chapter 6: Qualitative and Quantitative Data

Lecture 1: Qualitative Data

Lecture 2: Visualizing Qualitative Data

Lecture 3: Quantitative Data

Lecture 4: Visualizing Quantitative Data

Lecture 5: Visualizing Stock Price Quantitative Data

Chapter 7: Descriptive Statistics

Lecture 1: Min, Max, Sum, Prod and Sort functions on Quantitative Data

Lecture 2: Mean or Arithmetic Mean

Lecture 3: Geometric Mean

Lecture 4: Applications of Geometric Mean

Lecture 5: Harmonic Mean

Lecture 6: Median and Mode

Lecture 7: Outliers

Lecture 8: Quartiles and Quantiles

Lecture 9: Variance and Standard Deviation

Lecture 10: Stock Price Data – Variance and Standard Deviation

Lecture 11: Correlation and Covariance

Lecture 12: Stock Price Data – Correlation and Covariance

Chapter 8: Bivariate and Multivariate Data

Lecture 1: Bivariate Qualitative Data

Lecture 2: Bivariate Quantitative Data

Lecture 3: Multivariate Data

Chapter 9: Probability Distributions

Lecture 1: Probability Distribution

Lecture 2: Uniform Distribution

Lecture 3: Normal Distribution

Chapter 10: Inferential Statistics – Hypothesis Testing

Lecture 1: p-value – Statistical Hypothesis

Lecture 2: Degrees of Freedom

Lecture 3: Confidence Interval

Lecture 4: Hypothesis Testing

Lecture 5: Chi-squared test

Chapter 11: More on – R Programming Fundamentals

Lecture 1: Sequences Operator

Lecture 2: Replicate Function

Lecture 3: Conditional Control Statements

Lecture 4: Loops or Iterative Statements

Lecture 5: Functions

Chapter 12: More on – Vectors

Lecture 1: Subsetting Vectors

Lecture 2: Vector Matching Operator & Methods

Lecture 3: Vector Arithmetic & Mathematical Functions

Lecture 4: Vector – Implicit & Explicit Coercion

Chapter 13: More on – Matrix and Lists

Lecture 1: Matrix – Naming & Binding Rows-Columns

Lecture 2: Subsetting Matrix

Lecture 3: Matrix Operations & Functions

Lecture 4: Subsetting List

Lecture 5: List – Naming, Subset Operator & Concatenation

Chapter 14: More on – Data Frames

Lecture 1: Data Frame subset() function

Lecture 2: Data Frame rbind() and cbind()

Lecture 3: Data Frame edit() function

Chapter 15: More on – Data Import and Export

Lecture 1: Import Data from RDS Files

Lecture 2: Import Data from Internet

Lecture 3: Exporting Data to CSV Files

Instructors

Syed Mohiuddin
Instructor

Rating Distribution

1 stars: 2 votes

2 stars: 4 votes

3 stars: 11 votes

4 stars: 36 votes

5 stars: 25 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!