RStudio Bootcamp- for Data Management, Statistics graphics

RStudio Bootcamp- for Data Management, Statistics & graphics, available at $64.99, has an average rating of 4.6, with 35 lectures, 7 quizzes, based on 16 reviews, and has 82 subscribers.

You will learn about Primary goal is to provide users with an easy way to learn how to perform an analytics task in RStudio. Includes many common tasks, including data management, descriptive summaries, inferential procedures, regression analysis, multivariate methods and graphics. Includes some complex applications such as Simulation Tried to provide a simple classroom type approach that is easy to understand for a new user, and supplied several solutions where deemed necessary. This course is ideal for individuals who are If you intend to be a professional analyst, who use multiple statistical packages daily, this course is for you ! or Useful for statisticians, epidemiologists, economists, engineers, physicians, sociologists and others engaged in data analysis. or This course intends to bolster the analytic abilities of a new user as well It is particularly useful for If you intend to be a professional analyst, who use multiple statistical packages daily, this course is for you ! or Useful for statisticians, epidemiologists, economists, engineers, physicians, sociologists and others engaged in data analysis. or This course intends to bolster the analytic abilities of a new user as well.

Enroll now: RStudio Bootcamp- for Data Management, Statistics & graphics

Summary

Title: RStudio Bootcamp- for Data Management, Statistics & graphics

Price: $64.99

Average Rating: 4.6

Number of Lectures: 35

Number of Quizzes: 7

Number of Published Lectures: 35

Number of Published Quizzes: 7

Number of Curriculum Items: 42

Number of Published Curriculum Objects: 42

Original Price: ?799

Quality Status: approved

Status: Live

What You Will Learn

Primary goal is to provide users with an easy way to learn how to perform an analytics task in RStudio.

Includes many common tasks, including data management, descriptive summaries, inferential procedures, regression analysis, multivariate methods and graphics.

Includes some complex applications such as Simulation

Tried to provide a simple classroom type approach that is easy to understand for a new user, and supplied several solutions where deemed necessary.

Who Should Attend

If you intend to be a professional analyst, who use multiple statistical packages daily, this course is for you !

Useful for statisticians, epidemiologists, economists, engineers, physicians, sociologists and others engaged in data analysis.

This course intends to bolster the analytic abilities of a new user as well

Target Audiences

If you intend to be a professional analyst, who use multiple statistical packages daily, this course is for you !

Useful for statisticians, epidemiologists, economists, engineers, physicians, sociologists and others engaged in data analysis.

This course intends to bolster the analytic abilities of a new user as well

R is a general purpose statistical software package used in many fields of research. It is licensed for free – an open source software. It has large user and growing developer base.

I have developed this course for users of R. My primary goal is to provide an easy way to learn how to perform an analytic task in this system, without having to go through complex documentations. This course will give you a classroom like training experience and covers vast topics such as data management, descriptive summaries. inferential procedures, regression analysis, time series analysis, multivariate methods, simulation and graphics.

Therefore this course not only teaches you to clean and analyze data, it also gives you a pavement to develop colorful reports for the purpose of management communication.

I did not attempt to complicate things in as many ways as possible to keep the understanding sweet and simple. I have given a simple approach that is easy to understand for a new user, and have tried to provide several solutions where deemed possible.

I request you to watch the lectures at your own pace and practice the codes one by one with the given and new datasets. This will enhance your learning even more.

Course Curriculum

Chapter 1: Installation and Introduction to R Studio

Lecture 1: Course Introduction

Lecture 2: How to install R Studio ?

Lecture 3: Introduction To R Studio

Chapter 2: Inputs and Outputs

Lecture 1: Input Techniques

Lecture 2: Output Techniques

Chapter 3: Data Management

Lecture 1: Structure and Metadata

Lecture 2: Derived Variables and Data Manipulation

Lecture 3: Merging, Combining and Subsetting Datasets

Lecture 4: Date and Time Variables

Chapter 4: Statistical and Mathematical Functions

Lecture 1: Probability Distribution and Random Number Generation

Lecture 2: Mathematical Functions

Lecture 3: Matrix Operations

Chapter 5: Programming and Operating System (OS) Interface

Lecture 1: Programming and OS interface

Chapter 6: Common Statistical Procedures

Lecture 1: Summary Statistics

Lecture 2: Bivariate Statistics and Tests for Continuous Variables

Chapter 7: Linear Regression and ANOVA

Lecture 1: Regression Basics

Lecture 2: Linear Regression and ANOVA

Lecture 3: Residuals and Diagnostic Plots

Chapter 8: Regression Generalizations and Modeling

Lecture 1: Binary Logistic Regression – Basics

Lecture 2: Binary Logistic Regression – R Studio

Lecture 3: Poisson Regression – R Studio

Lecture 4: Factor Analysis Using R Studio

Lecture 5: Survival Analysis in R – Using Kaplan-Meier Plot

Lecture 6: Survival Analysis in R – Cox Hazard Regression

Chapter 9: Graphical Compendium

Lecture 1: Univariate Plots

Lecture 2: Bivariate Plots

Lecture 3: Some Special Purpose Plots – Maps and Interaction Plots

Lecture 4: Some Special Purpose Plots – Circular and Normal Q-Q Plots

Lecture 5: ROC Curve

Chapter 10: Graphical Options and Configurations

Lecture 1: Graphical Options in Detail

Lecture 2: Options and Paramters

Chapter 11: Time Series Analysis

Lecture 1: Time Series Analysis Using R – Part I

Lecture 2: Time Series Analysis Using R – Part II

Chapter 12: Simulation

Lecture 1: Simulation Part I – Categorical Data, tTest & Logistic Regression Simulation

Lecture 2: Simulation Part II – Monty Hall Simulation

Instructors

Soumyajit Halder
Operations Manager

Rating Distribution

1 stars: 1 votes

2 stars: 0 votes

3 stars: 0 votes

4 stars: 0 votes

5 stars: 15 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!