R Programming for Statistics and Data Science by Udemy: Fee, Duration, How to Apply

Quick Facts

particular	details
Medium of instructions English	Mode of learning Self study	Mode of Delivery Video and Text Based

Course overview

The R Programming for Statistics and Data Science online course is a 6.5 hours short program provided by Udemy. The course includes 39 articles and 36 downloadable resources. Candidates will get lifetime access to the course content. The course covers the fundamentals of R in programming.

The R Programming for Statistics and Data Science training teaches about loops, conditional statements and functions of R programming. The course teaches candidates to build functions in R and get the data in and out of R. The course includes basic tools used in data science with R, ggplot2 package, and grammar of graphics.

The R Programming for Statistics and Data Science syllabus covers the fundamentals of statistics, hypothesis testing in R. The course teaches to draw insight and visualize different types of data to make data-supported decisions. Candidates can access the course content on their television and smartphone.

Course and certificate fees

Fees information

₹ 4,099

R Programming for Statistics and Data Science Fee

Course	Fee in INR
R Programming for Statistics and Data Science	Rs. 4099

certificate availability

Yes

certificate providing authority

Udemy

Who it is for

Data scientist Computer programmer

Beginners to programming
Aspiring data scientists
Anyone who wants to learn to code.
Candidates who are interested in data analysis and statistics.

Eligibility criteria

Certification Qualifying Details

Applicants will receive the R Programming for Statistics and Data Science certification after successful completion of the course material and the curriculum.

What you will learn

Statistical skills Data science knowledge R programming

After completing the R Programming for Statistics and Data Science online training, learners will gain knowledge about the building blocks of R programming, matrices, data frames, data visualization, manipulating data, fundamentals of programming with R, vectors, and vector operations, etc.

The syllabus

Introduction

Ten Things you will Learn in this Course

Getting started

Intro
Downloading and Installing R & RStudio
Quick guide to the RStudio user interface
RStudio's GUI
Changing the appearance in RStudio
Installing packages in R and using the library

The building blocks of R

Creating an object in R
Exercise 1 Creating an object in R
Data types in R - Integers and doubles
Data types in R - Characters and Logicals
Objects and Data Types
Exercise 2 Data types in R
Coercion rules in R
Exercise 3 Coercion rules in R
Functions in R
Exercise 4 Using functions in R
Functions and arguments
Exercise 5 The arguments of a function
Building a function in R (basics)
Objects and Functions
Exercise 6 Building a function in R
Using the script vs. using the console

Vectors and vector operations

Intro
Introduction to vectors
Vector recycling
Exercise 7 Vector recycling
Naming a vector in R
Exercise 8 Vector attributes - names
Introduction to vectors
Getting help with R
Getting Help with R
Slicing and indexing a vector in R
Extracting elements from a vector
Exercise 9 Indexing and slicing a vector
Changing the dimensions of an object in R
Exercise 10 Vector attributes - dimensions

Matrices

Creating a matrix in R
Faster code: creating a matrix in a single line of code
Creating a matrix
Exercise 11 Creating a matrix in R
Do matrices recycle?
Indexing an element from a matrix
Slicing a matrix in R
Exercise 12 Indexing and slicing a matrix
Matrix arithmetic
Exercise 13 Matrix arithmetic
Matrix operations in R
Matrix operations
Exercise 14 Matrix operations
Categorical data
Creating a Factor in R
Factors in R
Exercise 15 Creating a factor in R
Lists in R
Exercise: Lists in R
Completed 33% of the course

Fundamentals of programming with R

Relational operators in R
Logical operators in R
Vectors and logicals operators
Relational and Logical operators in R
Exercise Logical operators
If, else, else if statements in R
Exercise If, else, else if statements in R
If, else, else if statements - Keep-In-Mind's
For loops in R
Exercise: For Loops in R
While loops in R
Exercise: While loops in R
Repeat loops in R
Loops in R
Building a function in R 2.0
Building a function in R 2.0 - Scoping
Exercise Scoping
Completed 50% of the course

Data frames

Intro
Creating a data frame in R
Exercise 16 Creating a data frame in R
The Tidyverse package
Data import in R
Importing a CSV in R
Data export in R
Exercise 17 Importing and exporting data in R
Creating data frames
Getting a sense of your data frame
Indexing and slicing a data frame in R
Data frame operations
Extending a data frame in R
Exercise 18 Data frame operations
Dealing with missing data in R

Manipulating data

Intro
Data transformation with R - the Dplyr package - Part I
Data transformation with R - the Dplyr package - Part II
Sampling data with the Dplyr package
Using the pipe operator in R
Manipulating data
Exercise 19 Data transformation with Dplyr
Tidying data in R - gather() and separate()
Tidying data in R - unite() and spread()
Tidying data
Exercise 20 Data tidying with Tidyr

Visualizing data

Intro
Intro to data visualization
Intro toggplot2
Variables: revisited
Building a histogram with ggplot2
Exercise 21 Building a histogram with ggplot2
Building a bar chart with ggplot2
Exercise 22 Building a bar chart with ggplot2
Building a box and whiskers plot with ggplot2
Exercise 23 Building a box plot with ggplot2
Building a scatterplot with ggplot2
Exercise 24 Building a scatterplot with ggplot2

Exploratory data analysis

Population vs. sample
Mean, Median, Mode
Preview
Skewness
Exercise 25 Determining Skewness
Variance, standard deviation, and coefficient of variability
Covariance and correlation
Exercise 26 Practical example with real estate data

Hypothesis Testing

Distributions
Standard Error and Confidence Intervals
Hypothesis testing
Type I and Type II errors
Test for the mean - population variance known
Exercise: Test for the mean - population variance known
The P-value
Test for the mean - Population variance unknown
Exercise: Test for the mean - population variance unknown
Comparing two means - Dependent samples
Exercise: Comparing two means - Dependent samples
Comparing two means - Independent samples

Linear Regression Analysis

The linear regression model
Correlation vs regression
Geometrical representation
First regression in R
How to interpret the regression table
Exercise: Doing a regression in R
Decomposition of variability: SST, SSR, SSE
R-squared
Completed 100% of the course

Admission details

To get admission to the R Programming for Statistics and Data Science certification training, follow the step by step procedure mentioned below:

Step 1: Navigate to the official Udemy course website by clicking the link below.

(https://www.udemy.com/course/r-programming-for-statistics-and-data-science/)

Step 2: To proceed, click the ‘Buy Now' option.

Step 3: Register by entering your personal information.

Step 4: Pay the online course fee amount and begin learning.

How it helps

Candidates pursuing R Programming for Statistics and Data Science online training will be benefited in the following ways:

Candidates will learn about the basics of programming in the R language.
Students will get a grasp of statistics along with core tools of R and data science.
A certificate of completion will be provided to the students after completing the training program.

Instructors

Mr Simona
Instructor
Freelancer

Other Bachelors

FAQs

What is the duration of the R Programming for Statistics and Data Science program?

The duration of the R Programming for Statistics and Data Science program is 6.5 hours.

Which provider offers the R Programming for Statistics and Data Science course?

Udemy offers the R Programming for Statistics and Data Science course.

The R Programming for Statistics and Data Science online training consists of how many articles?

The R Programming for Statistics and Data Science online training consists of 39 articles.

How many downloadable resources are available in the R Programming for Statistics and Data Science training?

There are 36 downloadable resources available in the R Programming for Statistics and Data Science training.

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R Programming for Statistics and Data Science

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