Learn R 2020 – Best R courses & Best R tutorials & Best R books

Best R Courses 2020

 

Best R Books 2020

Best R tutorials 2020

R Programming A-Z™: R For Data Science With Real Exercises!

Learn R programming by doing! There are a lot of R courses and conferences out there. However, R has a very steep learning curve and students are often overwhelmed. This course is different! This course is really step by step. In each new tutorial, we build on what has already been learned and take one more step forward. After each video, you learn a valuable new concept that you can apply immediately. And the best part is you learn through live examples.

This training is full of real-life analytical challenges that you will learn to solve. We will solve some of these problems together, others will make them homework exercises. In summary, this course has been designed for all skill levels and even if you have no programming or statistics experience, you will be successful in this course!

You will learn:

Learn to program in R at a good level
Learn how to use R Studio
Learn the basics of programming
Learn how to create vectors in R
Learn how to create variables
Learn about integer, doubles, logical, character, and more types in R
Learn how to create a while () loop and a for () loop in R
Learn how to create and use matrices in R
Learn the matrix () function, learn rbind () and cbind ()
Learn how to install packages in R
Learn how to customize R Studio to suit your preferences
Understand the law of large numbers
Understanding the normal distribution
Practice working with statistical data in R
Practice working with financial data in R
Practice working with sports data in R

This is the best R course in 2020.

Data Science and Machine Learning Bootcamp with R

Data Scientist has been ranked number one on Glassdoor and the average salary for a data scientist exceeds $ 120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world’s most interesting problems! This course is designed both for complete beginners with no programming experience or for seasoned developers who want to jump into data science!

This comprehensive course is comparable to other Data Science bootcamps which typically cost thousands of dollars, but now you can learn all of this information at a fraction of the cost! With over 100 HD video lectures and detailed code notebooks for each lecture, this is one of the most comprehensive data science and machine learning courses on Udemy!

We’ll teach you how to program with R, create amazing data visualizations, and use machine learning with R! Here are some of the topics we’ll learn:

Programming with R
Advanced R features
Using R data frames to solve complex tasks
Use R to manage Excel files
Web scraping with R
Connect R to SQL
Use ggplot2 for data visualizations
Use plot for interactive visualizations
Machine learning with R, including:
Linear regression
K Nearest neighbors
K means grouping
Decision trees
Random forests
Twitter data mining
Neural networks and deep learning
Support Vectore machines

You will learn

Program in R
Use R for data analysis
Create data visualizations
Use R to manage csv, excel, SQL or web scraping files
Use R to easily manipulate data
Use R for machine learning algorithms
Use R for data science

R Programming: Advanced Analytics In R For Data Science

Ready to take your R programming skills to the next level?

Do you really want to become proficient in data science and analytics with R?

This course is for you!

Professional R Video training, unique datasets designed with years of industry experience in mind, engaging exercises that are both fun and also give you a taste for REAL WORLD analysis .

In this course, you will learn:

How to prepare data for analysis in R
How to run the median imputation method in R
How to work with date-times in R
What are lists and how to use them
What is the Apply family of functions
How to use apply (), lapply () and sapply () instead of loops
How to nest your own functions in functions of type Apply
How to nest the apply (), lapply () and sapply () functions inside each other

You will learn:

Perform data preparation in R
Identify missing records in dataframes
Locate missing data in your dataframes
Apply the median imputation method to replace missing records
Apply the factual analysis method to replace missing records
Understand how to use the which () function
Know how to reset the dataframe index
Working with the gsub () and sub () functions to replace strings
Explain why NA is a third type of logical constant
Process date-times in R
Convert date-times to POSIXct time format
Create, use, add, modify, rename, access and subsets of lists in R
Understand when to use [] and when to use [[]] or the $ sign when working with lists
Create a time series chart in R
Understand how the Apply family of functions works
Recreate an apply statement with a for () loop
Use apply () when working with matrices
Use lapply () and sapply () when working with lists and vectors
Add your own functions in the apply statements
Nest functions apply (), lapply () and sapply () one inside the other
Use the which.max () and which.min () functions

R Programming for Statistics and Data Science 2020

R programming is a skill you need if you want to work as a data analyst or data scientist in the industry of your choice. And why not you? The data scientist is the highest ranked profession in the United States.

But to do this, you need the tools and skills to manage data. R is one of the best languages ​​to get you where you want to be. Combine that with some statistical know-how, and you’ll be on your way to the title of your dreams. This course brings all of this and more into one easy-to-use package, and it’s the perfect start to your journey.

R for Statistics and Data Science is the course that will take you from a complete beginner in programming with R to a professional who can perform data manipulation on demand. It gives you the skillset you need to approach a new data science project with confidence and be able to critically evaluate your work and that of others.

This course wastes no time and goes straight to hands-on coding in R. But don’t worry if you’ve never coded before, we’re starting with the light and teaching you all the basics as you go! We wanted this to be an equally satisfying experience for newbies and those of you who just want a refresher on R.

What makes this course different from other courses?

Well-paced learning.

Receive top-notch training with content we’ve designed – and rigorously edited – to deliver powerful and effective results.

Even though preferred learning paces differ from student to student, we believe that being challenged just the right amount underpins the learning that remains.

Introductory Guide to Statistics.

We will introduce you to descriptive statistics and the fundamentals of inferential statistics.

We will do it step by step, gradually developing your theoretical knowledge and practical skills.

You will be proficient in confidence intervals and hypothesis testing, as well as regression and cluster analysis.

The essentials of programming – based on R.

Put yourself in the shoes of a programmer, exceed the data scientist average and increase the productivity of your operations.

Data manipulation and analysis techniques in detail.

Learn how to work with vectors, matrices, data blocks, and lists.

Become a fan of the “ Tidyverse package ” – R’s most comprehensive collection of data manipulation tools – allowing you to index and subsets of data, as well as spread (), gather (), order (), subset (), filter (), arrange () and mute ().

Create meaningful visualizations and data graphics.

It is practice makes perfect.

Reinforce your learning through many practical exercises, carried out with love, for you, by us.

What about homework, projects and exercises?

There is a ton of homework that will challenge you in all kinds of ways. You will have the option to tackle projects on your own or access a video tutorial if you get stuck.

You will learn:

Learn the basics of R programming
Working with conditional statements, functions, and loops in R
Create your own functions in R
Get your data in and out of R
Discover the main tools of data science with R
Manipulate data with the Tidyverse package ecosystem
Systematically explore data in R
Graphics grammar and the ggplot2 package
Visualize data: plot different types of data and pull insights
Transforming data: best practices for knowing when and how
Index, slice, and subset data
Learn the basics of statistics and apply them in practice
Hypothesis test in R
Understand and perform regression analysis in R
Working with dummy variables
Learn how to make data-driven decisions!
Have fun taking apart Star Wars and Pokemon data, plus more serious data sets

R Programming for Simulation and Monte Carlo Methods

R programming for simulation and Monte Carlo methods focuses on using R software to program probabilistic simulations, often referred to as Monte Carlo simulations. Typical simplified “real world” examples include simulating the odds of a baseball player having a twenty-game season “streak” sequentially with “batting” or estimating the probable total number of taxis in a row. strange town when one observes a certain sequence of numbered taxis passing by a particular street corner over a period of 60 minutes. In addition to detailing half a dozen (sometimes fun) “ real world ” extended application examples, the course also explains in detail how to use existing R functions and how to write your own R functions, to perform simulated inference estimates, including probabilities. and confidence intervals, and other cases of stochastic simulation. Techniques for using R to generate different characteristics of various families of random variables are explained in detail. The course teaches the skills necessary to implement various approaches to simulate continuous and discrete random variable probability distribution functions, parameter estimation, Monte-Carlo integration and variance reduction techniques. The course partially uses the spuRs Comprehensive R Archive Network (CRAN) package to demonstrate how to structure and write programs to perform mathematical and probabilistic simulations using R statistical software.

You will learn

Use R software to program probabilistic simulations, often called Monte Carlo simulations.
Use R software to program math simulations and create new math simulation functions.
Use existing R functions and understand how to write their own R functions to perform simulated inference estimates, including probabilities and confidence intervals, and to model other stochastic simulation cases.
To be able to generate different families (and moments) of discrete and continuous random variables.
To be able to simulate parameter estimation, Monte-Carlo integration of continuous and discrete functions and variance reduction techniques

Best R books 2020

R for Data Science: Import, Tidy, Transform, Visualize, and Model Data

Sale
R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
  • O Reilly Media
  • Wickham, Hadley (Author)
  • English (Publication Language)
  • 520 Pages - 01/10/2017 (Publication Date) - O'Reilly Media (Publisher)
Turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you to learn data science like data scientists as quickly as possible in no time at all. Also, this is probably the best R programming language book in 2020.

R in Action: Data Analysis and Graphics with R

Sale
R in Action: Data Analysis and Graphics with R
  • Kabacoff, Dr. Rob (Author)
  • English (Publication Language)
  • 608 Pages - 06/06/2015 (Publication Date) - Manning Publications (Publisher)
presents both the R language and the examples that are so useful for good business developers. Focusing on practical solutions, the book offers a crash course to learn statistics and covers elegant methods for dealing with messy and incomplete data that are difficult to analyze using traditional methods. You’ll use extensive graphical capabilities for exploring and presenting data visually. And this expanded second edition includes new chapters on time series analysis, cluster analysis, and classification methodologies, including decision trees, random forests, and support vector machines.

R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics

Sale
R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics (O'reilly Cookbooks)
  • O'Reilly Media
  • Teetor, Paul (Author)
  • English (Publication Language)
  • 438 Pages - 03/29/2011 (Publication Date) - O'Reilly Media (Publisher)
perform data analysis with R quickly and efficiently. Learn with a collection of simple, concise, task-oriented recipes makes you productive with R immediately in the world. The solutions available choose from one basic task to input and output, general statistics, graphics, and linear regression. R Cookbook will help beginners learn to get started and make advanced developers learn better.

Advanced R

Advanced R (Chapman & Hall/CRC The R Series)
  • CRC Press
  • Wickham, Hadley (Author)
  • English (Publication Language)
  • 478 Pages - 10/28/2014 (Publication Date) - Routledge (Publisher)
Tools and techniques for attacking many types of R programming problems. Learn Advanced R programming to develop the many necessary skills to produce quality code that can be used in a great variety of circumstances. You will learn the fundamentals of R, including standard data types and functions. Intermediate R programmers can dive deeper into R and learn new strategies for solving problems. The best Advanced R book.

As an Amazon Associate I earn from qualifying purchases.