Machine Learning AZ™: HandsOn Python & R In Data Science
Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included.
Created by Kirill Eremenko  Data Scientist
Students: 786116, Price: $94.99
Students: 786116, Price: Paid
Interested in the field of Machine Learning? Then this course is for you!
This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms, and coding libraries in a simple way.
We will walk you stepbystep into the World of Machine Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative subfield of Data Science.
This course is fun and exciting, but at the same time, we dive deep into Machine Learning. It is structured the following way:

Part 1  Data Preprocessing

Part 2  Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression

Part 3  Classification: Logistic Regression, KNN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification

Part 4  Clustering: KMeans, Hierarchical Clustering

Part 5  Association Rule Learning: Apriori, Eclat

Part 6  Reinforcement Learning: Upper Confidence Bound, Thompson Sampling

Part 7  Natural Language Processing: Bagofwords model and algorithms for NLP

Part 8  Deep Learning: Artificial Neural Networks, Convolutional Neural Networks

Part 9  Dimensionality Reduction: PCA, LDA, Kernel PCA

Part 10  Model Selection & Boosting: kfold Cross Validation, Parameter Tuning, Grid Search, XGBoost
Moreover, the course is packed with practical exercises that are based on reallife examples. So not only will you learn the theory, but you will also get some handson practice building your own models.
And as a bonus, this course includes both Python and R code templates which you can download and use on your own projects.
Important updates (June 2020):

CODES ALL UP TO DATE

DEEP LEARNING CODED IN TENSORFLOW 2.0

TOP GRADIENT BOOSTING MODELS INCLUDING XGBOOST AND EVEN CATBOOST!
R Programming AZ™: R For Data Science With Real Exercises!
Learn Programming In R And R Studio. Data Analytics, Data Science, Statistical Analysis, Packages, Functions, GGPlot2
Created by Kirill Eremenko  Data Scientist
Students: 210717, Price: $94.99
Students: 210717, Price: Paid
Learn R Programming by doing!
There are lots of R courses and lectures out there. However, R has a very steep learning curve and students often get overwhelmed. This course is different!
This course is truly stepbystep. In every new tutorial we build on what had already learned and move one extra step forward.
After every video you learn a new valuable concept that you can apply right away. And the best part is that you learn through live examples.
This training is packed with reallife analytical challenges which you will learn to solve. Some of these we will solve together, some you will have as homework exercises.
In summary, this course has been designed for all skill levels and even if you have no programming or statistical background you will be successful in this course!
I can't wait to see you in class,
Sincerely,
Kirill Eremenko
Complete Machine Learning with R Studio – ML for 2021
Linear & Logistic Regression, Decision Trees, XGBoost, SVM & other ML models in R programming language  R studio
Created by StartTech Academy  3,000,000+ Enrollments  4+ Rated  160+ Countries
Students: 203884, Price: $29.99
Students: 203884, Price: Paid
You're looking for a complete Machine Learning course that can help you launch a flourishing career in the field of Data Science, Machine Learning, R and Predictive Modeling, right?
You've found the right Machine Learning course!
After completing this course, you will be able to:
· Confidently build predictive Machine Learning models using R to solve business problems and create business strategy
· Answer Machine Learning related interview questions
· Participate and perform in online Data Analytics competitions such as Kaggle competitions
Check out the table of contents below to see what all Machine Learning models you are going to learn.
How will this course help you?
A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course.
If you are a business manager or an executive, or a student who wants to learn and apply machine learning, R and predictive modelling in Real world problems of business, this course will give you a solid base for that by teaching you the most popular techniques of machine learning, R and predictive modelling.
Why should you choose this course?
This course covers all the steps that one should take while solving a business problem through linear regression. This course will give you an indepth understanding of machine learning and predictive modelling techniques using R.
Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some preprocessing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business.
What makes us qualified to teach you?
The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques using R, Python, and we have used our experience to include the practical aspects of data analysis in this course.
We are also the creators of some of the most popular online courses  with over 150,000 enrollments and thousands of 5star reviews like these ones:
This is very good, i love the fact the all explanation given can be understood by a layman  Joshua
Thank you Author for this wonderful course. You are the best and this course is worth any price.  Daisy
Our Promise
Teaching our students is our job and we are committed to it. If you have any questions about the course content, machine learning, R, predictive modelling, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.
Download Practice files, take Quizzes, and complete Assignments
With each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts of machine learning, R and predictive modelling. Each section contains a practice assignment for you to practically implement your learning on machine learning, R and predictive modelling.
Below is a list of popular FAQs of students who want to start their Machine learning journey
What is Machine Learning?
Machine Learning is a field of computer science which gives the computer the ability to learn without being explicitly programmed. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention.
What are the steps I should follow to be able to build a Machine Learning model?
You can divide your learning process into 3 parts:
Statistics and Probability  Implementing Machine learning techniques require basic knowledge of Statistics and probability concepts. Second section of the course covers this part.
Understanding of Machine learning  Fourth section helps you understand the terms and concepts associated with Machine learning and gives you the steps to be followed to build a machine learning model
Programming Experience  A significant part of machine learning is programming. Python and R clearly stand out to be the leaders in the recent days. Third section will help you set up the Python environment and teach you some basic operations. In later sections there is a video on how to implement each concept taught in theory lecture in Python
Understanding of models  Fifth and sixth section cover Classification models and with each theory lecture comes a corresponding practical lecture where we actually run each query with you.
Why use R for Machine Learning?
Understanding R is one of the valuable skills needed for a career in Machine Learning. Below are some reasons why you should learn Machine learning in R
1. It’s a popular language for Machine Learning at top tech firms. Almost all of them hire data scientists who use R. Facebook, for example, uses R to do behavioral analysis with user post data. Google uses R to assess ad effectiveness and make economic forecasts. And by the way, it’s not just tech firms: R is in use at analysis and consulting firms, banks and other financial institutions, academic institutions and research labs, and pretty much everywhere else data needs analyzing and visualizing.
2. Learning the data science basics is arguably easier in R than Python. R has a big advantage: it was designed specifically with data manipulation and analysis in mind.
3. Amazing packages that make your life easier. As compared to Python, R was designed with statistical analysis in mind, it has a fantastic ecosystem of packages and other resources that are great for data science.
4. Robust, growing community of data scientists and statisticians. As the field of data science has exploded, usage of R and Python has exploded with it, becoming one of the fastestgrowing languages in the world (as measured by StackOverflow). That means it’s easy to find answers to questions and community guidance as you work your way through projects in R.
5. Put another tool in your toolkit. No one language is going to be the right tool for every job. Like Python, adding R to your repertoire will make some projects easier – and of course, it’ll also make you a more flexible and marketable employee when you’re looking for jobs in data science.
What are the major advantages of using R over Python?

As compared to Python, R has a higher user base and the biggest number of statistical packages and libraries available. Although, Python has almost all features that analysts need, R triumphs over Python.

R is a functionbased language, whereas Python is objectoriented. If you are coming from a purely statistical background and are not looking to take over major software engineering tasks when productizing your models, R is an easier option, than Python.

R has more data analysis functionality builtin than Python, whereas Python relies on Packages

Python has main packages for data analysis tasks, R has a larger ecosystem of small packages

Graphics capabilities are generally considered better in R than in Python

R has more statistical support in general than Python
What is the difference between Data Mining, Machine Learning, and Deep Learning?
Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information to data, decisionmaking, and actions.
Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning.
R Programming For Absolute Beginners
Learn the basics of writing code in R  your first step to become a data scientist
Created by Bogdan Anastasiei  University Teacher and Consultant
Students: 140637, Price: $39.99
Students: 140637, Price: Paid
If you have decided to learn R as your data science programming language, you have made an excellent decision!
R is the most widely used tool for statistical programming. It is powerful, versatile and easy to use. It is the first choice for thousands of data analysts working in both companies and academia. This course will help you master the basics of R in a short time, as a first step to become a skilled R data scientist.
The course is meant for absolute beginners, so you don’t have to know anything about R before starting. (You don’t even have to have the R program on your computer; I will show you how to install it.) But after graduating this course you will have the most important R programming skills – and you will be able to further develop these skills, by practicing, starting from what you will have learned in the course.
This course contains about 100 video lectures in nine sections.
In the first section of this course you will get started with R: you will install the program (in case you didn’t do it already), you will familiarize with the working interface in R Studio and you will learn some basic technical stuff like installing and activating packages or setting the working directory. Moreover, you will learn how to perform simple operations in R and how to work with variables.
The next five sections will be dedicated to the five types of data structures in R: vectors, matrices, lists, factors and data frames. So you’ll learn how to manipulate data structures: how to index them, how to edit data, how to filter data according to various criteria, how to create and modify objects (or variables), how to apply functions to data and much more. These are very important topics, because R is a software for statistical computing and most of the R programming is about manipulating data. So before getting to more advanced statistical analyses in R you must know the basic techniques of data handling.
After finishing with the data structures we’ll get to the programming structures in R. In this section you’ll learn about loops, conditional statements and functions. You’ll learn how to combine loops and conditional statements to perform complex tasks, and how to create custom functions that you can save and reuse later. We will also study some practical examples of functions.
The next section is about working with strings. Here we will cover the most useful functions that allow us to manipulate strings. So you will learn how to format strings for printing, how to concatenate strings, how to extract substrings from a given string and especially how to create regular expressions that identify patterns in strings.
In the following section you’ll learn how to build charts in R. We are going to cover seven types of charts: dot chart (scatterplot), line chart, bar chart, pie chart, histogram, density line and boxplot. Moreover, you will learn how to plot a function of one variable and how to export the charts you create.
Every command and function is visually explained: you can see the output live. At the end of each section you will find a PDF file with practical exercises that allow you to apply and strengthen your knowledge.
So if you want to learn R from scratch, you need this course. Enroll right now and begin a fantastic R programming journey!
C Programming For Beginners – Master the C Language
C Programming will increase career options. Become a better dev in other languages by learning C. Pointers explained
Created by Tim Buchalka's Learn Programming Academy  Professional Programmers and Teachers  1.24M students
Students: 89308, Price: $129.99
Students: 89308, Price: Paid
Have you never programmed a computer before, and think or have been told that C is a good programming language to get started with. It is!
Maybe you have some experience with other programming languages, but want to learn C. It's a great language to add to your resume!
Or perhaps you are stuck in a low paying programming job, and want to move up to a better, more senior position. Learning C can help you!
The fact is, learning how to program in C is not only an excellent programming language to get started with, but it will also make you a better programming in other computer languages!
Why learn C ?
C is often considered to be the mother of all languages because so many other languages have been based on it.
Though C is simple it is one of the most powerful languages ever created. Considering it was created over 40 years ago, it is still used heavily and is usually in the top 5 or 10 most popular and most widely programming languages in the world.
Learning C can actually make you a better programming in other languages like C++, Java, or C# by equipping you with a mental model of what the computer is actually doing when you run your programs.
By learning how things really work "under the hood", and understand memory space, CPU architecture and so on, you can create more efficient programs, and obtain a huge advantage over other programmers in the process.
If you want to become a better developer, learning C is a great way to start!
Why enrolling in this course is the best decision you can make.
By the end of this course, you will understand the fundamentals of the C Programming Language, and make yourself more marketable for entry level programming positions.
You will understand variables and the different data types, be able to utilize functions and arrays, understand the concept of pointers, learn about control flow (decision statements and iteration).
You will be in a position to apply for realtime programming positions, and truly understand the core language that most modern languages are based on!
If you have previously used the C programming language, then this course will deepen your understanding of it. If you have never used it, no problem, you will see that it can help you become a more efficient C developer.
The course will be constantly refined in the future based on student feedback!
This course does not skip on the details. You will learn how to write high quality code and become an excellent problem solver. This course does not just present how to code in the C programming language, but, also includes all the details on "why" you are doing the things you are doing. At the end of this course, you will fully understand the concepts of the C Programming language.
Your instructor, Jason Fedin has been teaching students for over 12 years via online classes at over 10 different online Universities. He has created many different class curriculums, ranging from mobile programming to bash scripting to ObjectOriented Design and of course the C programming language.
Additionally, he has been developing software for over 16 years in the real world at various companies, specializing in ObjectOriented Development and Mobile Applications.
This means you are learning from someone who has all the professional training, skills, and experience you need to teach you how to become proficient in the C programming language.
If you are ready to get that first paid programming job, or to move up to a more senior programming position, then this course is for you!
Your new job or consulting opportunity awaits!
Why not get started today?
Click the Signup button to sign up for the course!
2021 Data Science & Machine Learning with R from AZ Course
Become a professional Data Scientist with R and learn Machine Learning, Data Analysis + Visualization, Web Apps + more!
Created by Juan E. Galvan  Digital Entrepreneur  Marketer  Visionary
Students: 75285, Price: $94.99
Students: 75285, Price: Paid
Welcome to the Learn Data Science and Machine Learning with R from AZ Course!
In this practical, handson course you’ll learn how to program in R and how to use R for effective data analysis, visualization and how to make use of that data in a practical manner. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a highlevel statistical language.
Our main objective is to give you the education not just to understand the ins and outs of the R programming language, but also to learn exactly how to become a professional Data Scientist with R and land your first job.
The course covers practical issues in statistical computing which include programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting on R code. Blending practical work with solid theoretical training, we take you from the basics of R Programming to mastery.
We understand that theory is important to build a solid foundation, we understand that theory alone isn’t going to get the job done so that’s why this course is packed with practical handson examples that you can follow step by step. Even if you already have some coding experience, or want to learn about the advanced features of the R programming language, this course is for you!
R coding experience is either required or recommended in job postings for data scientists, machine learning engineers, big data engineers, IT specialists, database developers and much more. Adding R coding language skills to your resume will help you in any one of these data specializations requiring mastery of statistical techniques.
Together we’re going to give you the foundational education that you need to know not just on how to write code in R, analyze and visualize data but also how to get paid for your newly developed programming skills.
The course covers 6 main areas:
1: DS + ML COURSE + R INTRO
This intro section gives you a full introduction to the R programming language, data science industry and marketplace, job opportunities and salaries, and the various data science job roles.

Intro to Data Science + Machine Learning

Data Science Industry and Marketplace

Data Science Job Opportunities

R Introduction

Getting Started with R
2: DATA TYPES/STRUCTURES IN R
This section gives you a full introduction to the data types and structures in R with handson step by step training.

Vectors

Matrices

Lists

Data Frames

Operators

Loops

Functions

Databases + more!
3: DATA MANIPULATION IN R
This section gives you a full introduction to the Data Manipulation in R with handson step by step training.

Tidy Data

Pipe Operator

dplyr verbs: Filter, Select, Mutate, Arrange + more!

String Manipulation

Web Scraping
4: DATA VISUALIZATION IN R
This section gives you a full introduction to the Data Visualization in R with handson step by step training.

Aesthetics Mappings

Single Variable Plots

TwoVariable Plots

Facets, Layering, and Coordinate System
5: MACHINE LEARNING
This section gives you a full introduction to Machine Learning with handson step by step training.

Intro to Machine Learning

Data Preprocessing

Linear Regression

Logistic Regression

Support Vector Machines

KMeans Clustering

Ensemble Learning

Natural Language Processing

Neural Nets
6: STARTING A DATA SCIENCE CAREER
This section gives you a full introduction to starting a career as a Data Scientist with handson step by step training.

Creating a Resume

Personal Branding

Freelancing + Freelance websites

Importance of Having a Website

Networking
By the end of the course you’ll be a professional Data Scientist with R and confidently apply for jobs and feel good knowing that you have the skills and knowledge to back it up.
Data Science and Machine Learning Bootcamp with R
Learn how to use the R programming language for data science and machine learning and data visualization!
Created by Jose Portilla  Head of Data Science, Pierian Data Inc.
Students: 71052, Price: $89.99
Students: 71052, Price: Paid
Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $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 for both complete beginners with no programming experience or experienced developers looking to make the jump to Data Science!
This comprehensive course is comparable to other Data Science bootcamps that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost! With over 100 HD video lectures and detailed code notebooks for every lecture this is one of the most comprehensive course for data science and machine learning on Udemy!
We'll teach you how to program with R, how to create amazing data visualizations, and how to use Machine Learning with R! Here a just a few of the topics we will be learning:
 Programming with R
 Advanced R Features
 Using R Data Frames to solve complex tasks
 Use R to handle Excel Files
 Web scraping with R
 Connect R to SQL
 Use ggplot2 for data visualizations
 Use plotly for interactive visualizations
 Machine Learning with R, including:
 Linear Regression
 K Nearest Neighbors
 K Means Clustering
 Decision Trees
 Random Forests
 Data Mining Twitter
 Neural Nets and Deep Learning
 Support Vectore Machines
 and much, much more!
Enroll in the course and become a data scientist today!
R Programming: Advanced Analytics In R For Data Science
Take Your R & R Studio Skills To The Next Level. Data Analytics, Data Science, Statistical Analysis in Business, GGPlot2
Created by Kirill Eremenko  Data Scientist
Students: 51154, Price: $89.99
Students: 51154, Price: Paid
Ready to take your R Programming skills to the next level?
Want to truly become proficient at 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 Analytics of the REAL WORLD.
In this course you will learn:
 How to prepare data for analysis in R
 How to perform the median imputation method in R
 How to work with datetimes in R
 What Lists are and how to use them
 What the Apply family of functions is
 How to use apply(), lapply() and sapply() instead of loops
 How to nest your own functions within applytype functions
 How to nest apply(), lapply() and sapply() functions within each other
 And much, much more!
The more you learn the better you will get. After every module you will already have a strong set of skills to take with you into your Data Science career.
R Programming:For Data Science With Real Exercises
Learn Programming In R And R Studio. Data Analytics, Data Science, Statistical Analysis, Packages, Functions, GGPlot2
Created by Zulqarnain Hayat  Enterprise Database Architect
Students: 49503, Price: $84.99
Students: 49503, Price: Paid
This course will introduces the R statistical processing language, including how to install R on your computer, read data from SPSS and spreadsheets, and use packages for advanced R functions. The course continues with examples on how to create charts and plots, check statistical assumptions and the reliability of your data, look for data outliers, and use other data analysis tools. Finally, learn how to get charts and tables out of R and share your results with presentations and web pages.
The following topics are include:
· What is R?
· Installing R and R studio (IDE)
· Creating bar character for categorical variables
· Building histograms
· Calculating frequencies and descriptive
· Computing new variables
· Creating scatter plots
· Comparing means
=================================QUICK DEMO==================================
R language basics commands
Reading ,Accessing and Summarizing Data in R
Quick Install R language on UBUNTU Linux
Python & R Programming
Learn the two most widely used programming languages with Data Science: Python and R
Created by Sai Acuity Institute of Learning Pvt Ltd Enabling Learning Through Insight!  Cybersecurity, Data Science & Human Capital Practitioners!
Students: 45823, Price: $29.99
Students: 45823, Price: Paid
Both Python and R are popular programming languages for Data Science. While R’s functionality is developed with statisticians in mind (think of R's strong data visualization capabilities!), Python is often praised for its easytounderstand syntax.
Ross Ihaka and Robert Gentleman created the opensource language R in 1995 as an implementation of the S programming language. The purpose was to develop a language that focused on delivering a better and more userfriendly way to do data analysis, statistics and graphical models.
Python was created by Guido Van Rossem in 1991 and emphasizes productivity and code readability. Programmers that want to delve into data analysis or apply statistical techniques are some of the main users of Python for statistical purposes.
As a data scientist it’s your job to pick the language that best fits the needs. Some questions that can help you:

What problems do you want to solve?

What are the net costs for learning a language?

What are the commonly used tools in your field?

What are the other available tools and how do these relate to the commonly used tools?
When and how to use R?
R is mainly used when the data analysis task requires standalone computing or analysis on individual servers. It’s great for exploratory work, and it's handy for almost any type of data analysis because of the huge number of packages and readily usable tests that often provide you with the necessary tools to get up and running quickly. R can even be part of a big data solution.
When getting started with R, a good first step is to install the amazing RStudio IDE. Once this is done, we recommend you to have a look at the following popular packages:

dplyr, plyr and data.table to easily manipulate packages,

stringr to manipulate strings,

zoo to work with regular and irregular time series,

ggvis, lattice, and ggplot2 to visualize data, and

caret for machine learning
When and how to use Python?
You can use Python when your data analysis tasks need to be integrated with web apps or if statistics code needs to be incorporated into a production database. Being a fully fledged programming language, it’s a great tool to implement algorithms for production use.
While the infancy of Python packages for data analysis was an issue in the past, this has improved significantly over the years. Make sure to install NumPy /SciPy (scientific computing) and pandas (data manipulation) to make Python usable for data analysis. Also have a look at matplotlib to make graphics, and scikitlearn for machine learning.
Unlike R, Python has no clear “winning” IDE. We recommend you to have a look at Spyder, IPython Notebook and Rodeo to see which one best fits your needs.
* We recommend all our students to learn both the programming languages and use them where appropriate since many Data Science teams today are bilingual, leveraging both R and Python in their work.
R Programming for Statistics and Data Science 2021
R Programming for Data Science & Data Analysis. Applying R for Statistics and Data Visualization with GGplot2 in R
Created by 365 Careers  Creating opportunities for Business & Finance students
Students: 19235, Price: $94.99
Students: 19235, Price: Paid
R Programming for Statistics and Data Science 2021
R Programming is a skill you need if you want to work as a data analyst or a data scientist in your industry of choice. And why wouldn't you? Data scientist is the hottest ranked profession in the US.
But to do that, you need the tools and the skill set to handle data. R is one of the top languages to get you where you want to be. Combine that with statistical knowhow, and you will be well on your way to your dream title.
This course is packing all of this, and more, in one easytohandle bundle, and it’s the perfect start to your journey.
So, welcome to R for Statistics and Data Science!
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 complete data manipulation on demand. It gives you the complete skill set to tackle a new data science project with confidence and be able to critically assess your work and others’.
Laying strong foundations
This course wastes no time and jumps right into handson coding in R. But don’t worry if you have never coded before, we start off light and teach you all the basics as we go along! We wanted this to be an equally satisfying experience for both complete beginners and those of you who would just like a refresher on R.
What makes this course different from other courses?

Wellpaced learning.
Receive top class training with content which we’ve built  and rigorously edited  to deliver powerful and efficient results.
Even though preferred learning paces differ from student to student, we believe that being challenged just the right amount underpins the learning that sticks.

Introductory guide to statistics.
We will take you through descriptive statistics and the fundamentals of inferential statistics.
We will do it in a stepbystep manner, incrementally building up your theoretical knowledge and practical skills.
You’ll master confidence intervals and hypothesis testing, as well as regression and cluster analysis.

The essentials of programming – Rbased.
Put yourself in the shoes of a programmer, rise above the average data scientist and boost the productivity of your operations.

Data manipulation and analysis techniques in detail.
Learn to work with vectors, matrices, data frames, and lists.
Become adept in ‘the Tidyverse package’  R’s most comprehensive collection of tools for data manipulation – enabling you to index and subset data, as well as spread(), gather(), order(), subset(), filter(), arrange(), and mutate() it.
Create meaningheavy data visualizations and plots.

Practice makes perfect.
Reinforce your learning through numerous practical exercises, made with love, for you, by us.
What about homework, projects, & exercises?
There is a ton of homework that will challenge you in all sorts of ways. You will have the chance to tackle the projects by yourself or reach out to a video tutorial if you get stuck.
You: Is there something to show for the skills I will acquire?
Us: Indeed, there is – a verifiable certificate.
You will receive a verifiable certificate of completion with your name on it. You can download the certificate and attach it to your CV and even post it on your LinkedIn profile to show potential employers you have experience in carrying out data manipulations & analysis in R.
If that sounds good to you, then welcome to the classroom :)
Full Stack Data Science with Python, Numpy and R Programming
Learn data science with R programming and Python. Use NumPy, Pandas to manipulate the data and produce outcomes
Created by Oak Academy  Web & Mobile Development, IOS, Android, Ethical Hacking, IT
Students: 15169, Price: $94.99
Students: 15169, Price: Paid
Welcome to Full Stack Data Science with Python, Numpy, and R Programming course.

Do you want to learn Python from scratch?

Do you think the transition from other popular programming languages like Java or C++ to Python for data science?

Do you want to be able to make data analysis without any programming or data science experience?
Why not see for yourself what you prefer?
It may be hard to know whether to use Python or R for data analysis, both are great options. One language isn’t better than the other—it all depends on your use case and the questions you’re trying to answer.In this course, we offer R Programming, Python, and Numpy! So you will decide which one you will learn.
Throughout the course's first part, you will learn the most important tools in R that will allow you to do data science. By using the tools, you will be easily handling big data, manipulate it, and produce meaningful outcomes.
In the second part, we will teach you how to use the Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms and we will also do a variety of exercises to reinforce what we have learned in this course.
In this course, you will also learn Numpy which is one of the most useful scientific libraries in Python programming.
Throughout the course, we will teach you how to use the Python in Linear Algebra, and Neural Network concept, and use powerful machine learning algorithms and we will also do a variety of exercises to reinforce what we have learned in this Full Stack Data Science with Python, Numpy and R Programming course.
At the end of the course, you will be able to select columns, filter rows, arrange the order, create new variables, group by and summarize your data simultaneously.
In this course you will learn;

How to use Anaconda and Jupyter notebook,

Fundamentals of Python such as

Datatypes in Python,

Lots of datatype operators, methods and how to use them,

Conditional concept, if statements

The logic of Loops and control statements

Functions and how to use them

How to use modules and create your own modules

Data science and Data literacy concepts

Fundamentals of Numpy for Data manipulation such as

Numpy arrays and their features

Numpy functions

Numexpr module

How to do indexing and slicing on Arrays

Linear Algebra

Using NumPy in Neural Network

How to do indexing and slicing on Arrays

Lots of stuff about Pandas for data manipulation such as

Pandas series and their features

Dataframes and their features

Hierarchical indexing concept and theory

Groupby operations

The logic of Data Munging

How to deal effectively with missing data effectively

Combining the Data Frames

How to work with Dataset files

And also you will learn fundamentals thing about Matplotlib library such as

Pyplot, Pylab and Matplotlb concepts

What Figure, Subplot and Axes are

How to do figure and plot customization

Examining and Managing Data Structures in R

Atomic vectors

Lists

Arrays

Matrices

Data frames

Tibbles

Factors

Data Transformation in R

Transform and manipulate a deal data

Tidyverse and more
And we will do many exercises. Finally, we will also have handson projects covering all of the Python subjects.
Why would you want to take this course?
Our answer is simple: The quality of teaching.
When you enroll, you will feel the OAK Academy's seasoned instructors' expertise.
Fresh Content
It’s no secret how technology is advancing at a rapid rate and it’s crucial to stay on top of the latest knowledge. With this course, you will always have a chance to follow the latest trends.
Video and Audio Production Quality
All our content are created/produced as highquality video/audio to provide you the best learning experience.
You will be,

Seeing clearly

Hearing clearly

Moving through the course without distractions
You'll also get:

Lifetime Access to The Course

Fast & Friendly Support in the Q&A section

Udemy Certificate of Completion Ready for Download
Dive in now!
We offer full support, answering any questions.
See you in the course!
The Rust Programming Language
Learn a modern, powerful yet safe systems programming language!
Created by Dmitri Nesteruk  Software/Hardware Engineering • Quant Finance • Algotrading
Students: 14439, Price: $99.99
Students: 14439, Price: Paid
This course will teach you the fundamentals of Rust, a modern programming language that has the both the power of native code as well as the safety of some managed languages. In this course you will learn the following:
 How to download and install Rust; how to compile programs and (optionally) work with an IDE.
 Learn about fundamental data types and how to use them to declare variables.
 Undersand arrays, vectors and strings, the concept of slices.
 Learn to create functions, methods, closures, higherorder functions.
 Understand how to create various data structures such as structs and enums; also traits.
 Master Rust's explicit take on the concept of lifetime with ownership, borrowing, lifetime specifiers, lifetime elision.
 Learn how to safely share data around your (possibly multithreaded) application with Rc, Arc and Mutex.
 Use Rust's package managent using Cargo.
 Learn about other useful topics: documentation, conditional compilation, testing.
This course, like all my other courses, will be supplemented with additional lectures based on participants' requests.
R Level 1 – Data Analytics with R
Use R for Data Analytics and Data Mining
Created by RTutorials Training  Data Science Education
Students: 12944, Price: $109.99
Students: 12944, Price: Paid
Are you new to R?
Do you want to learn more about statistical programming?
Are you in a quantitative field?
You just started learning R but you struggle with all the free but unorganized material available elsewhere?
Do you want to hack the learning curve and stay ahead of your competition?
If your answer is YES to some of those points  read on!
This Tutorial is the first step  your Level 1  to R mastery.
All the important aspects of statistical programming ranging from handling different data types to loops and functions, even graphs are covered.
While planing this course I used the Pareto 80/20 principle. I filtered for the most useful items in the R language which will give you a quick and efficient learning experience.
Learning R will help you conduct your projects. On the long run it is an invaluable skill which will enhance your career.
Your journey will start with the theoretical background of object and data types. You will then learn how to handle the most common types of objects in R. Much emphasis is put on loops in R since this is a crucial part of statistical programming. It is also shown how the apply family of functions can be used for looping.
In the graphics section you will learn how to create and tailor your graphs. As an example we will create boxplots, histograms and piecharts. Since the graphs interface is quite the same for all types of graphs, this will give you a solid foundation.
With the R Commander you will also learn about an alternative to RStudio. Especially for classic hypthesis tests the R Coomander GUI can save you some time.
According to the teaching principles of R Tutorials every section is enforced with exercises for a better learning experience. Furthermore you can also check out the rtutorials R exercise database over at our webpage. In the database you will find more exercises on the topics of this course.
You can download the code pdf of every section to try the presented code on your own.
This tutorial is your first step to benefit from this open source software.
What R you waiting for?
Martin
Applied Statistical Modeling for Data Analysis in R
Your Complete Guide to Statistical Data Analysis and Visualization For Practical Applications in R
Created by Minerva Singh  Bestselling Instructor & Data Scientist(Cambridge Uni)
Students: 8419, Price: $124.99
Students: 8419, Price: Paid
APPLIED STATISTICAL MODELING FOR DATA ANALYSIS IN R
COMPLETE GUIDE TO STATISTICAL DATA ANALYSIS & VISUALIZATION FOR PRACTICAL APPLICATIONS IN R
Confounded by Confidence Intervals? Pondering Over pvalues? Hankering Over Hypothesis Testing?
Hello, My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation).
I have several years of experience in analyzing real life data from different sources using statistical modeling and producing publications for international peer reviewed journals. If you find statistics books & manuals too vague, expensive & not practical, then you’re going to love this course!
I created this course to take you by hand and teach you all the concepts, and take your statistical modeling from basic to an advanced level for practical data analysis.
With this course, I want to help you save time and learn what the arcane statistical concepts have to do with the actual analysis of data and the interpretation of the bespoke results. Frankly, this is the only one course you need to complete in order to get a head start in practical statistical modeling for data analysis using R.
My course has 9.5 hours of lectures and provides a robust foundation to carry out PRACTICAL, reallife statistical data analysis tasks in R, one of the most popular and FREE data analysis frameworks.
GET ACCESS TO A COURSE THAT IS JAM PACKED WITH TONS OF APPLICABLE INFORMATION! AND GET A FREE VIDEO COURSE IN MACHINE LEARNING AS WELL!
This course is your surefire way of acquiring the knowledge and statistical data analysis skills that I acquired from the rigorous training I received at 2 of the best universities in the world, perusal of numerous books and publishing statistically rich papers in renowned international journal like PLOS One.
To be more specific, here’s what the course will do for you:
(a) It will take you (even if you have no prior statistical modelling/analysis background) from a basic level to performing some of the most common advanced statistical data analysis tasks in R.
(b) It will equip you to use R for performing the different statistical data analysis and visualization tasks for data modelling.
(c) It will Introduce some of the most important statistical concepts to you in a practical manner such that you can apply these concepts for practical data analysis and interpretation.
(d) You will learn some of the most important statistical modelling concepts from probability distributions to hypothesis testing to regression modelling and multivariate analysis.
(e) You will also be able to decide which statistical modelling techniques are best suited to answer your research questions and applicable to your data and interpret the results.
The course will mostly focus on helping you implement different statistical analysis techniques on your data and interpret the results.
After each video you will learn a new concept or technique which you may apply to your own projects immediately!
TAKE ACTION NOW :) You’ll also have my continuous support when you take this course just to make sure you’re successful with it. If my GUARANTEE is not enough for you, you can ask for a refund within 30 days of your purchase in case you’re not completely satisfied with the course.
TAKE ACTION TODAY! I will personally support you and ensure your experience with this course is a success.
Statistics for Data Analysis Using R
Learn Programming in R & R Studio • Descriptive, Inferential Statistics • Plots for Data Visualization • Data Science
Created by Sandeep Kumar  Experienced Quality Manager • Six Sigma Coach • Consultant
Students: 7751, Price: $99.99
Students: 7751, Price: Paid
Perform simple or complex statistical calculations using R Programming!  You don't need to be a programmer for this :)
Learn statistics, and apply these concepts in your workplace using R.
The course will teach you the basic concepts related to Statistics and Data Analysis, and help you in applying these concepts. Various examples and datasets are used to explain the application.
I will explain the basic theory first, and then I will show you how to use R to perform these calculations.
Following areas of statistics are covered:
Descriptive Statistics  Mean, Mode, Median, Quartile, Range, Inter Quartile Range, Standard Deviation. (Using base R function and the psych package)
Data Visualization  3 commonly used charts: Histogram, Box and Whisker Plot and Scatter Plot (using base R commands)
Probability  Basic Concepts, Permutations, Combinations (Basic theory only)
Population and Sampling  Basic concepts (theory only)
Probability Distributions  Normal, Binomial and Poisson Distributions (Base R functions and the visualize package)
Hypothesis Testing  One Sample and Two Samples  z Test, tTest, F Test, ChiSquare Test
ANOVA  Perform Analysis of Variance (ANOVA) step by step doing the manual calculation and by using R.
R Programming from Scratch for Data Science – Step by step
Learn R Language (Data Science) for beginners : Become Data Scientist
Created by Happy Learning  Architect
Students: 7329, Price: $19.99
Students: 7329, Price: Paid
Welcome to the course, R Programming from Scratch
R is a powerful and widely used open source software and programming environment for data analysis. Companies across the globe use R as an essential tool for various types of analysis to get key insights from data and to make key decisions. This course will provide everything you need to know to get started with the R framework, and contains a number of demos to provide handson practice in order to become an efficient and productive R programmer. By the end of this course, you will also learn to play with data and to extract key information using various R functions and constructs.
You will learn below:
1. Introduction
2. Installation of R Language
3. Installation of R Studio on Windows and Linux
4. Variable and Operators
R for Beginners
Introduction to Programming Language R. Tools for Data Science, Data Analysis and Statistical Analysis. RStudio.
Created by Marko Intihar  Data Scientist, Researcher and Teacher
Students: 6081, Price: $94.99
Students: 6081, Price: Paid
Are you one of the people that would like to start a data science career or are you just fond of using data for data analysis in your spare time or for your job? Do you use spreadsheets for data cleaning, wrangling, visualization, and data analysis? I think it is time to enhance your hobby or your career path with learning adequate skills such as R.
R is s a programming language that enables all essential steps when you are dealing with data like:

importing,

exporting,

cleaning,

merging,

transforming,

analyzing,

visualizing,

and extracting insights from the data.
Originally R began as a free software environment for statistical computing with graphics supported. Over the years with the rapid development of computing power and the need for tools used for mining and analyzing tons of data that are being generated on every step of our lives, R has emerged into something much greater than its original laid path. Nowadays the R community is vast, every day thousands of people start learning R, and every day new R's libraries are being made and released to the world. These libraries solve different users' needs because they provide different functions for dealing with all kinds of data.
If you are still not convinced to join me on a journey where foundations for your R skills will be laid, please bear with me a bit more. In this R for Beginners course, you will dive into essential aspects of the language that will help you escalate your learning curve. Course first gently touches the basics like:

how to install R and how to install R's Integrated Development Environment (IDE) RStudio,

then you will learn how to create your first R script and R project folder,

R project folder will be your baseline folder where all your scripts and assignments will be saved,

you will learn how to install different R packages and how to use functions provided with each package.
After these first steps, you will dive into sections where all major R data structures are presented. You will be able to:

differentiate among each data structure,

use builtin functions to manipulate data structures,

reshape, access elements, and convert R objects,

import data from many different sources into R's workspace and

export R objects to different data sources.
When you will have a grasp of what R is capable of, a section devoted to programming elements will guide you through essential steps for writing a programming code that can execute repetitive tasks. Here you will master:

your first loops,

conditional statements,

your custom made functions,

and you will be able to optimize your code using vectorization.
It is said that a picture can tell an observer a powerful story and holds a stronger message than a thousand words combined. In the final section of this course, the greatest R's power is revealed, the power to tell the story by using data visualization. Here you will master how to build:

scatterplots,

line charts,

histograms,

box plots,

bar charts,

mosaic plots,

how to alter R's default graphical parameters to make beautiful figures,

and how to export a figure from R to a proper format for further sharing with your colleagues.
If you are still not convinced to start learning R, I will share with you how the course is structured:

Each section holds separate exercises covering learning material that is related to the section's topic.

Normally each exercise begins with a short intro that provides a basic understanding of the topic, then a coding exercise is presented.

During coding exercise, you will write the R code for executing given tasks.

At the end of each section, an assignment is presented.

Each assignment tests the skills you have learned during a given section.

In the last two assignments, you will write a code to build a simulation environment where you will execute the simulation and present the results with proper visualization techniques.
Do not lose more time and please enroll in the course today. I guarantee you will learn a lot and you will enjoy the learning process.
Statistics in R – The R Language for Statistical Analysis
Statistics made easy with the open source R language. Learn about Regression, Hypothesis tests, R Commander ...
Created by RTutorials Training  Data Science Education
Students: 3077, Price: $54.99
Students: 3077, Price: Paid
Do you want to learn more about statistical programming?
Are you in a quantitative field?
You want to know how to perform statistical tests and regressions?
Do you want to hack the learning curve and stay ahead of your competition?
If YES came to your mind to some of those points  read on!
This tutorial will teach you anything you need to know about descriptive and inferential statistics as well as regression modeling in R.
While planing this course we were focusing on the most important inferential tests that cover the most common statistical questions.
After finishing this course you will understand when to use which specific test and you will also be able to perform these tests in R.
Furthermore you will also get a very good understanding of regression modeling in R. You will learn about multiple linear regressions as well as logistic regressions.
According to the teaching principles of R Tutorials every section is enforced with exercises for a better learning experience. You can download the code pdf of every section to try the presented code on your own.
Should you need a more basic course on R programming we would highly recommend our R Level 1 course. The Level 1 course covers all the basic coding strategies that are essential for your day to day programming.
What R you waiting for?
Martin
Introduction to R Programming
Basics of R Software
Created by R MADANA MOHANA  Associate Professor, Computer Science and Engineering
Students: 2404, Price: $19.99
Students: 2404, Price: Paid
Any scientific task without the knowledge of software is difficult to imagine and complete in the current scenario. R is a free software that is capable of handling mathematical and statistical manipulations. It has its own programming language as well as built in functions to perform any specialized task. We intend to learn the basics of R software in this course.
All industries involved in mathematical and statistical computations, programming and simulations and having R & D setup will use this R Programming course.
Build Spring Boot Apps with the Kotlin Programming Language
Build fully functional, robust and efficient applications with Spring Boot and the Kotlin programming language
Created by Matt Greencroft  Course tutor at Virtual Pair Programmers
Students: 2269, Price: $49.99
Students: 2269, Price: Paid
Kotlin is a programming language for the JVM (and also for Javascript and native code too) which was created by JetBrains  the company behind the IntelliJ IDE. It offers a number of enhancements over Java, including that it's less verbose, has immutable variables, and almost always gets rid of the NullPointerException. Compared to other JVM languages, such as Scala, Kotlin is an easy transition for Java developers, and the Kotlin team hope that it will eventually replace Java alltogether!
In this course we learn how to code in Kotlin, with a particular focus on how to build full stack Spring Boot applications with Kotlin.
This course is aimed at existing Spring Framework Java developers who want to upgrade their skills to use Kotlin instead of (or as well as) Java.
2021 R 4.0 Programming for Data Science  Beginners to Pro
Learn Latest R 4 with RStudio & Jupyter. DataFrame, Vectors, Matrix, DateTime, GGplot2, Tidyverse, Plotly, etc.
Created by Laxmi Kant  Principal Data Scientist at mBreath and KGPTalkie
Students: 2019, Price: $19.99
Students: 2019, Price: Paid
Are you ready to accept the R Programming Challenge?
Want to analyze and get insights from your datasets?
This Course is for You!!!
You will learn R programming in a very interactive way. I will be explaining to you each line of code. You do not need any prior experience in coding. Anyone can start learning. We will start with R Programming and RStudio set up on the computer thereafter I will be teaching you fundamentals of R Programming.
In this course, you learn:

How to install RPackages

How to work with Rdata types

What is R DataFrame, Matrices, Vectors etc.

How to work with DataFrames

How to perform join and merge operations on DataFrames

How to plot data using ggplot2 in R 4.0

Analysis of reallife dataset Covid19
This course is in development. 20+ hours of lectures will be added to the course. Kindly, keep checking regularly.

THIS COURSE IS NOT COMPLETE YET. MACHINE LEARNING LECTURES WILL BE UPLOADED IN COUPLE OF WEEKS.

The Complete R Programming for Data Science – 7 courses in 1
Beginner to Pro: Learn R programming language, R studio, ggplot2, dplyr, statistics, caret, machine learning, projects
Created by Numyard Data Science Team  Data Science educational team
Students: 1998, Price: $89.99
Students: 1998, Price: Paid
In The Complete RProgramming for Data Science & Statistics program, we have carefully designed 7 FullFledged courses into 1 Master Course of 200+ videos, 50+ RPackages, Core Machine Learning and statistics concepts, 75+ practice problems and 2 Industrial projects
By end of this course, you will be able to solve Industry Data Science project in R starting including model building, model diagnostics and presenting actionable business insights
Here's how you will progress across the 7 courses in the Master Course:

Getting started with Rprogramming: First, you will learn to write your own R code and perform basic programming tasks. You will begin with the base R programming course, where you will master the fundamental data structures such as vectors, lists, dataframes , understand the core programming constructs and get enough coding practice. You will also create full featured plots for data analysis using base graphics.

Advanced coding with Tidyverse: Then you will move to advanced coding in R based on the tidyverse using the dplyr package. You will start using the elegant pipe syntax provided by the magrittr package and the data manipulation verbs.

Data.table for data wrangling in R: Then You will move on to master the data.table package which has advanced capabilities for fast data manipulation. Data Scientists love this package for its incredible speed gains. Here, you will do fast data imports, create pivot tables and get comfortable with wrangling data. You will learn techniques to make your R code run super fast.

Ggplot2 Graphics in R: Once you gather the core R programming skills, you start creating professional looking plots using the famous ggplot2 package. You will be able to create any data analysis plot. Be it box plots, scatterplots, dual axis time series plots, because you will not just learn the syntax, but also learn the underlying structure behind it.

Statistical Foundations for Machine Learning: You will gain mastery over the ‘statistical foundations for machine learning’, which by itself is a full fledged statistics course. You will understand the core statistics concepts such as the law of large numbers, central limit theorem, normal distribution, how statistical significance tests such as the tTest and ANOVA work and more, by solving multiple use cases of when and how to use them. You will know exactly how they work by following stepbystep hand computations and then implement in R to match the results. All the concepts are completely explained and demonstrated.

Statistical Modeling with Linear Regression and Case Study: After mastering statistics, you will achieve professionallevel R skills with linear regression. You will understand:

What sort of industrial problems you can apply them on

Understand the math behind it

You will build the algorithm itself from scratch

Learn how to interpret the results

Perform post model building diagnostics

Learn how to present the model results in a way that is valuable to the business and project stakeholders

7. Logistics Regression for Business and Case Problem: You will understand: Then you learn the logistics regression with the same methodology of application, mathematics, building algorithm, interpreting results, diagnosing models and presenting insights
Professional Level Industry Projects: Finally, to gain and endtoend professional Data Science project skills, you will solve two Industry projects 
> Predict Customer Purchase Propensity (Banking Domain)
> Predict U.S. Institute performance (Education Sector)
Throughout the program you will get interesting challenges, forum support for your queries and RDataScience certification for your CV.
R Programming – R Language for Absolute Beginners
R Programming course suitable for everyone, no coding experience or a statistics background needed
Created by Ivo Bernardo  Partner and Senior Data Scientist @ Daredata Engineering
Students: 1309, Price: $84.99
Students: 1309, Price: Paid
Are you a business analyst interested in expanding your data analyis toolkit? Or are you an entry level datascientist that just wants to understand how R coding works?
A lot of data analysis professionals are able to become more productive by being able to code  with the explosion of data and the continuous learning that our modern world demands, coding will be even more important in the future to stand out and keep the pace of organizations' need for data analytics.
This course was designed to be your first step into the R programming world! We will delve deeper into the concepts of R objects, understand the R user interface and play around with several datasets. This course contains lectures around the following groups:

Introductory slides lectures with the most wellknown commands for each type of R object.

Code along lectures where you will see how we can implement the stuff we will learn!

Test your knowledge with questions and practical exercises with different levels of difficulty!

Analyze real datasets and understand the thought process from question to R code solution!
This course was designed to be focused on the practical side of coding in R  instead of teaching you every function and method out there, I'll show you how you can read questions and examples and get to the answer by yourself, compounding your knowledge on the different R objects.
At the end of the course you should be able to use R to analyze your own datasets. Along the way you will also learn what R vectors, arrays, matrixes and lists are and how you can combine the knowledge of those objects to power up your analysis.
Here are some examples of things you will be able to do after finishing the course:

Load CSV and Excel files into R;

Do interesting line plots that enable you to draw conclusions from data.

Plot histograms of numerical data.

Create your own functions that will enable you to reutilize code.

Slice and dice Data Frames, subsetting data for specific domains.
Join thousands of professionals and students in this R journey and discover the amazing power of this statistical opensource language.
This course will be constantly updated based on students feedback.
R for Data Science: R Programming Bootcamp
Learn R Programming Fundamentals, Data Wrangling, Data Visualization for Data Science
Created by Syed Mohiuddin  Professional Educator
Students: 1221, Price: $94.99
Students: 1221, Price: Paid
Welcome to this course of R Programming for Beginners with the handson tutorial, and become an R Professional which is one of the most favoured skills, that employer's need.
Whether you are new to programming or have never programmed before in R Language, this course is for you! This course covers the R Programming from scratch. This course is selfpaced. There is no need to rush  you learn on your own schedule.
R programming language iѕ one of the best opensource programming language and more powerful than other programming languages. It iѕ well documented and has a clean syntax and quite еаѕу tо lеаrn.
This course will help anyone who wants to start a саrееr in Data Science and Machine Lеаrning. You need to have basic undеrѕtаnding оf R Programming to become a Data Scientist or Data Analyst.
This course begins with the introduction to R course that will help you write R code in no time. Then we help you with the installation of R and RStudio on your computer and setting up the programming environment. This course will provide you with everything you need to know about the basics of R Programming.
In this course we will cover the following topics:

Basics of R Programming including Operators

Fundamentals of R Programming

Vectors, Matrices, Lists

Data Frames

Importing Data in Data Frames using Text and CSV files

Data Wrangling using dplyr package

Data Visualization
This course teaches R Programming in a practical manner with handson experience with coding screencast.
Once you complete this course, you will be able to create or develop R Programs to solve any complex problems with ease.
Data Visualization with R and ggplot2
R Programming Language for Data Visualization. GGplot2, Data Analysis, Data Preparation, Data Sciene Tools, RStudio
Created by Marko Intihar  Data Scientist, Researcher and Teacher
Students: 765, Price: $124.99
Students: 765, Price: Paid
Today we live in a world where tons of data is generated every second. We need to analyze data to get some useful insight. One of the strongest weapons for data insight is data visualization. Probably you have heard this one before: "A picture tells more than a thousand words combined ". Therefore to tell stories from the data we need tools for producing adequate and amazing graphics. Here R as one of the most rapidly growing tools in the fields of data science and statistics provides needed assistance. If you combine R with its library ggplot2 you get one of the deadliest tools for data visualization, which grows every day and is freely accessible to anyone.
This course is designed to first give you quick and proper theoretical foundations for creating statistical plots. Then you dive into the world of exploratory data analysis where you are confronted with different datasets and creating a wide variety of statistical plots.
If you take this course, you will learn a ton of new things. Here are just a few topics you will be engaged with:

The grammar of graphics (the idea behind statistical plots, the foundation of ggplot2)

Data transformation with dplyr and tidyr (crash course included)

Exploratory data analysis (EDA) (statistical plots for exploring one continuous or one discrete variable)

EDA for exploring two or more variables (different statistical plots)

Combine ggplot2 with RMarkdown to wrap up your analysis and produce HTML reports

Create some additional types of plots by combining ggplot2 and supplementary libraries (word cloud, parallel coordinates plot, heat map, radar plot, ...)

Draw maps to show the spread of coronavirus disease

Customize the plot's theme

Create subplots using cowplot library

Highlight data on your plot with gghighlight library

and much more...
Course includes:

over 20 hours of lecture videos,

R scripts and additional data (provided in the course material),

engagement with assignments, where you have to test your skills,

assignments walkthrough videos (where you can check your results).
All being said this makes one of Udemy's most comprehensive courses for data visualization using R and ggplot2.
Enroll today and become the master of data visualization!!!
R For Beginners: Learn R Programming from Scratch
Learn R Programming in R and R Studio, analyse data with R programming course and become professional at data mining
Created by Oak Academy  Web & Mobile Development, IOS, Android, Ethical Hacking, IT
Students: 650, Price: $94.99
Students: 650, Price: Paid
Hi there,
Welcome to my “R For Beginners: Learn R Programming from Scratch” course.
In this course, you will learn how to code with R Programming Language, manage and analyze data with R programming and report your findings.
R programming language is a leading data mining technology. To learn data science, if you don’t know which high return programming language to start with. The answer is R programming.
This R programming course is for:

Students in statistical courses,

Analysts who produce statistical reports,

Professional programmers on other languages,

Academic researchers developing the statistical methodology,

Specialists in the various area who need to develop sophisticated graphical presentations of data,

and anyone who is particularly interested in big data, machine learning and data intelligence.
No Previous Knowledge is needed!
This course will take you from a beginner to a more advanced level.
If you are new to data science, no problem, you will learn anything you need to start with R.
If you are already used to R and you just need a refresher, you are also in the right place.
Here is the list of what you’ll learn by the end of course,
· Installation
· R Console Versus R Studio
· R and R Studio Installation
· Basic Syntax
· Data Types in R
· Operators and Functions in R
· R Packages
· Managing R Packages
· Data Management in R
· Getting Data into R
· Computation and Statistics
· Hands on Projects Experimental Learning
After every session you will have a strong set of skills to take with you into your Data Science career.
So, This is the right course for anyone who wants to learn R from scratch or for anyone who needs a refresher.
Fresh Content
It’s no secret how technology is advancing at a rapid rate. New tools are released every day, and it’s crucial to stay on top of the latest knowledge. You will always have uptodate content to this course at no extra charge.
Video and Audio Production Quality
All our contents are created/produced as high quality video/audio to provide you the best learning experience.
You will be,
· Seeing clearly
· Hearing clearly
· Moving through the course without distractions
You'll also get:
✔ Lifetime Access to The Course
✔ Fast & Friendly Support in the Q&A section
✔ Udemy Certificate of Completion Ready for Download
Dive in now!
We offer full support, answering any questions.
See you in the course!
R Programming Language
R Programming Language for Statistical Computing and Graphical Representation
Created by DATAhill Solutions Srinivas Reddy  Data Scientist
Students: 308, Price: $94.99
Students: 308, Price: Paid
This course is designed for software programmers, statisticians and data miners who are looking forward for developing statistical software using R programming. If you are trying to understand the R programming language as a beginner, this tutorial will give you enough understanding on almost all the concepts of the language from where you can take yourself to higher levels of expertise.
Before proceeding with this course, you should have a basic understanding of Computer Programming terminologies. A basic understanding of any of the programming languages will help you in understanding the R programming concepts and move fast on the learning track.
Learning R Programming for Data Science
Beginner to Intermediate R Programming Language Training Course
Created by Mohammed Barakat  Excellence Enabler and Process Management Expert
Students: 50, Price: $19.99
Students: 50, Price: Paid
This course puts the participant in the right path to become a competent Data Scientist by teaching him/her the basics of R Language as one prominent tool in Data Science.
The course starts by introducing Data Science and the steps taken to complete a Data Science project. Then it continues with lectures on various methods and functions of R enabling the participant to start his/her journey towards becoming a Data Scientist with R.
In this course participants will learn how to install and configure R and RStudio. Besides, participants will be able to create various data structures such as Vectors, Matrices, Factors, Data Frames, and Lists. They will solve simple data problems using Operators, Conditional Statements, Loops, base and userdefined functions. Participants will understand and use different data gathering and manipulation methods such as getting and cleaning external files, the Apply family, Regular Expressions, Dates & Times, Base Plotting, and the dplyr package.
Beginners Data Analysis and Visualization with R Programming
Learn the fundamentals of R language through extensive lab exercises showing data analytics and visualization
Created by SKILL CURB  TECHNOLOGY MADE EASY
Students: 18, Price: $19.99
Students: 18, Price: Paid
This course is designed for the students who are at their initial stage or at the beginner level in learning R Programming for Data Analysis and Visualization. This course is different from other R courses available, as it covers extreme basics in less time.
The course used the opensource RStudio which is a GUI based platform to code in R language. Starting from the very basics on setting up the R environment to run it successfully on your machine, this course covers fundamental concepts of variables, basics of R; like taking user inputs , printing the output in R and much more. This course also covers the control structures used in R, the loops, using the builtin functions and creating your own functions in R. An in depth section is dedicated to the data structures in R, where students will learn about vectors, matrices, arrays, lists, dataframes and factors in R programming.
Finally using all the knowledge gained, students will learn about visualizing and analyzing data in RStudio by plotting charts and graphs for different use case examples.
The course is designed with handson practice for the students to code in R. All the codes are also made available to the students in the resource section and in our github repo.