If you want to learn how to perform the most useful statistical analyses in the R program, you have come to the right place. Now you don’t have to scour the web endlessly in order to find how to do a Pearson or Spearman correlation, an independent t test or a factorial ANOVA, how to perform a sequential regression analysis or how to compute the Cronbach’s alpha. Everything is here, in this course, explained visually, step by step. So, what will you learn in this course? First of all, you will learn how to perform association tests in R, both parametric and non-parametric: the Pearson correlation, the Spearman and Kendall correlation, the partial correlation and the chi-square test for independence. The test of mean differences represent a vast part of this course, because of their great importance. We will approach the t tests, the analysis of variance (both univariate and multivariate) and a few non-parametric tests. For each technique we will present the preliminary assumption, run the procedure and carefully interpret all the results. Next you will learn how to perform a multiple linear regression analysis. We have assign several big lectures to this topic, because we will also learn how to check the regression assumptions and how to run a sequential (or hierarchical) regression in R. Finally, we will enter the territory of statistical reliability – you will learn how to compute three important reliability indicators in R. So after graduating this course, you will get some priceless statistical analysis knowledge and skills using the R program. Don’t wait, enroll today and get ready for an exciting journey!

R is considered as lingua franca of Data Science. Candidates with expertise in R programming language are in exceedingly high demand and paid lucratively in Data Science. IEEE has repeatedly ranked R as one of the top and most popular Programming Languages. Almost every Data Science and Machine Learning job posted globally mentions the requirement for R language proficiency. All the top ranked universities like MIT have included R in their respective Data Science courses curriculum. With its growing community of users in Open Source space, R allows you to productively work on complex Data Analysis and Data Science projects to acquire, transform/cleanse, analyse, model and visualise data to support informed decision making. But there's one catch: R has quite a steep learning curve! How's this course different from so many other courses? Many of the available training courses on R programming don't cover it its entirety. To be proficient in R for Data Science requires thorough understanding of R programming constructs, data structures and none of the available courses cover them with the comprehensiveness and depth that each topic deserves. Many courses dive straight into Machine Learning algorithms and advanced stuff without thoroughly comprehending the R programming constructs. Such approaches to teach R have a lot of drawbacks and that's where many Data Scientists struggle with in their professional careers. Also, the real value in learning R lies in learning from professionals who are experienced, proficient and are still working in Industry on huge projects; a trait which is missing in 90% of the training courses available on Udemy and other platforms. This is what makes this course stand-out from the rest. This course has been designed to address these and many other fallacies and uniquely teaches R in a way that you won't find anywhere else. Taught by me, an experienced Data Scientist currently working in Deloitte (World's largest consultancy firm) in Australia and has worked on a number of Data Science projects in multiple niches like Retail, Web, Telecommunication and web-sector. I have over 5 years of diverse experience of working in my own start-ups (related to Data Science and Networking), BPO and digital media consultancy firms, and in academia's Data Science Research Labs. Its a rare combination of exposure that you will hardly find in any other instructor. You will be leveraging my valuable experience to learn and specialize R. What you're going to learn in this course? The course will start from the very basics of introducing Data Science, importance of R and then will gradually build your concepts. In the first segment, we'll start from setting up R development environment, R Data types, Data Structures (the building blocks of R scripts), Control Structures and Functions. The second segment comprises of applying your learned skills on developing industry-grade Data Science Application. You will be introduced to the mind-set and thought-process of working on Data Science Projects and Application development. The project will then focus on developing automated and robust Web Scraping bot in R. You will get the amazing opportunities to discover what multiple approaches are available and how to assess alternatives while making design decisions (something that Data Scientists do everyday). You will also be exposed to web technologies like HTML, Document Object Model, XPath, RSelenium in the context of web scraping, that will take your data analysis skills to the next level. The course will walk you through the step by step process of scraping real-life and live data from a classifieds website to analyse real-estate trends in Australia. This will involve acquiring, cleansing, munging and analyzing data using R statistical and visualisation capabilities. Each and every topic will be thoroughly explained with real-life hands-on examples, exercises along with disseminating implications, nuances, challenges and best-practices based on my years of experience. What you will gain from this course will be incomparable to what's currently available out there as you will be leveraging my growing experience and exposure in Data Science. This course will position you to not only apply for Data Science jobs but will also enable you to use R for more challenging industry-grade projects/problems and ultimately it will super-charge your career. So take the decision and enrol in this course and lets work together to make you specialize in R Programming like never before!

This course, The Comprehensive Statistics and Data Science with R Course , is mostly based on the authoritative documentation in the online "An Introduction to R" manual produced with each new R release by the Comprehensive R Archive Network (CRAN) development core team. These are the people who actually write, test, produce and release the R code to the general public by way of the CRAN mirrors. It is a rich and detailed 10-session course which covers much of the content in the contemporary 105-page CRAN manual. The ten sessions follow the outline in the An Introduction to R online manual and specifically instruct with respect to the following user topics: 1. Introduction to R; Inputting data into R 2. Simple manipulation of numbers and vectors 3. Objects, their modes and attributes 4. Arrays and matrices 5. Lists and data frames 6. Writing user-defined functions 7. Working with R as a statistical environment 8. Statistical models and formulae; ANOVA and regression 9. GLMs and GAMs 10. Creating statistical and other visualizations with R It is a comprehensive and decidedly "hands-on" course. You are taught how to actually use R and R script to create everything that you see on-screen in the course videos. Everything is included with the course materials: all software; slides; R scripts; data sets; exercises and solutions; in fact, everything that you see utilized in any of the 200+ course videos are included with the downloadable course materials. The course is structured for both the novice R user, as well as for the more experienced R user who seeks a refresher course in the benefits, tools and capabilities that exist in R as a software suite appropriate for statistical analysis and manipulation. The first half of the course is suited for novice R users and guides one through "hands-on" practice to master the input and output of data, as well as all of the major and important objects and data structures that are used within the R environment. The second half of the course is a detailed "hands-on" transcript for using R for statistical analysis including detailed data-driven examples of ANOVA, regression, and generalized linear and additive models. Finally, the course concludes with a multitude of "hands-on" instructional videos on how to create elegant and elaborate statistical (and other) graphics visualizations using both the base and gglot visualization packages in R. The course is very useful for any quantitative analysis professional who wishes to "come up to speed" on the use of R quickly. It would also be useful for any graduate student or college or university faculty member who also seeks to master these data analysis skills using the popular R package.

R shiny allows you to present your data interactively – that means your app users can: Set filters and columns in tables Generate parameters for plots Zoom and focus on specific areas of plots Focus on selected portions of your data Provide or upload files, text and all sorts of data And much more App users can do all of this without any R knowledge. You do the coding, your users get the info they are looking for! In this course I will show you step by step how to master R Shiny. We will start out with the general shiny script – all scripts should have the same basic structure. You will then learn how to make your app interactive by using input widgets . These features take user inputs which can be used to generate or modify the app content. You will learn how to style your app for an appealing layout . I will also show you how to use HTML tags to integrate or embed standard web content like youtube videos, pdfs, text, pictures and much more. After that you will learn about advanced shiny apps that allow zooming and downloading And you will learn how to integrate tables. This course is not just pure theory. The last section is all about applying your new shiny skills. I prepared a course project which combines all the topics discussed in the course and more . This project is modeled after a real world financial app. You will get the project description and the raw data to test your skills. Note that this course requires basic R skills. If you are totally new to R, check out some R intro material first and then revisit the course. All the software downloads, add on packages as well as entry level hosting for shiny are totally free. I will show you what you need and where to get it. That includes hosting as well. Take a look at shiny – your boss, colleagues, students, customers will be astonished what modern day data visualization can do.

If you use Excel for any type of reporting or analytics then this course is for you. There are a lot of great courses teaching R for statistical analysis and data science that can sometimes make R seem a bit too advanced for every day use. Also since there are many different ways of using R that can often add to the confusion. The reality is that R can be used to make your every day reporting analytics that you do in Excel much faster and easier without requiring any complex statistical techniques while at the same time giving you a solid foundation to expand into those areas if you so wish. This course uses the Tidyverse standards for using R which provides a single, comprehensive and easy to understand method for using R without complicating things via multiple methods. It's designed to build upon the the skills you are already familiar with in Excel to shortcut your learning journey. When I first started using R I thought that it could be a good replacement for the automation type processes I used to write in VBA. This can be quite off putting for a lot of Excel users as VBA often adds an extra layer of complexity to your work and is often only something which is done to automate a process which has already been established in Excel. One of the key benefits of Excel is that you are working directly with the data without having to go through the complexity and overhead of using a programming language. Programming languages such as VBA are actually very difficult for working with data as there isn't even any concise way of referencing common data elements such as named table columns. To carry out an operation on every row would take several lines of code which runs slow and ends up hiding your formula which actually contains your business logic. Despite all of this people use VBA anyway as once you invest the time to setup your processes you can run the exact same steps thousands of times with a click of a button. What if there were a way to work directly with your data as simply as Excel but also have more programming power than VBA? That's what R can do for you. Since I've started using R people have asked me when it would be beneficial to use R instead of Excel. Here are some examples 1. vlookups and sumifs on large datasets can run very slowly in Excel. I've helped people to replace multiple lines of vlookups that take 80 minutes to run in Excel with a single function in R that takes less than 1/10th of a second. 2. Exploring and analysing your data in R can be Viewed in a simple table like Excel but also has a wide range of other methods which can be more effective. 3. Dashboards and visualisations are much richer and easier to construct than in Excel 4. Distributing your work in Excel can be beneficial since almost everyone has Excel installed. The problems with this are that not everyone always has the same version of Excel or addins installed which means your work might not be compatible. Also files are usually emailed around which can very quickly lead to hundreds of untracked copies of your Excel files with slight variations in them. The outputs from R can be simple Excel or csv files however your output can also be a web app that can be centrally stored and tracked on a server compatible with any web browser on your computer or smart phone. 5. Team collaboration and version control in Excel is done via shared workbooks and track changes. Turning on these features in Excel disables some of Excels best features and still results in file locking. Team collaboration in R is done on github which allows you to easily work across teams without file locking issues and full audit histories of your work. The beauty of R is that once you start using it you will no longer have to make a special investment of time to automate your processes after your analysis is done. Practically anything that you can do in Excel you'll be able to do faster and better for even your first round of analysis and will leave you with an script which means your work is reproducible and automated from the very beginning. Even though your existing Excel skills will help you to pick up R one of the hardest things is that you're so familiar with Excel that it's too easy to keep on using it. I used Excel for years and spent thousands of hours studying how to use it more efficiently, I even taught advanced courses in it. It seemed obvious to me at the time that it was one of the most efficient ways to work with data. Even though working with a programming language might be more powerful it often had too much over head and was too removed from the actual data analysis. R is the programming language I wish I learnt 20 years ago. Perhaps somewhat counter intuitively you'll end up spending less time thinking about how to put a piece  of work together than Excel and more time looking at your data in new ways that you've probably never even thought of.

'R Crash Course - a short and concise introduction to R and R Studio, R-programming for the Beginners' This is a R crash course for anyone who previously had no or very little contact with script-based programming in R. The main goal is to establish the basic understanding needed for more advanced courses that uses R language, RStudio and R-programming for example, for data science, machine learning or statistical analysis in R. This is also a baseline course that I will recommend to my students to take to refresh their knowledge on learn R-programming language for my upcoming data science courses in R. The best about this course that is a very concise manner (2 hours!) you will be able to learn all the fundamentals of R-programming that will enable you to get started with R! What will you learn in this course: § Package Management § Calculate with R § Variables § Vectors § Matrices § Lists § Data frames § Missing values § Functions § Control Structures § For loops All the R-scripts used in this course will be also provided to you. The course is ideal for professionals such as data scientists, statisticians, geographers, programmers, social scientists, geologists, and all other experts who need to use statistics & data science in their field. This course is NOT for you if you an intermediate or advanced user of R and don't need an introduction to R programming! Let’s get started!

If you want to learn how to perform the basic statistical analyses in the R program, you have come to the right place. Now you don’t have to scour the web endlessly in order to find how to compute the statistical indicators in R, how to build a cross-table, how to build a scatterplot chart or how to compute a simple statistical test like the one-sample t test. Everything is here, in this course, explained visually, step by step. So, what will you learn in this course? First of all, you will learn how to manipulate data in R, to prepare it for the analysis: how to filter your data frame, how to recode variables and compute new variables. Afterwards, we will take care about computing the main statistical figures in R: mean, median, standard deviation, skewness, kurtosis etc., both in the whole population and in subgroups of the population. Then you will learn how to visualize data using tables and charts. So we will build tables and cross-tables, as well as histograms, cumulative frequency charts, column and mean plot charts, scatterplot charts and boxplot charts. Since assumption checking is a very important part of any statistical analysis, we could not elude this topic. So we’ll learn how to check for normality and for the presence of outliers. Finally, we will perform some basic, one-sample statistical tests and interpret the results. I’m talking about the one-sample t test, the binomial test and the chi-square test for goodness-of-fit. So after graduating this course, you will know how to perform the essential statistical procedures in the R program. So… enroll today!

If you've always been a bit curious about how R works (specifically, how to do data wrangling with it) this course is for you. I cover a nice example and demonstrate lots of nifty, very useful techniques for turning a collection of messy data sources into something that's tidy. Many of the essential components of your data wrangling toolkit will be covered. This is not exactly a theoretical style course. I'm not going to go be explaining all the different techniques available to you with R code. Rather, this is a brief-ish demonstration-style course.. where you can follow along.

Have you ever wanted to code and use data to your advantage? Well, you've come to the right place! After purchasing this course, you'll be taken step-by-step through every process needed to do just that. Funded by a #1 Kickstarter Project by Mammoth Interactive. Our very talented instructor John Kemp, explains everything from a basic, beginner level. That means, you don't have to have any prior coding experience to succeed here. R Programming for Absolute Beginners: from Data Analytics to Visualization to Machine Learning! 5 hours on-demand video! Learn offline via the Udemy app 14 Articles 12 Downloadable Resources Full lifetime access Manipulate Data and Learn about Matrices with R. Data scientist and online mentor John Kemp will take you through the process of learning to code from scratch in a massively popular programming language: R in RStudio. You will learn everything it takes to be a data analyst, including: inputting and outputting data manipulating and storing solving business problems visualizing data making predictions using machine learning and so much more with the abundant R packages of the exploding R community. Learn Online at ANY Pace! The beauty of taking an online course like this is the ability to replay any of the lectures at any time. There is no time limit or final tests. You get to learn at your own pace with a practical model method of learning. One of the best features is that you can watch the courses at any speed you want. This means you can speed up the or slow down the video if you want to. This course is project-based so you will not be learning a bunch of useless coding practices. At the end of this course you will have real world apps to use in your portfolio. We feel that project based training content is the best way to get from A to B. Taking this course means that you learn practical, employable skills immediately. You can use the projects you build in this course to add to your LinkedIn profile. Give your portfolio fuel to take your career to the next level. Learning how to code is a great way to jump in a new career or enhance your current career. Coding is the new math and learning how to code will propel you forward for any situation. Learn it today and get a head start for tomorrow. People who can master technology will rule the future. An Amazing Instructor Deep in the Field. John Kemp has been programming in R as a data scientist since 2011. He has spent 100+ hours tutoring students online to help them learn R from scratch or to enhance their R abilities. John has been set up as an online mentor to partner with students looking to enter into the data science profession. In addition, John uses R as a hobby and will also help various businesses with data manipulation, network optimization and other data science tasks. Enroll Now while on Sale!

Do you want to master the most widely used language? Want to be in the league of the achievers and have your own success story? BEGIN THE EXCITING EXPEDITION OF LEARNING THE MOST WIDELY USED LANGUAGE!!! We are working towards making the world a better place with programming and learning R is the most bankable support that one can provide to its career. This course will take you through an exciting journey and you will surely feel overwhelmed with it. Moving slowly and steadily is the mantra behind this one so that you grasp each and every part religiously. The course will lead the way in making you learn how to do programming in R and use it for an effective analysis of data. As we start from the very basics we will guide you through installing the software essential for the programming to everything that needs to be a part of the curriculum to make you a proficient R programmer. Irrespective of the fact whether you have learned to program before or not, you can excel in the course if you do not move in the classroom with any inhibitions. With the knowledge acquired you will be able to easily work upon data analysis. As an important part of the changing world, this course will surely give you an upgrade to stand firm in these competitive times . You will become ready to climb the ladder of success with the certification in your hand. The course will definitely be a great learning experience as it is meticulously designed to make you more skilled. Our course has been created by the connoisseur of the programming language who is accustomed to it from the top to the bottom having great exposure and experience. Videos, written documents, quizzes, and exercises are the tools that we provide to make you understand the course in a better manner, add to your knowledge, and enrich your experience. The benefits after the course get completed • Gain knowledge of the basic principles of programming • Comprehend the critical concept of R programming from the basics • Understand to import and Export data • Gain knowledge of the different operators that help in performing the tasks • Get an understanding of the different data types and data structures • Acquire the knowledge of how to create vectors and variables in R • Comprehend how to build and use matrices in R • Learn about character, integer, numeric, double, logical, complex types in R • Become proficient in installing and handling packages in R • Understand Subsetting data in R • Comprehend data management and missing values in R • Gain knowledge of how to create a loop and if-else statement in R • Familiarize oneself with descriptive statistics • Learn to apply family in R • Get an understanding of data manipulation in R • Become proficient in summarizing the data graphically Some facts that need your attention • R is the most widely used open-source programming language • R is the IT skill that is highest paid • There are over 2 million R users around the globe • The number of R users is increasing by 40% every year • R is growing faster if compared to any other data science language • 70% of data miners use it • A lot of organizations are making use of it Reasons that make the course better than its contemporaries • Specialists from the field to teach the participants • Curriculum that is designed keeping in mind the requirements of the time • Tools added for a complete understanding of the subject • Recordings and videos to simplify things and make it exciting Words of appreciation from our Students 1. “A great to start with and the trainer took his time to teach the material methodically and overall did a great job. Additionally, for a course that is portrayed as 'R for Data Science', it is definitely a very good one to learn and enhance your career.”- Gunjan Tiwari 2. “I have very high expectations already. I am new to R programming but never found it tough to understand. I like the method of training and already started loving it. Good course and a great initiative by Henry Harvin” Abhay Srivastava 3. “This course is highly recommended for those who want to learn R from the scratch. The course moves at a great pace without creating any dull moments. It helps in developing the skill and being a proficient R programmer.” Lucky Mehndiratta 4. “The classes were really interactive, and I found the trainer very experienced and helpful. I felt free to clear my doubts and they were always there to help us all. I found Henry Harvin as the best platform for learning the course with top-class trainers and would suggest to my friends.” Samuel Thomas 5. “The course is very well-designed. The faculty pays a lot of attention at the individual level. The trainers give their best to deliver the course and this is the best part. The course itself becomes enriching if the person imparting it is good. So, I would love to recommend this course to others as well” Abheesha Gupta