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


(Sample video – Source: YoutubeVisit Course Page on Udemy)

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Course Description from Udemy

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 step-by-step. 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 real-life 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

 


(Sample video – Source: YoutubeVisit Course Page on Udemy)

What are the requirements?

  • No prior knowledge or experience needed. Only a passion to be successful!

What am I going to get from this course?

  • Learn to program in R at a good level
  • Learn how to use R Studio
  • Learn the core principles of programming
  • Learn how to create vectors in R
  • Learn how to create variables
  • Learn about integer, double, logical, character and other types in R
  • Learn how to create a while() loop and a for() loop in R
  • Learn how to build and use matrices in R
  • Learn the matrix() function, learn rbind() and cbind()
  • Learn how to install packages in R
  • Learn how to customize R studio to suit your preferences
  • Understand the Law of Large Numbers
  • Understand the Normal distribution
  • Practice working with statistical data in R
  • Practice working with financial data in R
  • Practice working with sports data in R


(Sample video – Source: YoutubeVisit Course Page on Udemy)

Who is the target audience?

  • This course is for you if you want to learn how to program in R
  • This course is for you if you are tired of R courses that are too complicated
  • This course is for you if you want to learn R by doing
  • This course is for you if you like exciting challenges
  • You WILL have homework in this course so you have to be prepared to work on it

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

Go to Udemy Now

 

 

 

 

 




R Programming

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R Programming is offered on Coursera by Johns Hopkins University,Baltimore,USA.This 4 week course has over 50 video lectures.




R Programming: Advanced Analytics In R For Data Science

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Course Description

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 date-times 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 apply-type 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.

What are the requirements?

  • Basic knowledge of R
  • Knowledge of the GGPlot2 package is recommended
  • Knowledge of dataframes
  • Knowledge of vectors and vectorized operations

What am I going to get from this course?

  • Perform Data Preparation in R
  • Identify missing records in dataframes
  • Locate missing data in your dataframes
  • Apply the Median Imputation method to replace missing records
  • Apply the Factual Analysis method to replace missing records
  • Understand how to use the which() function
  • Know how to reset the dataframe index
  • Work with the gsub() and sub() functions for replacing strings
  • Explain why NA is a third type of logical constant
  • Deal with date-times in R
  • Convert date-times into POSIXct time format
  • Create, use, append, modify, rename, access and subset Lists in R
  • Understand when to use [] and when to use [[]] or the $ sign when working with Lists
  • Create a timeseries plot in R
  • Understand how the Apply family of functions works
  • Recreate an apply statement with a for() loop
  • Use apply() when working with matrices
  • Use lapply() and sapply() when working with lists and vectors
  • Add your own functions into apply statements
  • Nest apply(), lapply() and sapply() functions within each other
  • Use the which.max() and which.min() functions

Who is the target audience?

  • Anybody who has basic R knowledge and would like to take their skills to the next level
  • Anybody who has already completed the R Programming A-Z course
  • This course is NOT for complete beginners in R



Linear Regression and Modeling

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Linear Regression and Modeling is offered on Coursera by Duke University,Durham NC, USA.The time commitment is 5-7 hours for 4 weeks




Introduction to R

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Course Description

With “Introduction to R”, you will gain a solid grounding of the fundamentals of the R language!

This course has about 90 videos and 140+ exercise questions, over 10 chapters. To begin with, you will learn to Download and Install R (and R studio) on your computer. Then I show you some basic things in your first R session.

From there, you will review topics in increasing order of difficulty, starting with Data/Object Types and Operations, Importing into R, and Loops and Conditions.

Next, you will be introduced to the use of R in Analytics, where you will learn a little about each object type in R and use that in Data Mining/Analytical Operations.

After that, you will learn the use of R in Statistics, where you will see about using R to evaluate Descriptive Statistics, Probability Distributions, Hypothesis Testing, Linear Modeling, Generalized Linear Models, Non-Linear Regression, and Trees.

Following that, the next topic will be Graphics, where you will learn to create 2-dimensional Univariate and Multi-variate plots. You will also learn about formatting various parts of a plot, covering a range of topics like Plot Layout, Region, Points, Lines, Axes, Text, Color and so on.

At that point, the course finishes off with two topics: Exporting out of R, and Creating Functions.

Each chapter is designed to teach you several concepts, and these have been grouped into sub-sections. A sub-section usually has the following:

  • A Concept Video
  • An Exercise Sheet
  • An Exercise Video (with answers)

What are the requirements?

  • Windows/Mac/Linux
  • Basic proficiency in math – vectors, matrices, algebra
  • Basic proficiency in statistics – probability distributions, linear modeling, etc
  • A high speed internet connection

What am I going to get from this course?

  • 90 videos (15+ hours)
  • To educate you on the fundamentals of R
  • 140+ exercise problems
  • To accelerate your learning of R through practice

Who is the target audience?

  • Enterprise Data Analysts
  • Students
  • Anyone interested in Data Mining, Statistics, Data Visualization



Statistics with R – Advanced Level

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Course Description

If you want to learn how to perform real advanced 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 an analysis of covariance or a mixed analysis of variance, how to execute a binomial logistic regression, how to perform a multidimensional scaling or a factor analysis. Everything is here, in this course, explained visually, step by step.

So, what’s covered in this course?

First of all, we are going to study some more techniques to evaluate the mean differences. If you took the intermediate course- which I highly recommend you – you learned about the t tests and the between-subjects analysis of variance. Now we will go to the next level and tackle the analysis of covariance, the within-subjects analysis of variance and the mixed analysis of variance.

Next, in the section about the predictive techniques, we will approach the logistic regression, which is used when the dependent variable is not continuous – in other words, it is categorical. We are going to study three types of logistic regression: binomial, ordinal and multinomial.

Then we are going to deal with the grouping techniques. Here you will find out, in detail, how to perform the multidimensional scaling, the principal component analysis and the factor analysis, the simple and the multiple correspondence analysis, the cluster analysis (both k-means and hierarchical) , the simple and the multiple discriminant analysis.

So after finishing this course, you will be a real expert in statistical analysis with R – you will know a lot of sophisticated, state-of-the art analysis techniques that will allow you to deeply scrutinize your data and get the most information out of it. So don’t wait, enroll today!

What are the requirements?

  • R and R studio
  • knowledge of advanced statistics

What am I going to get from this course?

  • perform the analysis of covariance
  • run the one-way within-subjects analysis of variance
  • run the two-way within-subjects analysis of variance
  • run the mixed analysis of variance
  • perform the non-parametric Friedman test
  • execute the binomial logistic regression
  • run the multinomial logistic regression
  • perform the ordinal logistic regression
  • perform the multidimensional scaling
  • perform the principal component analysis and the factor analysis
  • run the simple and multiple correspondence analysis
  • run the cluster analysis (k-means and hierarchical)
  • run the simple and multiple discriminant analysis

Who is the target audience?

  • students
  • PhD candidates
  • academic researchers
  • business researchers
  • University teachers
  • anyone looking for a job in the statistical analysis field
  • anyone who is passionate about quantitative analysis



Advanced R Programming

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Advanced R Programming is offered on Coursera by Johns Hopkins University,Baltimore,USA.This 4 week course has over 50 video lectures.




Data Science and Machine Learning Bootcamp with R

(Sample video – Source: YoutubeVisit Course Page on Udemy)

Disclaimer: If you sign up for a course using the links on this page, you receive a (huge) discount on the regular price, and R-exercises earns a commission. This helps us to keep R-exercises free.

Course Description from Udemy

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 Vector Machines

and much, much more!
Enroll in the course and become a data scientist today!

What are the requirements?

  • Computer Access with download privileges.
  • Basic Math Skills

What am I going to get from this course?

  • Program in R
  • Use R for Data Analysis
  • Create Data Visualizations
  • Use R to handle csv,excel,SQL files or web scraping
  • Use R to manipulate data easily
  • Use R for Machine Learning Algorithms
  • Use R for Data Science

Who is the target audience?

  • Anyone interested in becoming a Data Scientist

Learn more about Data Science and Machine Learning Bootcamp with R

Go to Udemy Now

 

 

 




Applied Data Science with R

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Course Description

“Data Science is the sexiest job of the 21st century – It has exciting work and incredible pay”.

Learning Data Science though is not an easy task. The field traverses through Computer Science, Programming, Information Theory, Statistics and Artificial Intelligence. College/University courses in this field are expensive. Becoming a Data Scientist through self-study is challenging since it requires going through multiple books, websites, searches and exercises and you will still end up feeling “not complete” at the end of it. So how do you acquire full-stack Data Science skills that will get you a and give you the confidence to execute it?

Applied Data Science with R addresses the problem. This course provides extensive, end-to-end coverage of all activities performed in a Data Science project. If teaches application of the latest techniques in data acquisition, transformation and predictive analytics to solve real world business problems. The goal of this course is to teach practice rather than theory. Rather than deep dive into formulae and derivations, it focuses on using existing libraries and tools to produce solutions. It also keeps things simple and easy to understand.

Through this course, we strive to make you fully equipped to become a developer who can execute full fledged Data Science projects. By taking this course, you will

  • Appreciate what Data Science really is
  • Understand the Data Science Life Cycle
  • Learn to use R for executing Data Science Projects
  • Master the application of Analytics and Machine Learning techniques
  • Gain insight into how Data Science works through end-to-end use cases.

By becoming a student of V2 Maestros, you will also get maximum discounts on all of our other current and future courses (coupon codes inside the course material). You will also get prompt support of all your queries and questions. We continuously strive to improve our course material to reflect the latest trends and technologies

What are the requirements?

  • Programming Experience in at least one language like Java, C/C++/C#, Python, Perl
  • Experience in analyzing Data preferred

What am I going to get from this course?

  • Appreciate what Data Science really is
  • Understand the Data Science Life Cycle
  • Learn to use R for executing Data Science Projects
  • Master the application of Analytics and Machine Learning techniques
  • Gain insight into how Data Science works through end-to-end use cases.

Who is the target audience?

  • IT Professionals aspiring to be Data Scientists
  • Students who want to learn about Data Science domain
  • Statisticians and Project Managers who want to expand their horizon into Data Science



Automating Data Exploration with R

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Course Description

As data scientists and analysts we face constant repetitive task when approaching new data sets. This class aims at automating a lot of these tasks in order to get to the actual analysis as quickly as possible. Of course, there will always be exceptions to the rule, some manual work and customization will be required. But overall a large swath of that work can be automated by building a smart pipeline. This is what we’ll do here. This is especially important in the era of big data where handling variables by hand isn’t always possible.

It is also a great learning strategy to think in terms of a processing pipeline and to understand, design and build each stage as separate and independent units.

What are the requirements?

  • Basic understanding of R programming
  • Some statistical and modeling knowledge

What am I going to get from this course?

  • Build a pipeline to automate the processing of raw data for discovery and modeling
  • Know the main steps to prepare data for modeling
  • Know how to handle the different data types in R
  • Understand data imputation
  • Treat categorical data properly with binarization (making dummy columns)
  • Apply feature engineering to dates, integers and real numbers
  • Apply variable selection, correlation and significance tests
  • Model and measure prepared data using both supervised and unsupervised modeling

Who is the target audience?

  • Interest and need to process raw data for exploration and modeling in R