The R language is best suited for statistical computations and visualization. Even if you do not have any prior experience in programming or statistical software, this Learning Path will help you get you up and running not only with the basics of R but also statistically modeling.
This learning journey begin by introducing R and setting things up so that you are ready to go using RStudio, the associated IDE. Then, you will look at R as a programming language and see how the standard things are done in it. You will obtain a dataset and then learn how to clean the dataset. Data cleaning constitutes almost 80% of data analysis. You will also explore discrete distributions, continuous distributions, and random number generation. Finally, you will see how to model your data and discover hypothesis testing. You will dive into descriptive statistics and graphs, parametric and nonparametric statistical methods, correlation and regression analysis, and time-series analysis.
By the end of this Learning path, you will be able to use R to model the different types of data with ease.
About The Author
Dr. Samik Sen is a Theoretical Physicist and loves hard problems to think about. After his Phd, which was about developing computational methods to solve problems for which no solutions existed, he began thinking about how to tackle the maths problem while lecturing. He developed algorithms to generate problem sets and solutions, and learned how to create video lessons. He has developed a large Facebook community teaching school maths around Ireland, with associated e-learning products and YouTube channel. Samik is currently fascinated by machine and deep Learning.He has developed a machine learning system which is performing better than he can himself which was the hope.
Olgun Aydin is a PhD candidate at Department of Statistics, Mimar Sinan University. He has been working on Deep Learning for his PhD thesis. Also working as Data Scientist.He is so familiar with Big Data technologies like Hadoop, Spark and able to use Hive, Impala. He is a big fan of R. Also he really loves to work with Shiny, SparkR.He has many academic papers and proceedings about applications of statistics on different disciplines. Mr. Olgun really loves statistic and loves to investigate new methods, share his experience with people.
The aim of the video is to introduce the section and overview of the language R.
We need to have the core programs before we can begin and in this video,we show where to get them.
In this video, we look at where to begin, so that we can get started.
In this video, you will learn how RStudio has packages which avoid the problems and how we'll work on them.
In this video, we see more familiar things in R.
In this video, we are now ready to write programs.
In this video, we will look at R data types which are new.
In this video, we will introduce some key commands to study data.
In this video, we willintroduce various commands to help us pick out elements in which we are interested in.
In this video, we will investigate the Titanic dataset to see what it says.
In this video, we willadd a value by processing our data.
In this video,we will download football results from a web page.
In this video, we will use R to do some statistics.
In this video, we will work with distributions using R.
In this video, we will see some of R's graphical power.
In this video, we will use the plotting package, ggplot2.
In this video, we will see another plotting technique known as Facets.
This video gives an introduction to R in general.
This video talks about the benefits of R in general. Why do we use R? Why is R useful?
In this video, we will see how big companies use sample R applications
In this video, we will show how to make the first applications in R
In this video, we will define problems with the installation of R packages
In this video, we will define the usage of matrix and dataframes in R.
This video shows the importance of using casting strings
This video deals with the concept of data manipulation
This video explains some examples of importing data from third-party sources to R and exporting data from R
In this video, we will import data from PostgreSQL databases and export data from R to PostgreSQL databases
In this video, we will calculate probabilities for some examples ofbinomial, poisson, and negative binomial distribution
In this video, we will calculate probabilities for some examples of normal, exponential, and weibull distribution
In this video, we will generate random numbers from some discrete and continuous distributions
In this video, we will talk about distribution fitting
In this video, we will define the p value and its calculation
In this video, we will show how to calculate some descriptive statistics and draw plots
In this video, we will talk about parametric statistical methods and how to apply parametric statistical method
n this video, we will talk about non-parametric statistical methods and how to apply parametric statistical method
In this video, we will talk about correlation and regression analysis and how to apply correlation and regression analysis
In this video, we will show how do we apply time series analysis
In this video, we will show how to apply missing value imputation
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