We love R, and we are certainly proud of using it. But much of the knowledge we get about R tends to be partial and unconnected because we normally are self-taught, right? When we require some particular code, we seek for it on the web. But very often, a lack of a guided approach to R remains present. So, what about learning from online courses of Statistics in R? These online courses of Statistics in R show one clear advantage: R codes and language are installed in our brain in a quicker way and with a better structure. Moreover, these courses let students to overcome the steep learning curve of R, as they are guided by an instructor.
But there exists another clear advantage when coursing R, which is the obtaining of a certificate that confirms the student acquired those skills. This lets students that learn R and Statistics to improve their Curriculum Vitae and to be more competitive in a connected world that is permanently seeking for professionals. For this reason, we promote Udemy courses. Udemy is one of the most-worlwide-known online learning platforms. And the prices of their courses are very cheap. We encourage you to enroll in these recommended online courses of Statistics in R!
Índice de contenidos
- 1 Online courses of Statistics in R by SuperDataScience Team and Kirill Eremenko
- 2 Online courses of Statistics in R by Bogdan Anastasiei
- 3 Specialized courses in R by Diego Fernandez
Online courses of Statistics in R by SuperDataScience Team and Kirill Eremenko
SuperDataScience Team & Kirill Eremenko have two spectacular and amazing online courses of Statistics in R. With more than 13.000 enrolled and satisfied students, they are one of the best R teams ever. Its content presents the highest standards of quality, with a qualification of 4.6 points (over 5) obtained from more than 1500 opinions. They are online courses of statistics in R and all their videos are with captions. This is an absolute guarantee that their online courses of statistics in R are indispensable!
R Programming A-Z™: R For Data Science With Real Exercises!
This first course (R Programming A-Z™: R For Data Science With Real Exercises!) will teach you R programming in detail. It is one of the courses I’ve ever met. This course is truly step-by-step. In every new tutorial we build on what had already learned and move one extra step forward. 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!
You will learn to program in R at a good level using RStudio, from the core principles of programming (creating vectors, variables…) to more advanced skills, creating a while() loop and a for() loop in R and building matrices. You will also learn how to install packages in R and customize RStudio to suit your preferences. And all of this with very clear examples.
R Programming: Advanced Analytics In R For Data Science
El second course (R Programming: Advanced Analytics in R for Data Science) goes further compared to the previous one, as it deeply explores statistical analysis.
You will learn how to perform Data Preparation in R, identify missing records in dataframes, locate missing data in your dataframes. You will also understand how to use the which() function and to reset the dataframe index. Converting date-times into POSIXct time format will be easy for you, as creating a time series in R or using apply(), lapply() and sapply() functions.
Online courses of Statistics in R by Bogdan Anastasiei
Bogdan Anastasiei and is an assistant professor at the University of Iasi, Romania, Faculty of Economics and Business Administration. He teaches Internet marketing and quantitative methods for business and run quantitative risk analyses and feasibility studies for various local businesses and been implied in academic projects on risk analysis and marketing analysis. About 20 years experience in teaching and about 10 years experience in business consulting is a guarantee that Bogdan perfectly knows what he teaches. He developed, among others, three courses to learn R Programming and Statistics, adapted to all levels of expertise (beginner, intermediate, advanced). We encourage you to enroll in these three courses as they are undoubtedly three extraordinarily good online courses of statistics in R!
Statistics with R – Beginner Level
If you want to learn how to perform the basic statistical analyses in the R statistical software, I recommend you to enroll in this course. Now you don’t have to scour the web endlessly in order to find how to compute the statistical indicators in R for example, or how to build a cross-table or a scatterplot chart. Everything is here, in this course, explained visually, step by step.
You will learn how to manipulate data in R in order to prepare it for the analysis. Afterwards, we will compute the main statistical figures in R: mean, median, standard deviation, skewness, kurtosis etc. Then you will learn how to visualize data using tables and charts. We will also learn how to check for normality and for the presence of outliers. Finally, we will perform one-sample statistical tests and interpret the results (one-sample t test, the binomial test and the chi-square test for goodness-of-fit).
Statistics with R – Intermediate Level
In this course, learn how to perform a Pearson or Spearman correlation, an independent t test or a factorial ANOVA, how to do a sequential regression analysis or how to compute the Cronbach’s alpha. Everything in this course is explained visually, step by step.
You will first 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). Later, we will approach the t tests, the analysis of variance (both univariate and multivariate) and a few non-parametric tests. Next you will learn how to perform a multiple linear regression analysis. Finally, we will enter the territory of statistical reliability.
Statistics with R – Advanced Level
In this advanced level course, you will learn 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 in this course is explained visually, step by step.
First, we will tackle the analysis of covariance, the within-subjects analysis of variance and the mixed analysis of variance. We will also approach the logistic regression and grouping techniques (multidimensional scaling, principal component analysis, factor analysis, correspondence analysis, cluster analysis and discriminant analysis).
Specialized courses in R by Diego Fernandez
Diego Fernandez main areas of expertise are finance and data analysis. Within finance he has focused on stock fundamental, technical and investment portfolio analysis. Within data analysis he has concentrated on probability, applied statistics, forecasting models, optimization methods and machine learning. For all of this he has become proficient in Microsoft Excel®, R statistical software and Python programming language analysis tools.
Stock Technical Analysis with R
This course (Stock Technical Analysis with R) teaches stock technical analysis through a practical course with R statistical software using real world data. By exploring main concepts from basic to expert level, you achieve better grades, develop your finance career or make decisions as DIY investor.
Forecasting Models with R
This second course (Forecasting Models with R) teaches forecasting models through a practical course with R statistical software using real world data. Learning forecasting methods is indispensable for business or financial analysis in areas such as sales and financial forecasting, inventory optimization… It is obviously necessary for any business forecasting related decision. Moreover, it is also essential for academic careers in data science, applied statistics, operations research, economics, econometrics and quantitative finance.
Investment Portfolio Analysis with R
The thirs course (Investment Portfolio Analysis with R) teaches investment portfolio analysis through a practical course with R statistical software using index replicating funds historical data for back-testing. Learning investment portfolio analysis is indispensable for finance careers in areas such as asset management, private wealth management, and risk management within institutional investors represented by banks, insurance companies, pension funds, hedge funds, investment advisors, endowments and mutual funds.
Regression Machine Learning with R
The last course (Regression Machine Learning with R) teaches regression machine learning. Learning regression machine learning is indispensable for data mining applications in areas such as consumer analytics, finance, banking, health care, science, e-commerce and social media. It is also essential for academic careers in data mining, artificial intelligence or applied statistical learning. And it is necessary for any business forecasting related decision.