Data Analysis with R

This course shows you how to execute the most essential data analyses using the R program. Unlike many other R courses out there, it focuses on the statistical analysis only. You will not learn how to work with vectors or matrices, or how to write functions (you can learn this from our free R programming course). However, after completing this course, you will master the statistical analysis in R.

You don’t have to know advanced math to start the course – it was conceived for non-mathematicians. In the video lectures I explain each procedure and show you how to execute it in R, step by step (and, of course, how to interpret the results). All the procedures are presented in a straightforward manner, avoiding the technical jargon and the mathematical formulas as much as possible. The formulas are used only when it is absolutely necessary, and they are explained in detail.

So if you want to learn how to perform complex statistical analyses in R, this course will put you on the path to mastery.

Now let’s talk a about the course structure – let’s see what you will learn exactly.

In the beginning, you will acquire the three basic skills of any data analyst: data manipulation, data summarization and data visualization.

So in the first course section you fill learn how to manipulate data in R, how to prepare it for the analysis. Afterwards, we will take care about data summarization – computing the main statistical indicators in R, both in the whole population and in subgroups of the population, as well as building tables and cross-tables. Then you will understand how to visualize data using charts. So we will create histograms, cumulative frequency charts, column charts, scatterplot charts and boxplot charts.

After setting these fundamentals in place, we will go on to checking statistical assumptions (normal distribution and absence of outliers). Next, you will learn how to perform a few univariate analyses (one-sample t test, binomial test, chi-square test for goodness-of-fit).

Correlations are the next major section of our course. Here we will study both parametric and non-parametric association tests (Pearson, Spearman etc.).

The tests of mean difference represent a vast part of this course, because of their great importance. We will firstly approach the t tests and the analysis of variance (one-way, two-way and three-way). For each technique we will present the statistical assumptions, run the procedure and carefully interpret all the results. Then we will go to the next level and tackle the multivariate analysis of variance (MANOVA), the analysis of covariance (ANCOVA), the within-subjects (or repeated measures) analysis of variance and the mixed analysis of variance. These techniques are widely used in data analysis, especially when we deal with experimental data.

Next, we have reserved a section for the nonparametric tests for mean difference, because these tests were not approached in the basic course. So you will learn how to run and interpret the following tests: Mann-Whitney, Wilcoxon, Kruskal-Wallis and Friedman.

Next you will learn how to perform a multiple linear regression analysis. We have assigned several big lectures to this topic, because we will also learn how to check the regression assumptions (absence of autocorrelation, absence of important multicollinearity, homoscedasticity etc.) and how to run a sequential regression in R.

In the following section we will enter the territory of statistical reliability – you will learn how to compute and interpret three important reliability indicators: Cronbach’s alpha, Cohen’s kappa and Kendall’s W.

Further, we will deal with logistic regression, which is used when our dependent variable is not continuous (i.e. categorical). This regression is used in many types of social research, when we have to answer questions like: what is the probability that a customer will buy a certain product, or a patient will contract a heart disease, or an elector will vote for a certain party. We are going to study thoroughly three types of logistic regression: binomial, ordinal and multinomial.

Then we are going to take care of 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.

This course is a perfect mix of theory and practice. It covers the gap between books (that tell you how to do it without showing you live) and video tutorials (that do show you but don’t explain the theoretical grounds). Upon completion, you will be the go-to person when it comes to statistical analysis with R – you will master the sophisticated, state-of-the art analysis techniques that allow you to deeply investigate your data and get the most information out of it.

Now you can stop scouring the web endlessly in order to find how to compute the statistical indicators in R, how to build a scatterplot chart, how to run a one-way ANOVA or a multiple regression with dummy variables. Everything is here, in this course, explained visually, bit-by-bit.

So don’t procrastinate, register today. You have nothing to lose, because this course comes with an iron-clad, no-question-asked 30-day guarantee.

Click the Enroll button right now and get ready for an exciting journey in world of data analysis!