Learn the Grammar of Graphics implemented in ggplot2 syntax using geometric objects, aesthethic mappings and facet plots. Use basic dplyr verbs to filter rows, select columns and sort/arrange datasets in combination with the pipe
Tidy your data with more complex transformations and apply various join operators.
See how to install R and RStudio on your local machine. Configure your development environment to increase your speed and productivity.
Create forecasting models for time series, linear regression and classification using decision trees. Evaluate the performance of your models and decide which one to choose based on different metrics.
Communicate your results through RMarkdown reports and create different outputs based on the same report in PDF, Word, HTML and even Powerpoint.
Learn to use R as a functional-, vector oriented programming language and apply basic functions and operators of the language. See how
NA values are integrated into the language and how they are treated in different functions. Get an overview of different basic data types like
factor and objects including the
data.frame. Understand how coercion works between objects and see why and when it can fail. Get a feeling how to use R efficiently through vector oriented programming.
Read and write data in various file formats including CSV- and Excel files and compare performance with binary formats like HDF5 and Apache Arrow. Use different Databases through the DBI interface like Oracle, PostgreSQL and SQLite. Scrape data from the Internet and extract common elements from tables.
Create basic plots for explorative data analysis using commands like
pairs(). Get introduced to the popular declarative plotting package ggplot2 and learn basic geoms and tranformations. Interactively create charts and see how code changes for different plots. Customize plots with themes and apply corporate designs. Use plotly for interactive charts and see how it compares to ggplot2 for various chart types.
See how the modeling framework in R is set up and use linear- and logistic regression models to predict unseen data. Learn how data needs to be prepared to obtain optimal prediction results. Understand estimators like AIC and BIC and how they can be applied to avoid overfitting. Use systematic frameworks for model selection and decide if a resulting, fitted model makes intuitive sense. Decide based on the problem and data when it makes sense to use more complex/non-linear models.
Reports are often the end result of your data analysis endeavor. With the RMarkdown format it is possible to create reports in various output formats including HTML, PDF, WORD or even interactive Shiny dasboards from one single source file. Learn how results of your data analysis can be clearly communicated to colleagues and decision makers. See how reports can be customized to reflect your corporate branding and automate the entire report generation workflow.
The Quantargo certificate in “Data Analysis with R” demonstrates that your are literate in the topics covered by this course. You have...
We have built a complete online Learning Hub with hands-on quizzes, code exercises and viedeos and an online Workspace that lets you immediately apply learned skills by trying out code.
Track progress and learn in your own pace with our interactive tutorials. In the CourseCockpit you’ll find all assets and course materials.
A complete RStudio environment to try out new ideas and play around with different packages and features. All files are synced automatically.