course-bc8a94f0-47ee-11ec-bab1-5b387966a310

Base-R Level 1

course-bc8a94f0-47ee-11ec-bab1-5b387966a310#01-basics

Start your R journey and learn the most fundamental building blocks of the environmenent.  Learn how to use and assign vectors with operators and functions. Use packages to access functions and  data from external sources.

Basics

Data Frames Level 1

course-bc8a94f0-47ee-11ec-bab1-5b387966a310#02-data-frames-tibbles

Create tabular data structures with data frames and see how they compare to tibbles.  Extract column vectors from data frames for computations. Get metadata information like dimensions. Select top- and bottom rows for a quick overview.

Data Frames and Tibbles

My first course

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Test Course2

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course-4ae18ad0-4888-11ec-a8a9-1d59d1303901#01-basics

course-4ae18ad0-4888-11ec-a8a9-1d59d1303901#02-data-frames-tibbles

My course updated

course-33ef7630-c389-11ec-9923-fd5e5c93c164

Complete Guide to Data Science

Learn machine learning fundamentals including use cases and different learning techniques. Differentiate between regression and classification.

Machine Learning Level 1

course-machine-learning-introduction#01-basics

Start your Machine Learning journey and learn the most fundamental building blocks.

Machine Learning Essentials

Introduction to Machine Learning

Transform large and complex data sets using base R and popular package ecosystems like tidyverse and data.table. See how these packages compare in terms of speed and usability. Master dplyr verbs quickly and build data processing pipelines for modelling and plotting. Unleash the power of data.table with lightning fast merges and queries.

Data Transformation Intro

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Discover use cases which require data transformation and the main tools available through base-R, tidyverse and data.table.

Introduction

Data Transformation with base-R

course-r-advanced-data-transformation#02-data-wrangling-with-base-r

Learn data wrangling functions available in base-R to filter, split and aggregate data.

Tidy Data Transformation Level 2

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Learn to use the basic dplyr verbs to filter rows, select columns and sort/arrange datasets in combination with the pipe <code>%>%</code> operator. Tidy your data with more complex transformations and apply various join operators.

Tidy Data Transformation

Data Transformation with data.table

course-r-advanced-data-transformation#04-data-wrangling-with-data-table

Do assignments, joins and aggregations faster with data.table using various optimizations including keys and re-use of objects in memory (no copy).

Advanced Data Transformation

Bring your data to life with advanced reporting capabilities in R. Clearly communicate your insights with sophisticated interactive reports and dashboards using RMarkdown and Shiny.

Shiny Basics

course-r-building-dashboards-with-shiny#01-shiny-basics

Get an introduction to general Shiny concepts and see the first example apps in action. Create your first Shiny app with the most fundamental user interface (UI) components.

Shiny Level 1

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Understand reactive programming concepts and make your app truly interactive. Add more advanced UI features by customizing Cascading Style Sheets (CSS) and including additional CSS/Javascript dependencies.

Advanced Shiny Concepts

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Create your first dashboards using the flexdashboard and shinydashboard frameworks.

Dashboards

course-r-building-dashboards-with-shiny#04-productionizing-shiny

Put your interactive Shiny application to production. Create an R package with your Shiny code and put it into a docker container for easier maintenance.

Productionizing Shiny

Building Dashboards with Shiny

R is known for its complex modelling capabilities among statisticians and data scientists. However, in corporate environments MS Excel is the primary tool used which needs to be integrated into many data science workflows. Most often, Excel files need to be used as a data source or as a reporting tool. In this tutorial you will learn about the most important R packages to read, create and style Excel files so that you can seamlessly integrate them into end-to-end data science workflows.

R/Excel Introduction

Overview of ways to read and create Excel files using R including readxl, writexl, openxlsx, RODBC, ...

Read Excel Files using readxl

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Read Excel files using readxl and learn how to deal with workbooks containing multiple spreadsheets.

Write Excel Files using writexl

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Write data to Excel quickly using writexl. Create files with a single sheet or workbooks with multiple sheets.

Create Customized Excel Files using openxlsx

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Read and write Excel files using openxlsx. Define the formatting of the columns, and create custom styling for the sheets.

R for Excel Users

This course is designed for those who have little to no prior programming experience in R or any other programming language. Its aim is to build up from the basics and teach you the capabilities of R. By the end of this course you will have acquired a valuable new skill for exploring datasets and creating impressive visualizations.

course-r-introduction#02-data-frames-tibbles

Tidy Data Transformation Level 1

Use basic dplyr verbs to filter rows, select columns and sort/arrange datasets in combination with the pipe <code>%>%</code> operator.

Data Transformation with dplyr

GGPlot Level 1

Understand plotting types and the core principles of the grammar of graphics. Create your first visualizations with the ggplot2 package including scatter plots, line graphs and bar charts.

Data Visualization with ggplot2

Introduction to R

Learn how all pieces of the tidymodels framework fit together and quickly create highly performing machine learning models.

Tidymodels Level 1

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Start your machine learning journey and learn the most fundamental building blocks of the tidymodels framework.

Tidymodels Recipes Level 1

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Learn why data preprocessing is crucial in your machine learning workflow and create your first data transformations with the recipes package.

Data Preprocessing with recipes

Tidymodels Parsnip Level 1

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Learn how to fit machine learning models using the parsnip package including linear regression, decision trees and boosting trees.

Model Fitting with parsnip

Tidymodels  Yardstick Level 1

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Learn how to estimate model quality based on different performance metrics using the yardstick package. 

Model Evaluation and Performance Metrics with yardstick

Tidymodels Rsample Level 1

course-r-machine-learning-tidymodels#05-resampling-with-rsample

Avoid overfitting by using resampling techniques including cross-validation and bootstrap using the rsample package.

Resampling techniques using rsample

Tidymodels Tune Level 1

course-r-machine-learning-tidymodels#06-model-optimization-tune

Optimize your model parameters using the tune package to find models which predict new data well.

Build up Your Data Science Skills

Introduction to Machine Learning

Advanced Data Transformation

R for Excel Users

Introduction to R

Machine Learning with tidymodels

Learn by Doing

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Common Questions

Build up Your Data Science Skills

Introduction to Machine Learning

Advanced Data Transformation

R for Excel Users

Introduction to R

Machine Learning with tidymodels

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