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Data Analysis with R

6 Topics • 198 Exercises • 3 Quizzes

Get first hands-on experience with the statistical environment R covering all aspects of the data analysis process: data import, transformation, modelling, basic reporting and data export.

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In this course...

  • 6 Topics
  • 198 Exercises
  • 3 Quizzes
  • Wide variety of topics
  • Unlock free course materials
  • Free quizzes and exercises
    • Key facts

    • Create insightful data visualizations with the ggplot2 package.
    • Tidy-up your data and create powerful dplyr data transformation pipelines.
    • Communicate your finding through RMarkdown reports in various formats including HTML, PDF, Word and Powerpoint.

    Chapters

    6 Topics • 198 Exercises • 3 Quizzes

    Introduction

    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 %>% operator.

    Tidy

    Tidy your data with more complex transformations and apply various join operators.

    Installation and First Steps

    See how to install R and RStudio on your local machine. Configure your development environment to increase your speed and productivity.

    Model

    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

    Communicate your results through RMarkdown reports and create different outputs based on the same report in PDF, Word, HTML and even Powerpoint.

    Case Studies

    Team

    Apply your data science skills on real-world case studies including Austrian vote data, crypto currencies, IMDB ratings and bank marketing data.

    The R Environment

    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 character, numeric and 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.

    Data Input/Output with Different Sources

    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.

    Data Wrangling with base R and dplyr

    See how data can be easily filtered, merged and corrected using basic R functions. Use dplyr and the %>% pipe operator to easily chain verbs like filter(), arrange(), select(), mutate() and summarise() together. Introduction to dbplyr to generate SQL queries using dplyr syntax.

    Plots with base R, ggplot2 and plotly

    Create basic plots for explorative data analysis using commands like plot(), hist(), 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.

    Statistical Modeling with R

    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.

    Creating Sophisticated Reports with RMarkdown

    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.

    Quantargo Certificate™

    The Quantargo certificate in “Data Analysis with R” demonstrates that your are literate in the topics covered by this course. You have...

  • In-depth knowledge on important concepts
  • Hands-on experience with the tools covered
  • A good starting point for further topics
  • Quantargo CourseCockpit™

    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.