TheRBootcamp

R Bootcamps

Learn Data Science from experienced R instructors and developers in a learning-friendly environment. We will show you the latest tools and techniques to discover and communicate insights from data, making you ready for the data revolution.

Our courses teach the full spectrum of data science with R in in-depth, hands-on courses for beginners, academics, and industry professionals. We cover statistical analysis and machine learning. We show you how to make elegant, informative data visualizations for data both big and small. We introduce you to some of R's most efficient libraries for data analysis and tools to write transparent, reproducible reports.

Feel free to explore the materials of our LATEST COURSE.

Essentials.

rlogo
Get to know the R and RStudio environment. Load your data into R and do basic manipulations.

Analysis.

ttest
Descriptive and inferential statistics. Applied machine learning and statistical prediction.

Visualisation.

ttest
Publication-ready visualisations for small and big data. Interactive websites with Shiny.

Productivity.

ttest
Speed up your code. Write and share reproducible code and reports using R Markdown.

R Bootcamps

Essentials.

rlogo
Get to know the R and RStudio environment. Load your data into R and do basic manipulations.

Statistics.

ttest
Descriptive and inferential statistics. Applied machine learning and statistical prediction.

Visualisations.

ttest
Publication-ready visualisations for small and big data. Interactive websites with Shiny.

Productivity.

ttest
Speed up your code. Write and share reproducible code and reports using R Markdown.

Upcoming Bootcamps

Applied Machine Learning with R

BASEL. 19/20 January 2019. 2-day course. At Advanced Studies, University of Basel.
Maximum: 15 students. Registration Fees: 1,490CHF.
1 spot left. → Learn more.
Register here!

Basel
source: towardsdatascience.com

Course description		Schedule
Machine learning is one of the most important and disruptive technologies today, in industries ranging from pharma, to insurance, to marketing. In this course, you will learn how to apply machine learning techniques to data using the R statistical language. You will learn the basics of the machine learning process, from acquiring and exploring data, to selecting and implementing machine learning models, to evaluating their performance. We will cover the most common machine learning problems (regression, classification, and clustering), and introduce you to popular algorithms for solving these problems such as regression, decision trees, and deep learning. We will cover applied examples such as medical diagnoses, hiring decisions, demand forecasting and marketing campaigns. Each day will contain a series of short lectures and examples to introduce you to new topics. The bulk of each day will be dedicated to hands-on exercises to help you ‘learn by doing’. Dedicated time will be given for 1:1 feedback. All course materials, tutorials, examples, exercises, and solutions will be available online for you to view at any time during, and after the course. Participants are requested to bring their own laptop with software installation rights. We will provide a limited number of spare laptops for those that cannot bring their own. The course begins with a very brief introduction to the R language to prepare participants for the content of the course. Prior experience with R, or any other programming language, as well as an introductory course in statistics, is helpful but not strictly necessary.			Day 1	Day 2
9:00	Welcome	Recap
9:30	What is ML Examples of machine learning & terminology.	Optimising prediction +Practical How to tweak model settings to optimize performance? How to estimate future prediction accuracy?
10:00	R for ML +Practical Understanding the basics of working with R.



12:00	Lunch	Lunch
	Lunch	Lunch
13:00	Fitting +Practical Understanding the basics of statistical learning aka how to fit a model? How to quantify model performance?	Choosing models +Practical What model classes exist? Mapping models to questions. Assessing costs and benefits.




15:30	Prediction +Practical How to use fitted models to predict new data? How to evaluate prediction performance and compare models?	Choosing features How to balance the number of cases and features? What are the right features?



17:30		Looking forward Advances, ethical challenges, and next steps.
18:00	Wrap-up	Wrap-up & Apero

Introduction to R for Data Science

BASEL. 23/24 February 2019. At Advanced Studies, University of Basel.
Maximum: 20 students. Registration Fees: 1,190CHF.
17 spots left. → Learn more.
Register here.

Basel
source: careeranna.com

Course description		Schedule
Data science is one of the most exciting fields around, and the R statistical language is one of the best ways to learn and practice it. In this packed weekend, you will learn how to do data science using R from the ground-up. You will start by learning the basics of the R language and the RStudio programming environment. You will then cover topics ranging from loading data from external files, to cleaning and organising data, to conducting basic statistical analyses and creating striking visualisations. Each day will contain a series of short lectures and examples to introduce you to new topics. The bulk of each day will be dedicated to hands-on exercises to help you ‘learn by doing’. Dedicated time will be given for 1:1 feedback. All course materials, tutorials, examples, exercises, and solutions will be available online for you to view at any time during, and after the course. Participants are requested to bring their own laptop with software installation rights. We will provide a limited number of spare laptops for those that cannot bring their own. There are no strict knowledge prerequisites for this course. Prior experience with a programming language (e.g.; SAS, R, STATA), as well as an introductory course in statistics, is helpful but not necessary.			Day 1	Day 2
9:00	Welcome	Recap
9:30	Introduction +Tutorial Understanding the basics of R. How to work with R Studio and R Projects?	Analysing +Practical How to calculate descriptive statistics? How to aggregate data?




12:00	Lunch	Lunch
	Lunch	Lunch
13:00	Data +Practical Understanding data objects and functions in R. How to read and write data?	Plotting +Practical How to create plots? (barplot, scatterplot, heatmaps etc.) How to customize plots? (colors, multi-panel, style, etc.)




15:30	Wrangling +Practical How to reformat and join data? How to slice and dice data? (renaming, sorting, filter, etc.)	Case studies Apply and expand what you have learned in a prepared case study or your own data.



17:30		Next steps How to continue your R journey.
18:00	Wrap-up	Wrap-up & Apero

→ Print view

Statistics with R

BASEL. 6/7 April 2019. 2-day course. At Advanced Studies, University of Basel.
Maximum: 15 students. Registration Fees: 1,490CHF.
11 spots left. → Learn more.
Preregister here!

Basel
source: lynda.com

Course description		Schedule
If you want to make robust, actionable data-driven decisions from data, you need to understand statistics. In this intensive bootcamp, you will learn the fundamental principles of statistical analysis and inference using the R statistical language in order to help you translate data into actionable analytics. Whether you are an experienced data analyst or completely new to the field of statistical inference, this course has something for you. You will start from the beginning, learning how to calculate summary statistics to gain immediate insights from data. You will then learn how to create, dissect, and implement regression models, including t-tests, correlation tests, and ANOVAs) to understand hidden relationships in your data. Advanced topics such as hierarchical regression, Bayesian methods, and non-parametric analyses will also be covered. Each day will contain a series of short lectures and examples to introduce you to new topics. The bulk of each day will be dedicated to hands-on exercises to help you ‘learn by doing’. Dedicated time will be given for 1:1 feedback. All course materials, tutorials, examples, exercises, and solutions will be available online for you to view at any time during, and after the course. Participants are requested to bring their own laptop with software installation rights. There are no strict knowledge prerequisites for this course. Prior experience with a programming language (e.g.; SAS, R, STATA), as well as an introductory course in statistics, is helpful but not necessary. The course includes with a brief introduction to the R language to prepare participants for the content of the course. Prior experience with R, or any other programming language, as well as an introductory course in statistics, is helpful but not necessary.			Day 1	Day 2
9:00	Welcome	Recap
9:30	Intro to Statistics Understanding the logic behind statistical inference.	Hierarchical models +Practical Pros and Cons of hierarchical (mixed) models? When and how to use them in R?
10:00	R for Statistics +Practical Understanding the basics of working with R.



12:00	Lunch	Lunch
	Lunch	Lunch
13:00	Regression models +Practical What is regression? How to run and customize regression models?	Robust statistics +Practical Understanding the assumptions of regression models? Are there robust alternatives?




15:30	Regression applications +Practical Implementing ANOVAs, t-tests, correlation tests as regression models. Practical considerations in using regression?	New statistics Statistics beyond p-values. A primer on Bayesian statistics.



17:30		Next steps How to continue the R journey?
18:00	Wrap-up	Wrap-up & Apero

About Us

We Teach R.

We are Dr. Dirk Wulff and Dr. Nathaniel Phillips, two data scientists who have jointly spent over 20 years doing data science with R for both research and industry applications. We founded The R Bootcamp out of our passion for teaching and communicating R. We have taught dozens of courses in R and statistics at the Bachelor’s and PhD level, and have written the free, online e-book YaRrr! The Pirate’s Guide to R. We are active R developers and have written several R packages including cstab, mousetrap, yarrr, and FFTrees.

If you have any questions don't hesitate to send us a message. To stay in touch and learn about the latest courses, sign up for our mailing list.

Team.

Dr. Dirk Wulff	Dirk is a researcher at the University of Basel and the Max Planck Institute for Human Development, Berlin, specialising in decision science and mathematical models of cognition. He is an expert on big data applications in the behavioral sciences using R, Python, and C++.
Dr. Nathaniel Phillips	Nathaniel is a data and decision scientist who specialises in machine learning, statistical inference, and the R programming language. Currently, he is a data scientist at Roche, where he leverages big data, machine learning, and visualisations to further the vision of personalised health care.
MSc Markus Steiner	Markus is a PhD student at the University of Basel. He studies risky decisions and specialises in building computational and statistical models of human cognition, using these to predict and improve real life decisions.

Dr. Dirk Wulff	Dirk is a researcher at the University of Basel and the Max Planck Institute for Human Development, Berlin, specialising in decision science and mathematical models of cognition. He is an expert on big data applications in the behavioral sciences using R, Python, and C++.	Dr. Nathaniel Phillips	Nathaniel is a data and decision scientist who specialises in machine learning, statistical inference, and the R programming language. Currently, he is a data scientist at Roche, where he leverages big data, machine learning, and visualisations to further the vision of personalised health care.
MSc Markus Steiner	Markus is a PhD student at the department for Cognitive and Decision Science, University of Basel. He studies risky decisions and specialises in building computational models of human cognition, using these to predict and improve real life decisions.

TheRBootcamp Learn Data Science with R

R Bootcamps

Essentials.

Analysis.

Visualisation.

Productivity.

R Bootcamps

Essentials.

Statistics.

Visualisations.

Productivity.

Upcoming Bootcamps

Applied Machine Learning with R

Introduction to R for Data Science

Statistics with R

Past Bootcamps

Bootcamps

About Us

We Teach R.

Team.

Contact Us

Send us a message.

Join our mailing list.