TheRBootcamp

We teach R

We are Basel-based data scientists who have jointly spent over 30 years doing data science with R for both research and industry. We founded The R Bootcamp out of our passion for R and Data Science. Our Team of like-minded R enthusiasts hosts public and in-house Data Science courses around Switzerland and Germany. Our goal is to help you learn the skills needed to discover and communicate insights from data and join the data revolution.

In association with the Advanced Studies, University of Basel, we continuously offer three standalone, complementary courses. For beginners with no R or other programming experience we recommend to first complete R for Data Science. For detailed course description see Upcoming Bootcamps.

R for Data Science

Learn the basics of R and how to use it to explore, manipulate, and visualize data.

Statistics with R

Learn to derive actionable analytics from data using statistical inference in R.

Machine Learning with R

Learn about the power of Machine Learning and how to employ it with R.

Our courses are composed of short, interactive presentations and longer hands-on practicals, during which you will work through step-by-step exercises designed to make you an active user of R. For every 5 participants there will be 1 instructor to ensure personalized support catering to your specific level and needs. Feel free to explore our open materials of our latest and previous courses. If you have any questions don't hesitate to send us a message. To learn about the latest courses, sign up for our mailing list.

We teach R

We are Basel-based data scientists who have jointly spent over 30 years doing data science with R for both research and industry. We founded The R Bootcamp out of our passion for R and Data Science. Our Team of like-minded R enthusiasts hosts public and in-house Data Science courses in Switzerland and Germany. Our goal is to help you learn the skills needed to discover and communicate insights from data and join the data revolution.

R for Data Science

Learn the basics of R and how to use it to explore, manipulate, and visualize data.

Statistics with R

Learn to derive actionable analytics from data using statistical inference in R.

Machine Learning with R

Learn about the power of Machine Learning and how to employ it with R.

Our courses are composed of short, interactive presentations and longer hands-on practicals, during which you will work through step-by-step exercises designed to make you an active user of R. For every 5 participants there will be 1 instructor to ensure personalized support catering to your specific level and needs. Feel free to explore the open materials of our latest and previous courses. If you have any questions don't hesitate to send us a message. To learn about the latest courses, sign up for our mailing list.

Upcoming Bootcamps

R for Data Science

Hosted by Advanced Studies, University of Basel.
Max. 14 spots. Registration Fees: 990CHF.
→ Course description.
Aug 24/25 | Register Nov 9/10 | Preregister

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

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Statistics with R

Hosted by Advanced Studies, University of Basel.
Max. 14 spots. Registration Fees: 990CHF.
→ Course description.
Sep 7/8 | Register

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?
10:30	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

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Machine Learning with R

Hosted by Advanced Studies, University of Basel.
Max. 14 spots. Registration Fees: 990CHF.
→ Course description.
Oct 5/6 | Preregister

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?
10:30	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

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Past Bootcamps

Bootcamps

[ML2] May 2019
Schedule and Materials

[St2] April 2019
Schedule and Materials

[DS2] February 2019
Schedule and Materials

[ML2] January 2019
Schedule and Materials

[DS2] October 2018
Schedule and Materials

[DS4] July 2018
Schedule and Materials

[DS4] April 2018
Schedule and Materials

A perfect course if you would like to get a good understanding about machine learning and how to apply it in R. The course covers the most important topics regarding machine learning and is very well organized. In the end you will be able to create meaningful machine learning models, to evaluate and to optimize them. The lecturers are enthusiastic about the topic and explained everything in an understandable way. Frederik, Senior Consultant

Many thanks for the excellent course, I really learnt a lot. the documents were super useful and the examples really interesting. In addition you guys were all awesome, super helpful, engaging and the atmosphere was really stimulating.
Bruno, Head of Clinical Development

You really deserve all compliments: for presentation, for teaching, for your help and last but not least and friendly attitude. The course was above my expectations! The material presented was hands-on and up-to-date!
Stefan, Senior Risk Modeller & Researcher

I learned from the basics to really cool stuff like interactive plots. Right after the bootcamp I decided to move fully into R. Really great bootcamp!
Edgar, Associate Professor

Every participant will have found something he or she could relate to, be it statistics, visualizations, presentations or web apps. It certainly made me want to take my computer and get cracking right away!
Johan, Analyst

Learning R at TheRBootcamp has offered me [...] the feeling of staying relevant in the changing finance industry
Irena, Credit Officer

Team

Dr. Dirk Wulff Head instructor	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++. He is an active R developer having authored the packages `cstab`, `memnet`, `mousetrap`.
Dr. Nathaniel Phillips Head instructor	Nathaniel is a data and decision scientist who specialises in machine learning, statistical inference, and R. 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. He has authored the free, online e-book YaRrr! The Pirate’s Guide to R and the R packages `yarrr`, and `FFTrees`.
MSc Markus Steiner Team Basel	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. Michael Schulte-Mecklenbeck Team Bern	Michael is a senior lecturer for methodology at the department of Business Administration, University of Bern. His research focuses on human decision processes, eye-tracking and real world traffic behavior. He is an experienced user of R and organises the BernR meetup.

Dr. Dirk Wulff Head instructor	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++. He is an active R developer having authored the packages `cstab`, `memnet`, `mousetrap`.	Dr. Nathaniel Phillips Head instructor	Nathaniel is a data and decision scientist who specialises in machine learning, statistical inference, and R. 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. He has authored the free, online e-book YaRrr! The Pirate’s Guide to R and the R packages `yarrr`, and `FFTrees`.
MSc Markus Steiner Team Basel	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.	Dr. Michael Schulte-Mecklenbeck Team Bern	Michael is a senior lecturer for methodology at the department of Business Administration, University of Bern. His research focuses on human decision processes, eye-tracking and real world traffic behavior. He is an experienced user of R and organises the BernR meetup.

TheRBootcamp Learn Data Science with R

We teach R

We teach R

Upcoming Bootcamps

R for Data Science

Statistics with R

Machine Learning with R

Past Bootcamps

Bootcamps

Team

Contact Us

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Join our mailing list.