The very first iteration of what eventually became DataCamp.com. Created by Jonathan Cornelissen and Martijn Theuwissen.

1. 7/12/2012 – TIBPC – Jonathan Cornelissen & Martijn Theuwissen
An interactive e-learning platform for
statistics and data analysis

3. Rcademy provides value for 2 groups
• Interactive courses:
immediate feedback
• Every student learns at own
pace
• Social interaction
• Certificates/Job opportunities
• Tools to build interactive
courses
• Efficiency:
Easy evaluation of students
• Reputation
• Visibility
• Ideology
Students Teachers

4. Big data has CAGR of 58%
=> Expected shortage of
190.000 data analysts
1.5 million managers
by 2018:
(McKinsey & Co)
At the center of three markets with large potential
Data
Analytics
Online
learning
R
Often used, fastest growing
statistical software:
• > 2 million users
• Growing 40-60% yearly
• University teaching
Education being disrupted like every
other information-centric industry:
• 50% of courses in US delivered
online by 2019
(Clayton Christensen)

5. Rcademy situated at the crossroad of interactive and
statistical education
Interactive learning platforms (Online) statistical educationRcademy
• www.codecademy.com
• www.codeschool.com
• www.programr.com
• www.khanacademy.org/cs
• www.codeavengers.com
• www.teamtreehouse.com
• www.coursera.com
• www.statistics.com
• www.statisticalhorizons.com
• www.revolutionanalytics.com/services
/training
• …
• Above platforms: very successful
• Not offering R or any other statistical
learning experience.
• Passive content to learn statistics
• None of them real interactive learning

6. Market distribution strategy in three phases
Phase 1: Convince content creators
1. Well-know statistics professors
• Large student audiences & open
minded towards new forms of
education
e.g. those having video lectures on
coursera,…
Target group
• Efficiency
• Ideology
• Visibility
• Reputation
Incentives
2. R book authors • Boost book sales
3. R package authors • Increase package usage
4. Companies
who develop prioprietary R solutions
• Grow user-base and make
publicity

7. Market distribution strategy in three phases
Phase 2: Course creation fires off distribution channels
1. Statistics professors
2. Book Authors
3. R package authors
4. Companies
5. All content public
Students both online and offline
Readers of next book editions
Promote to all existing package users
Client network
Anyone who uses Google
Phase 3: Viral loop creation among students through
• Gamification
• Social media integration
• Certification

8. Monetization strategy
1. Training and selection services:
Private courses (for training, recruiting,..)
As long as your course is public, use of the platform is free
2. Targeted advertising
3. Certificates
4. Students solving company problems

9. A committed team with a passion for education and
technology
Jonathan Cornelissen
• PhD in Statistics, KU Leuven
• Google summer of code for R
• Ruby on Rails web development courses in Chicago
Martijn Theuwissen
• Business Engineer, KU Leuven
• Educational representative at student and university level
• Leadership award for promoting entrepreneurship at the
Washington Center
• Previously started non-profit education start-up Sagio.be
• Winner “ING Start Academy”
• Winner “UNIZO Prijs Ondernemingszin”
• Will start working full-time on platform and distribution from January
• Talks with KU Leuven about spin-off/financing options

10. Q&AMore information? Contact us at info@Rcademy.org

11. Back-up slides:

12. Distribution Channel : Professors
Acquisition Cost Per User Distribution Channel - Distribution Channel Breakout
Distribution Channel : R-Book Authors
Distribution Channel : Companies Distribution Channel : R-Package Authors

13. Rcademy IT architecture: Two main ingredients:
R Server
- Hosted on Amazon EC2
- Based on Opencpu.org project
open-source by nature
Ruby on Rails web application
- Hosted on Heroku
- Written by Rcademy and proriatery Client web browser
- Rcademy can be accessed
over the internet by any
computer, laptop or Ipad.

14. Rcademy student user interface: schematic overview of the 4 main ingredients:
Area where graphs are shown:
Visualisation of data is a key element in
learning statistical concepts. Graphs can be
rendered by the actions of students in the
upper right panel. Alternatively, course
creators can specify which graphs should be
loaded when starting the exercise and make
the graph part of the exercise.
Exercise description:
In this area, the challenge for a student is
described. Course creators can also use this
area to explain students statistical concepts.
Furthermore, the description can contain
explanation on best coding practices, or a
description of the dataset used for the
exercise.
Solving the exercise:
Area for students to type code that solves the
exercise described on the right. Moreover,
course creators can specify that code chunks
are preloaded upon starting the exercise to
help students and fasten the learning
experience. The latter has been shown to be a
very effective way to learn students how to
program.
Giving feedback to students:
Area where feedback is given to students on
the correctness of their solution and where the
output of their commands is shown. Additional
hints can be provided if students make
mistakes.

15. Distribution importance in most likely scenario