The document introduces the topic of data science and the programming language R. It defines data science and provides examples of using data science for social good. Additionally, it outlines some of the main features and pros and cons of using R for data science applications and statistical analysis.

1. Introduction to Data Science
with R
Mustafa Wahedi

2. Table of contents
• What is Data Science?
• Examples and further readings
• Hacking skills - R
• R pros
• R cons
• Questions + Workshop

3. What is Data Science?
• Definition (wikipedia):
„Data Science is an interdisciplinary field about processes and
systems to extract knowledge or insights from data in various forms,
either structured or unstructured, which is a continuation of some of
the data analysis fields such as statistics, data mining, and predictive
analytics, (…) .”

4. What is Data Science?
Drew Conway 2010

5. Examples and further readings
• Data Science for Social Good
• Problem: Donations for Kenyan households need to be adressed to
the right villages with the most need first
• Idea: Decipher Satellite images with the help of an algorithm
(supervised learning)

6. Examples and further readings
• R for Data Journalism
• http://rddj.info/
• Used since the 50s/60s – first recorded use by a major news
organization was in 2009 by The Guardian in their Datablog
• Higher level of Immersion with topics as the reader is able to interact
with Data in form of interactive visualization

7. Hacking skills - R
• R is a a programming language and software environment for
statistical computing and graphics supported by the R Foundation.
• Based off S (turn ideas into software, quickly and faithfully)
• Source code of R is written primarily in C, Fortran and R. API to almost
any language
• The idea is to load software packages with already established
functions / methods / algorithms into your environment and to use
those for your needs
• 7,801 additional packages available (as of January 2016)

8. R pros
• R is a fast way for prototyping
• R core has a lot of statistical features to quickly analyse datasets
• R runs in-memory, it calculates fast
• Huge userbase – almost every problem you run into will have been
already discussed
• Visualizing datasets in R is easy, fast and with the right packages also
very intuitive (ggplot2)
• If you need a statistical method, which is very niche, odds are there is
a package available for that

9. R cons
• Steep learning curve, as Rs Syntax has some quirks you got to get used to
• R runs in-memory – big data needs big memory and memory overflow
happens if you are not careful
• Debugging is a mess with R, in part because of lack of documentation and
error messages in R can be cryptic
• When R was founded / created, security questions were not asked, thus
implementing R code on live server needs to be thought about thourougly
• Not a lot of interactivity (e.g. javascript in frontend to show results of R
analysis – but this is also being tackled now by the community (shiny,
rCharts))

10. Questions?
https://de.linkedin.com/in/mustafa-wahedi-3016486b