Source: http://www.who.int/mediacentre/factsheets/fs310/en/

"The ultimate purpose of data analysis is to convince other people that their beliefs should be altered by the data."

• An umbrella term used to describe datasets that are difficult to analyse with conventional tools.

• To run with Big Data you must learn to walk with small data

• Tiny reproducible examples are the best way to learn

• Use StackOverflow: reproducible examples are needed

http://labs.strava.com/clusterer/#m=gcwf5f9k&z=14&c=&dl=0&dh=400&t=Ride

http://www.bikecitizens.net/30000-kilometres-in-one-image/

If you cannot visualise your data, it is very difficult to understand your data. Conversely, visualisation will greatly aid in communicating your results.

Human beings are remarkably adept at discerning relationships from visual representations. A well-crafted graph can help you make meaningful comparisons among thousands of pieces of information, extracting patterns not easily found through other methods. … Data analysts need to look at their data, and this is one area where R shines. (Kabacoff, 2009, p. 45).

If you want to be data scientist you must program 4 "documentation, sharing and scientific repeatability": https://t.co/FQhZYMODF8 #rstats

— Robin Lovelace (@robinlovelace) January 2, 2016

http://www.r-bloggers.com/why-you-should-learn-r-first-for-data-science/

Source: Cheshire and Lovelace (2014) - available online

Source: This tutorial!

Source: This tutorial!

Source: R-Bloggers

http://www.sciencedirect.com/science/article/pii/S0016718513002480

http://www.sciencedirect.com/science/article/pii/S136984781500039X

http://pct.bike/