Visualising data on health and mortality has a most up-to-date ring to
it, as if it had required the rise of big data and computational tools for
something as intricate as visual health statistics to
“Everybody Lies” is the harsh title of Seth Stephens-Davidowitz’s
new book. While it doesn’t provide any feasible recipe to prevent people
from lying, the book helps the reader in one essential realm: to grasp and
conceptualize the power of data and data science. Its key strength: It does
so in a very engaging and accessible way.
81 years of budget data and various categories in three diagrams – the
United States Fiscal Chart from the 1870 US census atlas is a real
blockbuster in the history of data visualisation. The atlas as a whole is
full of interesting graphics and has a widespread reputation as an early
gem of data visualisation.
Open data in biomedicine is a gold mine that can strengthen innovation
in pharmaceutical R&D. In combination with the right analytics, public
data helps identify therapeutic targets and ligands, enhance clinical
development, and boost portfolio management efficiency. The challenge is to
purposefully integrate abundant and heterogeneous data scattered across
In times where seemingly every second “The Economist” Special Report
focuses on either Artificial Intelligence (AI) or Big Data, general
expectations regarding current technological capabilities are higher than
ever. Rightly so, as there have been so many notable advances in recent
years. What does this mean for trend detection?
Bei vielen Freundschaftsspielen der deutschen Fußballnationalmannschaft
– wie jüngst gegen England – sind in letzter Zeit einige
Sitzplätze frei geblieben. DFB-Teammanager Oliver Bierhoff warnte bereits
vor einer “Übersättigung” des Fußballs. Gibt es generalisierbare
Treiber für die Beliebtheit einer Fußball-Partie? Ein kleines Experiment
– abseits der großen Fußballbühne.
80% of work in data science projects is dedicated to data quality
assessment, data preparation and integration. Applying and tweaking the
algorithms, improving the performance of models (basically all the fun
stuff) covers only 20%. What’s the reason for this?
Despite the recent Volkswagen scandal, German cars still have a
world-class reputation. And Germans still love cars: more than 44 million
vehicles are registered, that’s about one car for every second citizen.
Understandably, the used car market is also large – but, how does one
arrive at a fair value for a used car?
The story immediately went viral: Big Data company Cambridge Analytica
and its sophisticated psychographic models helped Donald Trump to secure
the victory in the 2016 presidential election. The story played to all
prevalent fears in the age of big data: privacy, microtargeting,
behavioural steering. But now – with far less media buzz – the
company admits that it was never really involved in the Trump campaign.
What can we learn from this ‘scam’?