## Local Differential Privacy: Getting Honest Answers on Embarrassing Questions

Do you cheat on your partner? Do you take drugs? Are you gay? Are you an atheist? Did you have an abortion? Will you vote for the right-wing candidate? Not all people feel comfortable answering those kinds of questions in every situation honestly.

So, is there a method to find the respective proportion of people without putting them on the spot? Actually, there is! If you want to learn about randomized response (and how to create flowcharts in R along the way) read on!

## Kalman Filter as a Form of Bayesian Updating

The Kalman filter is a very powerful algorithm to optimally include uncertain information from a dynamically changing system to come up with the best educated guess about the current state of the system. Applications include (car) navigation and stock forecasting. If you want to understand how a Kalman filter works and build a toy example in R, read on!
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## Learning Data Science: A/B Testing in Under One Minute

Especially in the areas of web design and online advertising, everybody is talking about A/B testing. If you quickly want to understand what it is and how you can do it with R, read on!
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## Will AI become conscious any time soon?

We all know the classical Sci-Fi trope of intelligent machines becoming conscious and all the potential ramifications that could follow from there (free will, fighting their human creators, ethical dilemmas, and so forth). Now, is this a realistic scenario? As a researcher in the area of AI (see e.g. So, what is AI really?), with a penchant for philosophy, I share my thoughts here with you, so read on!
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## COVID-19 in the US: Back-of-the-Envelope Calculation of Actual Infections and Future Deaths

One of the biggest problems of the COVID-19 pandemic is that there are no reliable numbers of infections. This fact renders many model projections next to useless.

If you want to get to know a simple method how to roughly estimate the real number of infections and expected deaths in the US, read on!
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## Collider Bias, or Are Hot Babes Dim and Eggheads Ugly?

Correlation and its associated challenges don’t lose their fascination: most people know that correlation doesn’t imply causation, not many people know that the opposite is also true (see: Causation doesnâ€™t imply Correlation either) and some know that correlation can just be random (so-called spurious correlation).

If you want to learn about a paradoxical effect nearly nobody is aware of, where correlation between two uncorrelated random variables is introduced just by sampling, read on!
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## COVID-19: The Case of Germany

It is such a beautiful day outside, lot’s of sunshine, spring at last… and we are now basically all grounded and sitting here, waiting to get sick.

So, why not a post from the new epicentre of the global COVID-19 pandemic, Central Europe, more exactly where I live: Germany?! Indeed, if you want to find out what the numbers tell us how things might develop here, read on!
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Valentine’s Day is around the corner and love is in the air… but, shock horror, nearly every second marriage ends in a divorce! Unfortunately, I can tell you first hand that this is an experience you’d rather not have. In this post, we see how data science, in the form of the `OneR` package and an interesting new data set, might potentially help you to avoid that tragedy… so read on!

## Epidemiology: How contagious is Novel Coronavirus (2019-nCoV)?

A new invisible enemy, only 30kb in size, has emerged and is on a killing spree around the world: 2019-nCoV, the Novel Coronavirus!

It has already killed more people than the SARS pandemic and its outbreak has been declared a Public Health Emergency of International Concern (PHEIC) by the World Health Organization (WHO).

If you want to learn how epidemiologists estimate how contagious a new virus is and how to do it in R read on!
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## Does Australia need More Fires (but the Right Kind)? A Multi-Agent Simulation

We have all watched with great horror the catastrophic fires in Australia. Over many years scientists have been studying simulations to understand the underlying dynamics better. They tell us, that “what Australia needs is more fires, but of the right kind”. What do they mean by that?

One such simulation of fire is based on Multi-Agent Systems (MAS), also called Agent-Based Modelling (ABM). An excellent piece of free software (and in fact the de facto standard) is NetLogo. Even better is that NetLogo can be fully controlled by R… and we will use this feature to learn some crucial lessons!

If you want to understand more about the dynamics of fire in particular and about some fascinating properties of dynamical systems in general via controlling NetLogo with R, read on!
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