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Package 2

Puzzles
Resources

Mathematical problem solving:

What do you need? Patience, curiosity, optimism, and techniques, to name a few. You gain some of these skills by solving problems that are quite doable, but even more by trying to solve problems, that are actually a bit too hard for you.

Solving a problem different to anything you have seen before is like a first ascent of a mountain - a genuine exploration, with greater risks, challenges and satisfaction. Taking wrong turns and climbing backwards to try another route. And again. And again...

Eventually you may give up solving it on your own, but you can still convert your efforts into valuable experience. Read the solutions. Does that mean you actually understand them? Usually not. Try to solve it on your own again a week later... Read, wait, solve, repeat until you can.

George Polya's classic book "How to solve it"Link opens in a new window 

Summary of Polya's techniquesLink opens in a new window

Terence Tao's blog on "Solving mathematical problems"Link opens in a new window

NRICH Thinking MathematicallyLink opens in a new window

NRICH Maths at Home for 16-18Link opens in a new window

YouTube puzzle channelLink opens in a new window

Mathematics and the pandemic:

With the start of the Covid-19 pandemic, mathematics has entered media and politics. It was unusual for Germany to have a chancellor with a PhD in quantum chemistry, but even more unexpected Angela MerkelLink opens in a new window would go on explaining the definition and role of R0Link opens in a new window in interviews with journalists about the rationale for the lockdown. Christophe Fraser's team at the Big Data InstituteLink opens in a new window has been developing a system of digital contact tracingLink opens in a new window in conjunction with testing to control R0 while opening up the country.

Plus is an online magazineLink opens in a new window about the beauty and the practical applications of mathematics and currently has a lot on Covid-19Link opens in a new window (fighting the pandemicLink opens in a new window, R0Link opens in a new window, social distancingLink opens in a new window, communication)Link opens in a new window

Simulations of pandemic and effect of countermeasuresLink opens in a new window (3Blue1Brown)

Thinking:

What types of thinking underly problem solving? There is logical thinking as in the Green-eyed Prisoners PuzzlesLink opens in a new window. There is combinatorial thinking in the this Passcode riddleLink opens in a new window. A few examples for the so-called out-of-the-box thinking are discussed in this video about the Psychology of Problem-SolvingLink opens in a new window. And there are the enemies of thinking, as illustrated for example in the video How to stay calm under pressureLink opens in a new window.

Wason's selection taskLink opens in a new window and How Warwick students and staff solved itLink opens in a new window

BBC's "Great British Intelligence Test"Link opens in a new window

Folding:

If you could not get hold of yarn for the mathematical knitting projects linked to in the last package, here is your second chance to use your hands for doing and understanding mathematics. All you need is paper and a flat hard horizontal surface (e.g. a table)!

The Mathematics, Laws and Theory behind Crease PatternsLink opens in a new window

Mathematics and Origami by Andrew Kei Fong LamLink opens in a new window

Data-driven research in ecology

Mathematicians, statisticians and data scientists are increasingly needed for quantitative research in ecology. Conservation and evolution of species and ecosystems under changing environments are crucial questions in today's world. For decision making industrial countries, trade-offs have to be found between ecological and economical objectives, and they need to be based on both theory and empirical observations. The availability of data is impressive due to shared data from scientific, repositories of volunteers observations (aka citizen scientists) and the increasing use of remote sensing technologies in the field.

You can start exploring such yourself using publicly accessible datasets. Here are some repositories:

Before such data can be analysed, you need to do a lot of detective work. What are the meanings of the columns and rows in the data files? Try to find information on the site where you got the data from and search for suitable website for more context about the domain to get ideas of how the data should be interpreted. Are the data complete or are entries missing? Are they missing at random or are there patterns (e.g. related to certain times or locations)? Once you have understood what the data entries actually mean, you can think of good visualisations and summaries of the information contained in the raw data sets. Why not acquiring some basic R skills using the links above and trying your hand on some of the datasets? See Package 1Link opens in a new window for resources on how to learn R. (If you know other languages, e.g. Python, you may use this as well.)