Datalicious Notebookmania – My favorite 7 IPython Notebooks

One of the most remarkable features of this year’s Strataconf was the almost universal use of IPython notebooks in presentations and tutorials. This framework not only allows the speakers to demonstrate each step in the data science approach but also gives the audience an opportunity to do the same – either during the session or afterwards.

Here’s a list of my favorite IPython notebooks on machine learning and data science. You can always find a lot more on this webpage. Furthermore, there’s also the great notebookviewer platform that can render Github’bed notebooks as they would appear in your browser. All the following notebooks can be downloaded or cloned from the GitHub page to work on your own computer or you can view (but not edit) them with nbviewer.

So, if you want to learn about predictions, modeling and large-scale data analysis, the following resources should give you a fantastic deep dive into these topics:

1) Mining the Social Web by Matthew A. Russell

miningIf you want to learn how to automatically extract information from Twitter streams, Facebook fanpages, Google+ posts, Github accounts and many more information sources, this is the best resource to start. It started out as the code repository for Matthew’s O’Reilly published book, but since the 2nd edition has become an active learning community. The code comes with a complete setup for a virtual machine (Vagrant based) which saves you a lot of configuring and version-checking Python packages. Highly recommended!

2) Probabilistic Programming and Bayesian Methods for Hackers by Cameron Davidson-Pilon

bayesianThis is another heavy weight among my IPython notebook repositories. Here, Cameron teaches you Bayesian data analysis from your first calculation of posteriors to a real-time analysis of GitHub repositories forks. Probabilistic programming is one of the hottest topics in the data science community right now – Beau Cronin gave a mind-blowing talk at this year’s Strata Conference (here’s the speaker deck) – so if you want to join the Bayesian gang and learn probabilistic programming systems such as PyMC, this is your notebook.

3) Parallel Machine Learning Tutorial by Olivier Grisel

bigdata_alchemyThe tutorial session on parallel machine learning and the Python package scikit-learn by Olivier Grisel was one of my highlights at Strata 2014. In this notebook, Olivier explains how to set up and tune machine learning projects such as predictive modeling with the famous Titanic data-set on Kaggle. Modeling has far too long been a secret science – some kind of Statistical Alchemy, see the talk I gave at Siemens on this topic – and the time has come to democratize the methods and approaches that are behind many modern technologies from behavioral targeting to movie recommendations. After the introduction, Olivier also explains how to use parallel processing for machine learning projects on really large data-sets.

4) 538 Election Forecasting Model by Skipper Seabold

538_reverseengineeredEver wondered how Nate Silver calculated his 2012 presidential election forecasts? Don’t look any further. This notebook is reverse engineering Nate’s approach as he described it on his blog and in various interviews. The notebook comes with the actual polling data, so you can “do the Nate Silver” on your own laptop. I am currently working on transforming this model to work with German elections – so if you have any ideas on how to improve or complete the approach, I’d love to hear from you in the comments section.

5) Six Degrees of Kevin Bacon by Brian Kent

graphlab_sixdegreesThis notebook is one of the showcases for the new GraphLab Python package demonstrated at Strata Conference 2014. The GraphLab library allows very fast access to large data structures with a special data frame format called the SFrame. This notebook works on the Freebase movie database to find out whether the Kevin Bacon number really holds true or whether there are other actors that are more central in the movie universe. The GraphLab package is currently in public beta.

6) Get Close to Your Data with Python and JavaScript by Brian Granger

plotlyThe days of holecount and 1000+ pages of statistical tables are finally history. Today, data science and data visualization go together like Bayesian priors and posteriors. One of the hippest and most powerful technologies in modern browser-based visualization is the d3.js framework. If you want to learn about the current state-of-the-art in combining the beauty of d3.js with the ease and convenience of IPython, Brian’s Strata talk is the perfect introduction to this topic.

7) Regex Golf by Peter Norvig

I found the final notebook through the above mentioned talk. Peter Norvig is not only the master mind behind the Google economy, teacher of a wonderful introduction to Python programming at Udacity and author of many scientific papers on applied statistics and modeling, but he also seems to be the true nerd. Who else would take a xkcd comic strip by the word and work out the regular expression matching patterns that provide a solution to the problem posed in the comic strip. I promise that your life will never be the same after you went through this notebook – you’ll start to see programming problems in almost every Internet meme from now on. Let me know, when you found some interesting solutions!

“Zur Sozialdynamik bewegter Körper”

Statistics is often regarded as the mathematics of gambling, and it has some roots in theorizing about games, indeed. But it was the steam engine that really made statistics do something: Thermodynamics, the physics of heat, energy, and gases. Aggregating over huge masses of particles – not observable on an individual level – by means of probability distribution was the paradigm of 19th century science. And this metaphor also was successfully adopted to describing not only masses of molecules, but also masses of people in a mass society.

Particle or Person? This could be someone walking down a street, seeing her friend on the other side, waving her, and then just walking on. Of course it could also be my drawing of a neutron beta-decaying to a proton.
Particle or Person? This could be someone walking down a street, seeing her friend on the other side, waving her, and then just walking on. Of course it could also be my drawing of a neutron beta-decaying to a proton.
For physics, at the end of the 19th century it had become clear, that models reduced on aggregates and distributions where not able to explain many observations that where experimentally proven, like black body radiation or the photo-electric effect. It was Max Planck and Albert Einstein that moved the perspective from statistical aggregates to something that had not been usually taken into consideration: the particle. Quantum physics is the description of physical phenomena on the most granular level possible. By changing focus from the indistinct mass to the individual particle, also the macroscopic level of physics started to make sense again, combining probabilistic concepts like entropy with the behavior of the single particle that we might visualize in a Feynman-diagram.

Special relativity or rather psychohistory?
Special relativity or rather psychohistory?
The Web presented for the first time a tool to collect data describing (nearly) everyone on the individual level. The best data came not from intentional research but from cookie-tracking, done to optimize advertising effectiveness. Social Media brought us the next level: semantic data, people talking about their lives, their preferences, their actions and feelings. And people connected with each other, the social graph showed who was talking to whom and about which topics – and how tight social bonds were knit.

We now have the data to model behavior without the need of aggregating. The role of statistics for the humanities changes – like it has done in physics 150 years ago. Statistics is now the tool to deal with distributions as phenomena as such rather than just generalizing from small samples to an unknown population. ‘Data humanity’ would be a much better term for what is usually called ‘data science’ – this I had written after O’Reilly’s Strata conference last year. But I think I might have been wrong as we move from social science to computational social science.

Social research is moving from humanities to science.

Further reading:

“Our Pythagorean World”

Crowdsourcing Science

Open foresight is a great way to look into future developments. Open data is the foundation to do this comprehensively and in a transparent way. As with most big data projects, the difficult part in open foresight is to collect the data and wrangle it to a form that can actually be processed. While in classic social research you’d have experimental measurements or field notes in a well defined format, dealing with open data is always a pain: not only is there no standard – the meaningful numbers might be found anywhere in your source and be called arbitrarily; also the context is not given by some structure that you’d have imposed into your data in advanced (as we used to do it in our hypothesis-driven set-ups).

In the last decade, crowdsourcing has proven to be a remedy to dealing with all kinds of challenges that are still to complex to be fully automatized, but which are not too hard to be worked out by humans. A nice example is featuring many “citizen science projects”, from finding exoplanets or classifying galaxies, to helping to model global climate history by entering historic ships’ log data.

Climate change caused by humanity might be the best defended hypothesis in science; no other theory had do be defended against more money and effort to disprove it (except perhaps evolution, which has do fight a similar battle about ideology). But apart from the description, how climate will change and how that will effect local weather conditions, we might still be rather little aware of the consequences of different scenarios. But aside from the effect of climate-driven economic change on people’s lives, the change of economy itself cannot be ignored when studying climate and understand possible feedback loops that might or might not lead into local or global catastrophe. is an open data / open source project aiming at the economic impact of climate change. Collecting data is crowdsourced – everyone can contribute key indicators of geo-economic dependency like interregional and domestic flow of supply and demand in an easy “Wikipedia-like” way. And like Wikipedia, the validation is done by crowd-crosscheck of registered users. Once data is there, it can be fed into simulations. The team behind Zeean, lead by Anders Levermann at Potsdam Institute for Climate Impact Research is directly tied into the Intergovernmental Panel on Climate Change IPCC, leading research on climate change for the UN and thus being one of the most prominent scientific organizations in this field.

A first quick glance on the flows of supply shows how a conflict in the Ukraine effect the rest of the world economically.
A first quick glance on the flows of supply shows how a conflict in the Ukraine effect the rest of the world economically.
The results are of course not limited to climate. If markets default for other reasons, the effect on other regions can be modeled in the same way.
So I am looking forward to the data itself being made public (by then brought into a meaningful structure), we could start calculating our own models and predictions, using the powerful open source tools that have been made available during the last years.