I have just joined PyData conference in Florence, and I will list briefly some interesting insights.
Time Travel and Time Series Analysis with Pandas and Statsmodels, @hendorf. The focus of the talk was time series analysis. The speaker pointed out something that a data scientist should not forget when doing such time series analysis. He pointed out that the time level of aggregation is something to do with care when doing such analysis. Do you take into account that February has a number of days that accounts to only 90% of the number of days of March? If you compare e.g. sales per month, you cannot just ignore this fact. In the talk, I found out that statsmodels has some nice tools that perform trend analysis and seasonality analysis.
Machine learning and IoT for automatic presence detection of workers on fall protection life lines, @stefanoterna. The talk was an excellent overview of how TomorrowData is able to deploy machine learning systems in the "real world". Their system uses neural networks to detect a man walking on industrial cables. It was interesting to hear about the different challenges that one has to consider in the Internet of Things area due to hardware and environmental constraints. The fact that they had to manually annotate the signals coming from an accelerometer reminded me of my work about indoor localization. In this kind of areas, the data collection is indeed a challenge due to its manual cost (compared to the datasets you can easily collect through a web app).
Introduzione a Orange Data Mining, @ericbonfadini. Eric introduced Orange Data Mining which is both a python library and a GUI for machine learning projects. I found interesting the nice GUI. It allows to define pipelines of jobs to mine data. You can quickly get insights about data and play around with machine learning models. I see this tool as quite useful mainly for didactic purposes. I think it can be a nice tool for teachers to explain data mining and machine learning in a nice graphical way. It is really suitable for lectures.
Simple APIs and innovative documentation processes: looking back at the success of Scientific Python, @EGouillart. The talk was the point of view of a core developer of a scientific package like scikit-image. The speaker gave nice insights about the API design choices that need to be taken when you contribute to open source projects. For example, what is the advantage of getting rid of most classes in your package and mainly expose functions. The idea is that, if you get rid of the boilerplate of classes, you are forced to expose/return just numpy arrays which you can then easily integrate to other tools in your pipeline, e.g. scikit-learn. Another thing to take into account is that 54% of the users of packages are running a Windows machine (although probably the developers of such package don't). So, you need to take into account the tech gap between the developers and the end users. Finally, the speaker mentioned the power of Sphinx as a documentation tool.
Building Data Pipelines in Python, @marcobonzanini. Luigi is an awesome tool because simply it makes you feel relaxed when you are running a data pipeline. You can programmatically define arbitrary dependencies between tasks, and Luigi will make sure that the dependencies are fulfilled. Marco's talk was a really nice intro to the tool.
Going Functional in the Python Data Science Stack, @data_hope. The speaker explained the directed acyclic graphs that are behind functional programming. It was interesting to hear about Dask package and how you can bring its lazy evaluation model. Dask allows you to abstract your code and perform operations on datasets that do not fit in memory. The speaker pointed out that doing functional programming means to decouple "how" from "what". You can just focus on "what" your algorithm should do, then you just choose "how" it will do it (e.g. Dask).
Reti Neurali in Python, @spiunno. The talk was a great overview of what are neural networks and how you can implement them with Theano and Lasagne. The speaker was able give a talk that was suitable both to beginners and both to an intermediate audience. In particular, the Q&A session was really active, and interesting topics were discussed, e.g. preventing overfitting, computational costs, gravitational waves, etc. Regarding overfitting prevention, I learnt about "dropout" which is a nice technique that consists basically in dropping out links of the networks at random for each sample. The advantage is that you prevent overfitting and reduce the computational cost at the same time.
@hendorf thank you for coming! enjoy your next conference :)— PyCon Italy (@pyconit) April 20, 2016