Data science is often associated with fast and dirty data analysis + machine learning solutions that do not follow the software engineering practices. Many regard data science more like a Swiss army tool that combines incompatible data and software components in impromptu ways.

Continue reading “Through the needle’s eye: Data science in production” →

I wrote this blog post when working at Sqreen, a startup that develops Software-as-a-service (SaaS) solutions to protect web applications from cyber attacks. This post summarizes the streaming technology used to analyse the attacks in real time.

Continue reading “Streaming data with Amazon Kinesis” →

IPython notebook became a very popular tool for programming short scripts in Python, interactive computing, sharing code, teaching or even demonstrations. Its advantage is the possibility to combine Python code with graphics, HTML, videos or even interactive JavaScript objects in one notebook. With this functionality it may also serve as a great presentation tool.

Continue reading “Giving presentations with IPython notebook” →

From October 11th to 18th, I will be traveling to Society of Neuroscience conference in New Orleans. If you are also there and want to meet, leave me a comment or send an e-mail.

I just delivered another talk on data visualization in Python:

All materials including exercises can be found at https://python.g-node.org/wiki/dataviz

Python 3 has been around for some time (the most recent stable version is Python 3.2), but till now it was not widely adopted by scientific community. One of the reason was that the basic scientific Python libraries such as NumPy and SciPy were not ported to Python 3. Since this is no longer the case, there is no reasons anymore to resist migration to Python (you can find the pros and cons on the Python website)

In this guide I am going to describe some tips that I learnt while trying to make my scripts compatible with Python 3. There is nothing to be afraid of – the procedures are actually quite easy and very rewarding (it is like a glimpse into the future of Python!).
Continue reading “6 steps to migrate your scientifc scripts to Python 3” →

First official version of SpikeSort was finally released. You can find out more about this spike sorting software from: http://spikesort.org Continue reading “SpikeSort 0.12 released!” →

Google App Engine (GAE) is a great platform for learning web programming and testing out new ideas. It is free and offers great functionality, such as Channel API (basically Websockets). Deployment is as easy as clicking a button (on a Mac) on running a Python script (on Linux). The best of all is that you can program in Python and offer an easy end-user web interface without time consuming installation, dependencies and nerves. Continue reading “Scientific computing with GAE and PiCloud” →

Spike sorting is a common pre-processing step in analysis of single or multi-unit responses. The goal of the procedure is to detect the times at which a single cell generated an action potential based on the extracellular recordings of electric potential close to the cell. Continue reading “New spike sorting library in Python” →

I have just finished teaching at a summer school on Advanced Scientific Programming in Python.

The school was a remarkable success, which I hope most of the participants can agree with. Lets wait for the survey results.

The school featured among other thing a PacMan competition. More information can be found on the school wiki.

The Model of Neural Systems programming course will start on Monday, October 27th. It will be given by Robert Schmidt and me. The first programming assignments are available on the course webpage. See you all on Monday!