For details on this working group, please see the py4science community site!

What are the origins and consequences of a networked society? Networks are a growing field within many disciplines, including sociology, economics, computer science, and physics. This group will take an interdisciplinary approach for exploring these and related questions, and will familiarize itself with the latest tools being developed to study networks.

During the first class, participants will design a reading list of networks research based on their experience and interests. Each week, one or two readings will be discussed, led by a member of the group. In addition to discussing networks-based research, participants will also learn how to use a networks analysis software package, such as Gephi, by completing a short problem set each week. Lastly, over the course of the semester, each participant will also present her/his own networks-related research. The goal is that we will increase our understanding of networks research by becoming more familiar with the approaches taken by one another's disciplines.

Interested students should REGISTER and can learn more about the working group by attending a preliminary meeting on August 28th at 1pm in D-Lab. While attending this preliminary meeting is not mandatory for participation, if you are unable to make it, please register and get in touch with the working group's coordinator, Carl Nadler (cnadler@econ.berkeley.edu), since a poll will be taken at this meeting to determine when future meetings will occur. Questions about the working group can also be addressed to Carl. Any researcher working on networks-related research is welcome to attend.

Our goal is to work together as a group to learn how to make digital maps. We will start with very simple things (like putting pins into a Google map) and get increasingly sophisticated. One thing that would be great for us to be able to do is, say, replicate and improve the US census map by racial category (see coverage here and here). Being able to reproduce this will teach us all a lot (scalable computing, UI design, working with census data -- not to mention navigating the potentially charged world of racial diversity, etc.). However, we can map kitten photos on Flickr too.

Raymond Yee will not be the only instructor. The goal is for all of us to contribute, and the degree to which everyone contributes will determine what happens. I welcome people with a wide range of perspectives but want everyone to contribute to their ability for us to build together. I will be biased towards wanting to build with Python + JavaScript + open source data and tools --- though I think Google maps/ Google Earth are things I like even when they aren't open source.  I want for us to think about how to build for mobile too.

This is a peer-run working group in scientific computing using the programming language Python. We'll plan to cover topics in efficient numerical and statistical computing, as well as data visualization. A standard reference is Wes McKinney's Python for Data Analysis. A hard copy is available in D-Lab, and the book is freely available via the Berkeley campus Safari Bookshelf (the link only works on campus or via a library proxy). Registration is welcomed, but it's not required. Feel free to come if you're not certain that this group is for you. Bob Bell is coordinating.

We keep track of our meetings at python.berkeley.edu/py4science/py4data.html.

Our goal is to work together as a group to learn how to make digital maps. We will start with very simple things (like putting pins into a Google map) and get increasingly sophisticated. One thing that would be great for us to be able to do is, say, replicate and improve the US census map by racial category (see coverage here and here). Being able to reproduce this will teach us all a lot (scalable computing, UI design, working with census data -- not to mention navigating the potentially charged world of racial diversity, etc.). However, we can map kitten photos on Flickr too.

Raymond Yee will not be the only instructor. The goal is for all of us to contribute, and the degree to which everyone contributes will determine what happens. I welcome people with a wide range of perspectives but want everyone to contribute to their ability for us to build together. I will be biased towards wanting to build with Python + JavaScript + open source data and tools --- though I think Google maps/ Google Earth are things I like even when they aren't open source.  I want for us to think about how to build for mobile too.

This is a peer-run working group in scientific computing using the programming language Python. We'll plan to cover topics in efficient numerical and statistical computing, as well as data visualization. A standard reference is Wes McKinney's Python for Data Analysis. A hard copy is available in D-Lab, and the book is freely available via the Berkeley campus Safari Bookshelf (the link only works on campus or via a library proxy). Registration is welcomed, but it's not required. Feel free to come if you're not certain that this group is for you. Bob Bell is coordinating.

We keep track of our meetings at python.berkeley.edu/py4science/py4data.html.

This is a peer-run working group in scientific computing using the programming language Python. We'll plan to cover topics in efficient numerical and statistical computing, as well as data visualization. A standard reference is Wes McKinney's Python for Data Analysis. A hard copy is available in D-Lab, and the book is freely available via the Berkeley campus Safari Bookshelf (the link only works on campus or via a library proxy). Registration is welcomed, but it's not required. Feel free to come if you're not certain that this group is for you. Bob Bell is coordinating.

We keep track of our meetings at python.berkeley.edu/py4science/py4data.html.

Our goal is to work together as a group to learn how to make digital maps. We will start with very simple things (like putting pins into a Google map) and get increasingly sophisticated. One thing that would be great for us to be able to do is, say, replicate and improve the US census map by racial category (see coverage here and here). Being able to reproduce this will teach us all a lot (scalable computing, UI design, working with census data -- not to mention navigating the potentially charged world of racial diversity, etc.). However, we can map kitten photos on Flickr too.

Raymond Yee will not be the only instructor. The goal is for all of us to contribute, and the degree to which everyone contributes will determine what happens. I welcome people with a wide range of perspectives but want everyone to contribute to their ability for us to build together. I will be biased towards wanting to build with Python + JavaScript + open source data and tools --- though I think Google maps/ Google Earth are things I like even when they aren't open source.  I want for us to think about how to build for mobile too.

Our goal is to work together as a group to learn how to make digital maps. We will start with very simple things (like putting pins into a Google map) and get increasingly sophisticated. One thing that would be great for us to be able to do is, say, replicate and improve the US census map by racial category (see coverage here and here). Being able to reproduce this will teach us all a lot (scalable computing, UI design, working with census data -- not to mention navigating the potentially charged world of racial diversity, etc.). However, we can map kitten photos on Flickr too.

Raymond Yee will not be the only instructor. The goal is for all of us to contribute, and the degree to which everyone contributes will determine what happens. I welcome people with a wide range of perspectives but want everyone to contribute to their ability for us to build together. I will be biased towards wanting to build with Python + JavaScript + open source data and tools --- though I think Google maps/ Google Earth are things I like even when they aren't open source.  I want for us to think about how to build for mobile too.