10/14, 16:20–16:50 (Asia/Tokyo), pyconjp_3
言語: English
Ever wondered about how to find bottlenecks in the electricity grid? Patient zero in a outbreak? Or recommending friends on a social network? This talk is for data scientists and other programmers who want to add another tool in their data science toolkit. We will discuss how tools like NetworkX in the Python ecosystem can be used to better understand and analyze network data.
In this talk we will cover the basics of network(graph) theory and network thinking, then we will go over some algorithms used to analyse network data. All throughout the talk we will use NetworkX to implement and discuss network science.
We will roughly cover the following topics during the talk.
Part A: Introduction to Graphs and the NetworkX API
Part B: Graph Algorithms
- Hubs: Which nodes are the important nodes in our data?
- Paths: Where should I jump next to find my destination?
- Structures: Who should I be friends with?
Part C: Linear Algebra and Network Science
- What do matrices have to do with nodes and edges?
Part D: How and when to use NetworkX?
- Some other tools in the ecosystem
- The tradeoffs of the library
By the end of the talk you should be comfortable with working with network data using the PyData ecosystem (NetworkX, pandas, numpy!).
I am currently working on the NetworkX open source project (work funded through a grant from Chan Zuckerberg Initiative!) Also collaborating with folks from the Scientific Python project (Berkeley Institute of Data Science), Anaconda Inc and GESIS, Germany. Before this I used to work on the GESIS notebooks and gesis.mybinder.org.
I am also interested in the development and maintenance of the open source data & science software ecosystem. I try to help around with the Scientific Open Source ecosystem wherever possible. To share my love of Python and Network Science, I have presented workshops at multiple conferences like PyCon US, SciPy US, PyData London and many more!