We’ll have 5 speakers at this event, covering various aspects of the Python Data Science ecosystem (NumPy, SciPy, pandas, matplotlib, scikit-learn, NetworkX, NLTK, IPython notebook, etc.)
Here’s more information on the talks scheduled for this meetup:
1. Data Munging with Pandas – John Fries, CTO, OpenMail
Data munging skills are the keys to the data analysis kingdom. In this talk we’ll go over fundamental data munging techniques using the Pandas library and present some data munging brain teasers.
Speaker bio: MIT Bachelor’s in Mathematics, Software engineer at Google for 6 years where his major projects included Picasaweb, Google News, & YouTube.
2. Using the iPython Notebook for data analysis – John Lin, Data Scientist, TrueCar
This talk will illustrate how to use the iPython Notebook, while comparing and contrasting this tool with “regular” Python software development. Also included will be the advantages of using iPython Notebook for data analysis and pitfalls to avoid.
Speaker bio: John Lin received his economics training at the University of Michigan and Caltech, and has worked as a programmer. He now combines both loves as a Data Scientist at TrueCar, working to improve the car shopping experience and making it awesome.
3. Interactive data exploration and visualization in IPython – Tamara Knutsen, Front End Engineer, OpenMail
Visualization allows for insightful exploration of data. I will present the different visualization and plotting Python libraries for interactive data exploration in IPython notebook, covering Matplotlib, Seaborn, mpld3, Bokeh, VisPy and more.
Speaker bio: Former computational neuroscientist at Caltech, now programming front end and analytics at OpenMail.
4. Multiprocessing in Python – Rudy Gilmore, Data Scientist, TrueCar
This talk is an introduction to running your data analysis code in parallel in Python. I will discuss some of the options for running code on multiple processors, while showing a few simple examples.
Speaker bio: Former research physicist, now working in data science.
5. Machine learning with scikit-learn – Eduardo Ariño de la Rubia, Ingram Content Group
Hear an introduction to the machine learning possibilities in Python using the fantastic scikit-learn ecosystem. I will discuss the basics of individual models available, including examples, while incorporating higher-level constructs such as pipelines.
Speaker bio: Long-time programmer who is especially enamored with bio-inspired soft computing and machine learning methods.
Date: November 3, 2014 (Monday)
– 6:30pm food/bev & networking
– 7:30pm talks starts promptly
You must have a confirmed RSVP and please arrive by 6:55pm the latest. Please RSVP for this meetup here on Eventbrite, as space is limited due to the capacity of the room. Make sure to RSVP now!
Venue: Venice Arts, 1702 Lincoln Blvd, Venice, CA 90291