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A class in the digital tool shed at the library
Data Science at The Claremont Colleges Library

The Claremont Colleges Library offers support for data science work across disciplines at all seven colleges.

This includes help finding existing datasets, research data management, (including writing data management plans for funding applications), resources for training and education, and a place to make connections with collaborators.

For more information, contact Jeanine Finn, Data Science and Digital Scholarship Librarian (jeanine.finn@claremont.edu)

 

 

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Data Science for Good

Pomona mathematics professor Jo Hardin has spent the summer collaborating with data science colleagues Hunter Glanz (Cal Poly, San Luis Obispo) and Nick Horton (Amherst) on an ambitious post-a-day-project building the Teach Data Science blog.

The blog is filled with useful resources and reflections on teaching data science, designed to ease the learning curve for faculty engaging in this interdisciplinary space.

Jo’s recent post on Data Science for Good  compiles some excellent resources for educators trying to incorporate a more critical approach to data science topics, with links to a number of groups and projects looking at the relationship between big data projects and social justice outcomes.

 

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A Hands-on Workshop Series in Machine Learning (Fall 2019)

Who: All 7C students & faculty

When: 5:45 to 7:45 pm on 7 consecutive Wednesdays from Oct 2nd to Nov 13th

Where: Aviation Room, Hoch-Shanahan Dining Commons, HMC

Why: To learn machine learning techniques and related tools in Python!

More information:

The workshop series is designed with a focus on the practical aspects of machine learning using real-world datasets using the tools in the Python ecosystem. It is targeted towards complete beginners familiar with Python but is also designed adaptively so that you will be challenged even if you have some familiarity with machine learning tools. 

You will learn the minimal but most useful tools for exploring datasets using pandas quickly and then move on to the conventional machine learning algorithms and other related concepts that comes in handy for all models including neural networks. The neural networks will be introduced gently from the fourth session onwards and you will learn some more involved architectures such as Convolution Neural Networks (CNN) and apply them to real-world datasets. The sessions will be a good mix of theory explained intuitively in a simplified manner and hands-on exercises.

If you are interested, please have a look here for more information on the series, including the topics to be covered. Seats are limited, please register using this link. The only prerequisites are python programming and basics of probability and statistics. It is important that you attend all the sessions of the series for it to be useful.