Further Resources

Beyond the course you may be interested in the following resources and groups.

NIH groups

To support the integration of data science with the biological sciences the NIH Office of the Associate Director for Data Science has been created. The two initiatives that are useful for us:

List of special interest groups and listservs relevent to data science
This is a collated list of special interest groups and listservs relevent to machine learning and programming.
Training events
Regular training events are organized.

Local groups

DCPython is the local python user group. Three main types of event are held. A monthly talk, a monthly ‘Project Night’, and a weekly learning python event. Jonathan currently organizes the Project night event. It is an opportunity to informally meet and discuss/work on any projects you are interested in.
Django District
Django district is another local python user group. It is different to DCPython in that it focuses on using python on the web. Despite the name all aspects and frameworks are discussed, not just the django framework. A monthly talk event is held and they also attend the Project Night event with DCPython.
Data Community DC
Data community DC is an umbrella organization for several local meetup events and initiatives focused on data science. They maintain a very useful calendar and also have a monthly newsletter


Idiomatic Python
This book is a guide to what it means to write ‘pythonic’ code, using a style that not only works but is easily understood by the majority of other programmers.
Fluent Python
Fluent Python explores the less used areas of python. This is a nice complement to Idiomatic Python and is probably most useful if you are already familiar with another language and have been relying on the concepts from that other language in python.
From Python to Numpy
A guide to optimizing your code to rely more on the optimized functionality in numpy and less on loops in python.
Python for Data Analysis
A useful guide on manipulating, processing, cleaning and analyzing data in python. There will be a new edition in 2017.


Python Weekly
Python Weekly is a weekly newsletter curated by Rahul Chaudhary highlighting interesting new posts, events, and packages related to python.
O’Reilly Data Science newsletter
O’Reilly provides this data science newsletter with weekly highlights on the techniques, market, and busniess of data science.
Many interesting new developments in machine learning are seen first in preprint form on the arXiv e-print service.
arxiv sanity
arxiv sanity aims to ease the process of identifying relevant articles on arxiv. The service was created and is run by Andrej Karpathy.
Winner interviews from kaggle
Kaggle is a hosts competitions in machine learning. Datasets and prizes are donated by companies looking for improvements on their current approaches. The winners are frequently interviewed and release their code. The competitions can be somewhat artificial as the datasets have been carefully designed but the solutions are often very interesting.
Tips for reading code and Code is not literature
Reading code that others have written can help us to improve our our coding. These two articles offer practical advice on the most productive approach to take.