Familiarity with either python or R is highly recommended, but attendees need not be data scientists necessarily.
Data science teams often struggle with maintaining clean code libraries and sharing code between members to avoid duplicated efforts and rework. In addition, when team members leave an organization, institutional knowledge and coding skill is frequently lost. But this is not inevitable!
Whatever your preferred programming language, internally packaging your best code and using a git system allows you to engage in peer review, share information, and preserve knowledge that your organization would otherwise lose. In this talk, Stephanie Kirmer will discuss best (and worst!) practices when it comes to technical collaboration in data science teams, and explain why building your own collaborative infrastructure is worth the up-front effort.
Some of the elements covered in this talk will include:- What it looks like and why it works when you build libraries and packages for internal use instead of maintaining assorted discrete scripts- Where to start with library/package building, and how create culture and incentives so that your code tools are maintained and supported- The importance of quality documentation and how to create and maintain it- Methods for making contributing and collaborating on packages welcoming and easy for all members of the team, not just the package creator
Your data science team can produce faster, more effective, more sustainable, and more reproducible work using these tools and ideas that we can borrow from the world of software development.
Stephanie Kirmer is a data scientist at Uptake in Chicago, where she focuses on building supervised learning models to predict and diagnose mechanical failures. Prior to joining Uptake, she worked in data analytics in the public sector, including at the University of Chicago Urban Labs. Find her on twitter @data_stephanie.
Metis (thisismetis.com) accelerates careers in data science by providing full-time immersive bootcamps, evening part-time professional development courses, online resources, and corporate programs based in Seattle, New York, Chicago, and San Francisco.
Brought to you by Kaplan, Metis focuses primarily on Python, machine learning, data visualization, deep learning, big data processing, statistical foundations, and more. Students and alumni of the bootcamp program receive continuous support from our career advisors, empowering them to pursue a successful career in the fast-growing field of data science.
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