Exchange on Best Practices & Pitfalls of Remote Data Science Work - Recap

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In these unique times, we wanted to provide more opportunities for our data community to connect and support each other. 

That led to this special event kindly suggested by @tgb417, who facilitated live exchanges to share experiences, best practices, & pitfalls to better navigate these times.

A quick foreword to the key discussion points by Tom: "Thank you all for joining us.  Please keep the conversation going below, I look forward to continuing this important discussion as we all grow in our skill to work remotely."

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1. Connecting business stakeholders to data teams in an online world: The key is explaining the basics and methodology, for those who may not know to ask. Focusing on starting small with storytelling was suggested to win buy-in from these stakeholders. 

Resources suggested: Dataiku DSS 7 Interactive Statistics and Visualization; The Art of Story Telling in Data Science and how to create data stories?

2. Lessons learned from working remotely:  Working from home can both be a blessing and a curse as you have a more flexible schedule but not hard stops on working. Finding balance is even more difficult with children around, and recognizing the need for regular breaks is important to re-focus. Making sure everyone is on the same page by organizing files and preventing duplicate work, not reinventing the wheel, and ways to work smarter, not harder were part of this lively discussion. 

Resource suggested: Tips & resources for remote work in data science

Special tip mentioned by Tom: “Over communicate, and then communicate a bit more!” from How to Succeed When Everyone Works from Home by Andrew Recinos, President, Tessitura Networks

3. Starting your data journey while being the only person in your company specializing in Data Science:  Asking for resources can be an issue with people who may not understand the resources needed to succeed. A suggestion was made to identify quick wins to demonstrate the value of Data Science. Try speaking the internal stakeholders language so that they better understand how they can set you up for success. 

A resource suggested as an example: Educators R Learners

4. Finding a job in NLP:  Seek organizations who are operationalizing data science, and contact them. If that can’t be done, find a project you are passionate about, do the analysis, and get it out there to find other passionate people. 

Resource suggested: NLP Used for Prediction - Watch on Demand; Dataiku Community Job Board

5. Degree of freedom in terms of sourcing both technology and data and how to prepare your teammates with these elements: Key point from this discussion: It’s better to be guided by principles around what you should be doing and not be the person who is creating those principles on the go, because otherwise the accountability and responsibility sits on you.

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