# Spring Quarter Project Group - Data Science at UCSB
## Week 3: Team Workflows
<img src="https://scontent-sjc2-1.xx.fbcdn.net/v/t31.0-8/17761132_1062381860572360_8797131265453106947_o.jpg?oh=fbb3d6c2baa375ea1408c9e153cc5c8b&oe=59888707" width="600px">
[Carpe Data](https://carpe.io/) Data Talk
+ Time and Date: April 20th 2017
+ Location: Flying A Studio in UCen
[Data Science Club Showcase](https://www.library.ucsb.edu/events-exhibitions/data-science-club-showcase)
+ *Someone* in your team should have a staging site profile already and should be iterating on the project to be ready for this date
+ Time and Date: April 26th 2017 7PM
+ Location: Interdisciplinary Research Collaboratory (Library)
<img src="http://ww1.prweb.com/prfiles/2015/04/28/13075821/HG-Data-Logo-Ocean-1030x225.png" width="600px">
[HG Data](https://www.hgdata.com/) Hackathon confirmation by **this week**
+ Time and Date: April 28th 2017
+ Fill in the sign in sheet [here](https://tinyurl.com/mmd9d3t)
+ [Facebook Link](https://www.facebook.com/events/1312104892216288/)
## **Form Teams**
Talk about what you are looking to accomplish this quarter, for those of you who were already in a team from last quarter use things that you felt needed improvements as learning experiences.
Key take aways:
+ De facto project leader
+ Organize time wisely
+ Have tangible goals every meeting
+ Be concrete in what you wish to accomplish every meeting time
+ Implement **Stand Ups**
## Structure of an effective Stand Up:
+ What did I accomplish last meeting?
+ What will I do today?
+ What obstacles are impeding my progress? (Blockers)
## Good Team Work Ethics
Make note of attendance and make safety nets for days of absence (usually around midterm season)
+ Project Idea
+ Necessary steps needed if data set isn't readily available
+ Do you have to *webscrape*? If so do you prior experience or anyone in your team.
+ Provide link for team members to be able to access the data set.
+ Understand *GitHub* as it relates to *git* workflow, teach it to other teammates
+ *Virtual Environments*, again teach if other teammates
### Post Shit on Slack
Any documentation which you think will help your other teammates get context
+ Relevant articles about the data set
+ Published papers about data set
+ Documentation of *Algorithms* and implementation examples (online *Jupyter Notebooks*, *Kaggle* kernels, *r-blogger* article)
+ Documentation for the modules and packages most useful documentation available.
+ Discuss weaknesses, technologies, expertise, talent
+ Pick **R** or **Python**
+ Discuss schedules
[Just doing it live](https://www.youtube.com/watch?v=Qy-Y3HJNU_s)
Best way to learn is just by doing, and following by example.
## **Find Forms of Communication**
Those with more experience help teammates do the following:
+ Establish channels for communication: