Last Updated: Sun Mar 13 11:57:07 AM
Where we’ve been
- DATA 385: Introduction to Data Science
- Hiding from Omicron
Where we’re at
- Still avoiding Omicron
- Ready to take the tools you’ve learned on how to get and wrangle data to the next level and get some practice with applications
- This week is all about orienting yourself to new tools and starting to create a place to build your online professional portfolio.
Where we’re going
- Doing all we can to lower the spike and get back to more casual in person events.
- Starting to explore the world of DS, from data pipelines to machine learning, to ethical algorithms, to what’s new and exciting. Finding your nitch, and learning some useful tools along the way.
Goals for the week
- Review and understand the class policies and logistics
- Get connected to the class learning tools
- Create an professional online presence [CO2]
- Where are you from
- Why are you interested in data science?
- What do you want to do when you graduate?
To post later in Discord - What is one thing you want us to know about you? - Do you have any pets, or favorite items? (pictures!)
Note: I have a TON of things that I’d like to share with you. Sometimes when I get excited multiple items/topics will try to come out of my head all at once and get jumbled. Ask for clarification, ask for me to repeat myself, ask for examples, ask, ask ask. I am a resource for your learning!
❓ What questions do you have?
This course aims to help students navigate the evolving field of Data Science. We do so by not only providing training in advanced programming and technical skills, but also by providing opportunities for collaboration and reflection on how Data Science impacts human society. The overarching themes of the class include the following.
- Team collaboration tools (Discord, Zoom, Github, HackMD)
- Community Coding - hold helper hours X times /week
- DATAFEST!! April 1-3rd.
- What’s your Data Science Profile?
- Identifying and applying appropriate methods to answer a business or research need.
- Introduction to Statistical learning / Machine learning techniques
- Effectively communicating your results is necessary for adoption and further development.
- Dissemination of reproducible, transparent research.
- Present at the College of Natural Science Poster Session
- Writing instructional materials/documentation/tutorials for others to learn things.
- Reflections and discussion on readings, interesting findings
- Peer review
- Ethics of predictive analytics, artificial intelligence
- Protecting privacy in the age of open data
- Playing with the emotions of the electorate / Facebook 2016 / Election 2020?
- Role of Tech in controlling/spreading fake news
- Cos we gotta make munny!
- What are your options?
- Money, Location, Satisfaction –> Can we get all 3?
- There is no way we can cover everything there is to Data Science in 1 (or 2) classes.
- You have to find things that you want to work on.
- Start building your own library of resources
- Increasing your Google-fu
Initial Thoughts: What is Data Science to you?
- Start a learning journal. This can be on paper, or online such as Google Docs. You will send me the URL this week, I will be checking in on it regularly. Make sure I have write access so I can leave comments.
- 3-2-1 Bridge Activity
Open work time
Head down to the deliverables section below and start working on the items under Getting Started
We’ll be using a combination of notes and github classroom starter repos.
- Dr D’s notes from a half day workshop.
Requested order of approach as listed below.
Join the Discord Server. After you agree to the code of conduct you will be able to select
DATA-485 as your role which will grant you access to our class channels. Post an introduction: Name, What is one thing you want us to know about you? Introduce your pets and/or favorite items.
Discord Join Link
- Also read over the syllabus on your own.