Data Science Professional Development

Welcome to the Professional Development.  Our goals for this workshop are to get you familiar with teaching and learning in Jupyter notebooks, to understand how Data Science is bringing some fresh perspectives into higher education, and to engage with you in conversation about how we can build compelling curriculum together. 

Jonathan Ferrari  - External Modules Program Coordinator - Jonathan is a recent grad from Berkeley Data Science (quad majoring with Cognitive Science, Psychology and Computer Science) , he has worked as course staff for many UC Berkeley Data Science classes, and completed an honors thesis working with Apple on using machine learning to translate sign language to English.  

Eric Van Dusen - Tech and Outreach Lead, Lecturer - Eric is currently working as the Cohort Coordinator for the California Learning Lab, teaching Economics and Data Science, and collaborating to make Data Science Education accessible to all.

Resources for Instructors 

• Authoring Resources

• Instructor Guide UC Berkeley 

• Data 8 Github Repo

• Data 8 Textbook   ( built from Jupyter notebook) 

• Data 8 Course Site   ( Links to public options to open notebooks) 

• Data 8 Youtube site ( many different resources/ diferent semesters) 

• Cal-ICOR  ( resource for infrastructure) 

• Data Science Modules El Camino Collaboration - 2024 ( github repo ) 

• workshop.datahub.berkeley.edu( login is email, password = correcthorsebatterystaple)

• jonathanferrari@berkeley.edu

• (Intro notebook) https://colab.research.google.com/github/ucb-ds/el-camino-ds/blob/main/el-camino-notebook_EVD.ipynb

• (Data 4AC Notebook) https://colab.research.google.com/github/ds-modules/data-4ac/blob/master/notebook1/notebook1.ipynb

• (Data 8 Lab 1) https://colab.research.google.com/github/data-8/materials-sp25/blob/main/lab/lab01/lab01.ipynb

• Launch all of Data 8 in JupyterLite - https://www.data8.org/materials-sp22-jupyterlite-no-footprint/lab/index.html

• Blank Notebook https://colab.research.google.com/ (must put this line at the top and run it `!pip install -q datascience`

• Cones Dataset: https://raw.githubusercontent.com/ucb-ds/el-camino-ds/refs/heads/main/cones.csv

• NBA Dataset: https://raw.githubusercontent.com/ucb-ds/el-camino-ds/refs/heads/main/nba_salaries.csv