Welcome! Node is a ten-week introductory data science course offered by Forge (formerly HackCville), where you'll pick up core data literacy skills and apply them in domains of interest.

Among other things, we'll learn the foundations of data manipulation, exploratory data analysis, machine learning, and apply them towards our own areas of interests in projects. Though the course assumes some basic knowledge of programming or familiarity with Python, all are welcome.

By the end of this course, our goal is to show what's possible by learning data science – how to ask the right questions, critique models, and cut through the noise to help you find the patterns in your work. Ultimately, we'd like you to leave with enough familiarity of Python to be independent analysts in your own fields, as well as understand how data science is increasingly used in the world around us.

All weeks will comprise of (1) a lesson delivered through workshops, typically as a Jupyter Notebook, (2) supplemental practice to be completed during labs, (3) cumulative projects that apply data skills to real-world problems.

All course material (slides, notebooks, datasets, etc.) will live in the Node GitHub repository. For ease of access, we've setup a mirror at files.node.ishaandey.com that'll allow for downloading individual files.

• Week 1: What is Data Science?
  • Walk through the data science process with a live City of San Francisco dataset
  • Lab: Setup tools for the semester
• Week 2: Panda, Panda, Panda
  • What is a DataFrame? Learn the lingo of Pandas with masks, chaining, and groupbys
  • Lab: Develop business insights with Gap sales data
  • Project 1: Data Ethics
• Week 3: Everything Data Wrangling
  • Handle irregular data types and aggregate data from several sources
  • Lab: Develop business insights with Gap sales data
• Week 4: Show, Don't Tell
  • Let your audience explore with you by building dynamic visualizations of the Seattle rental market
  • Lab: Practice end-to-end EDA with Node class survey data
• Week 5: DevOps 101
  • Learn industry-level practices for collaborating & publishing work: command line, git, and virtual environments
  • Lab: Write whimsical five-word stories with Git
  • Project 2: Work in teams to produce exciting visuals to a non-technical audience
• Week 6: I, Robot
  • A conceptual introduction to AI and ML. Identify applications and build an intuition for the ML pipeline
  • Lab: Start the ML pipeline to predict Node student majors
• Week 7: Breaking the Black Box
  • A visual dive into some classification methods: decision trees and k-nearest neighbors
  • Lab: Understand model behavior with explainable machine learning
• Week 8: More Than Just Accuracy
  • Critically evaluate model performance through an animated approach
  • Project 3 (Option 1): Compete with your peers on Kaggle to build the best model
• Week 9: Beyond ML
  • Explore topics ranging from APIs, NLP, web scraping & more, taught by our PCs
  • Project 3 (Option 2): Learn something on your own, and publish an article on Medium!
• Week 10: Show and Tell
  • Show off your work! Show us anything you've made this semester, and we'll present what we're excited about, too.

Feel free to contact the team at [email protected] with any questions about content or request permission to use material.

Content developed by Ishaan Dey at Forge (formerly HackCville), Summer 2020. All content rights belong to Ishaan Dey unless otherwise stated. See About & Permissions for contact information. Shoutout to Christian Jung, Ben Artuso, and the Forge team for their feedback and support.