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Data Science Programming in R and Python Syllabus
End of semester grade request letter
Class slides
D1: Introduction
D2: Technology
P1D1: Workflow and Github
P1D2: Visualization and the Tidyverse
P1D3: R programming wrap up
P1D4: Python for data science
P1D5: No class
P1D6: Open programming time for Python
P2D1: Getting data and gun deaths
P2D2: Functions and automation
P2D3: Open programming time
P2D4: Being and data scientist
P2D5: Writing Python code
P2D6: Open programming time
P3D1: Spatiotemporal Data
P3D2: Visualizing space and time
P3D3: Programming with spatial data
P3D4: Programming with spatial data (part 2)
P3D5: Visualizing Space and Time
P3D6: Ethics in modeling and scraping
P3D7: Open programming time (R)
P3D8: Coding challenge
P3D9: Programming with spatial data (Python)
P3D10: Spatial Calculations and Manipulations with GeoPandas
P3D11: Managing spatial and nested data in Python
P5D1: We don’t have time for the SQL
P4D1: Understanding the Pavlov in Machine Learning
P4D2: Cleaning data for scikit-learn and machine learning
P4D3: From scikit-learn to DALEX/SHAP
P4D3: From the Tidyverse to Tidymodels
P4D4: Model Explainers R
Readings
User Guides
VScode for Data Science
Slack
Github and Git
Packages in R and Python
Altair Visualization
Links to resources
Office Hours
Class slides
P3D8: Coding challenge
P3D8: Coding challenge
https://github.com/KSUDS/challenge_runners