Quantitative Methods In The Social Sciences · Columbia University
Different visualization techniques for spatial data, GeoDa, GIS in Python
Learned things including supervised learning, basic model selection, Feature Selection, Decision tree, Ensemble Models, working with Text as Data and Neural Networks through python.
- This class builds on the ML class from QMSS
Managing data in R. Data wrangling, loops, functions, apply family. SQL. Working with APIs and create a rest API on AWS.
The course covered a number of ways to perform spatial analysis, including cluster analysis, locally defined regressions, and a number of visualizations. Additionally the course gave a basic overview of python and R.
Many machine learning techniques, how to use Python, use sci-kit library, intro to keras
Fancier models for predictions with tabular, image, audio, video, and time series data.
I gained lots of knowledge and skills in dealing with the coding language R. Very invaluable class as I can take the knowledge and skills I learned and apply them in real life.
Valuable skills on how to use software and how to approach spatial data quesitons
machine learning algorithms and practice