Intro to pandas using weather datasets (Leesburg and London)
Baye’s Theorem
Goodness of Fit for categorical data
Core understanding: How to assess a categorical model (and the shortcomings of ‘accuracy’)
Week 3
Into to numpy
Linear Regression (computing coefficients from scratch)
Goodness of Fit for numerical data
Core understanding: How to assess a numerical model (and the ambiguity of ‘correlation’)
Week 4
Matrix Operations
Coding matrix arithmetic in python and numpy
Core understanding: numpy operations, review of matrix operations
Week 5
Running Time analysis of Matrix multiply
Types of regression (linear, log-linear, log-log)
Core understanding: How to select and analyze a regression model
Week 6
Research topics
Book club
Core understanding: coming up with research ideas, learning about your area
Week 7
PSAT?
Movable / free week
Week 8
Begin sci-kit learn
Mushroom dataset
Overview of several ML algorithms
Core understanding: Various types of ML algorithms, basics of EDA and modeling, goodness of fit, hyper-parameters
Week 9
Deep dive into Linear Regression
Life Expectancy dataset
Correlated variables, multi-linear regression, normalization and regularization
Interpreting linear coefficients
Core understanding: Finding a best linear regression, removing correlated variables, interpreting results, strength of correlations between observations and effects