Supervised Learning: Classification
Logistic Regression LASSO Random Forest Ensemble Methods Feature Importance Scoring
Supervised Learning: Regression
Linear Regression Penalized Linear Regression Stochastic Gradient Descent Scoring New
Machine Learning Approaches to Multivariate Statistics
Machine Learning Theory Data pre-processing Missing Data Dummy Coding Standardization
Frequentist Approaches to Multivariate Statistics
Linear Regression Multivariate linear regression Capturing Non-linear Relationships Comparing Model
Traditional Inferential Statistics
Comparing Groups P-Values, summary statistics, sufficient statistics, inferential targets T-Tests
Missing Data
Exploring and understanding patterns in missing data Missing at Random
Python, Hadoop and Spark
Introduction to the difference in Python, Hadoop, and Spark Importing
Exploring Data Graphically
Histogram Box-and-whiskers plot Scatter plots Forest Plots Group-by plotting
Exploratory Data Analysis with Pandas
Univariate Statistical Summaries and Detecting Outliers Multivariate Statistical Summaries and
Data Preparation with Pandas
Filtering Creating and deleting variables Discretization of Continuous Data Scaling
NumPy: Matrix Language
Introduction to the ndarray NumPy operations Broadcasting Missing data in
Bringing Data In
Structured Data Structured Text Files Excel workbooks SQL databases Working
Defining Actionable, Analytic Questions
Defining the quantitative construct to make inference on the question
Base Python Introduction
History and current use Installing the Software Python Distributions String
